21-06 VOL9 - NV Energy

BEFORE THE PUBLIC UTILITIES COMMISSION OF NEVADA

Application of NEVADA POWER COMPANY d/b/a NV Energy and SIERRA PACIFIC POWER COMPANY d/b/a NV Energy, seeking approval to add 600 MW of renewable energy and 480 MW of energy storage capacity, among other Docket No. 21-06___ items, as part of their joint 2022-2041 integrated resource plan, for the three year Action Plan period 2022-2024, and the Energy Supply Plan period 2022-2024.

VOLUME 9 OF 18

TECHNICAL APPENDIX DEMAND SIDE PLAN

ITEM DESCRIPTION PAGE NUMBER

DSM-18 2020 Field Trials- NPC and SPPC 2

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DSM-18

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Techinical Appendix DSM-18

PowerShift by NV Energy invites you to participate in an energy-saving Field Study – and when you do, you’ll receive a $75 “thank you” gift card

Hello,

At PowerShift by NV Energy, partnering with our customers to help you save energy and money is important to all of us. That’s why we’re inviting you to participate in a confidential and voluntary 12-month energy-efficiency field study we’ll be conducting in the coming months. This study requires no expense on your part, and to say “thank you” you’ll receive a $75 Visa gift card.

Your household has been selected to participate in this field study because you applied to interconnect your solar and battery system with NV Energy in the past. The study will rely on the LG Chem battery system and SolarEdge inverter you have installed at your home.

As the interest and installation of behind-the-meter energy storage systems has grown, this field study will provide valuable information to help us support this technology, while giving you the opportunity to learn more about your home’s energy usage.

The field study will be implemented as follows:

No additional equipment will be installed and no site visits are required. You will be asked to complete and sign a digital Customer Agreement to authorize your

participation and the collection of applicable data. Upon your approval, NV Energy will be granted access by Solar Edge to collect data via a “desk

assessment.” All battery data will be anonymous and no Personal Identifiable Information (PII) will be used or

linked to your data. SolarEdge inverter configurations and telemetry data points will be collected remotely and

securely to identify opportunities and effectiveness of the battery to minimize grid import (i.e., available stored energy available during grid peak periods).

In 2020 and 2021, between June 1 and Sept. 30, demand response events will be tested, allowing a percentage of your energy storage system to offset your household’s loads, reducing your grid import during times of high demand on the electric grid.

o No more than 10 test events will take place during these summer months. These events are also known as our PowerShift community energy events that last about two hours.

The results of our study will be provided to you at the end of the 12-month period. Your participation is strictly voluntary and the data collected will be held in the strictest of

confidence.

NV Energy takes great pride in serving your energy needs. Participation in this 12-month field study will provide valuable insight into your solar and battery system.

To participate, please reply to [email protected] by August 28, 2020. Also, feel free to contact us for more information or with any questions.

We look forward to hearing from you and thank you for your consideration.


AAdvanced Demand Response Strategies for Extreme Climate Conditions

Prepared For: HDR

Prepared By: ESSG – Energy Studies and Services Group

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Executive Summary Weather Trends High Temperature Trends (>=90°F, >=95°F, >=100°F, >=105°F, >=110°F)

Sampled weather data between 2015 – 2018 displays an upward trend in high-temperature run hours at temperature ranges between 90°F and 110°F. From 2015 and 2018, there was a 16% increase in run hours at 90°F from 1707 hrs. to 1987 hrs. From 2015 and 2018, there was a 20% increase in run hours at 95°F from 1072 hrs. to 1290 hrs. From 2015 and 2018, there was a 35% increase in run hours at 100°F from 535 hrs. to 720 hrs. From 2015 and 2018, there was a 73% increase in run hours at 105°F from 180 hrs. to 311 hrs. From 2015 and 2018, there was a 107% increase in run hours at 110°F from 27 hrs. to 56 hrs.

From 2015 and 2017, there was a 233% increase in run hours at 110°F from 27 hrs. to 90 hrs.

The increased occurrences of higher temperature readings as well as the upturn in run hours will continue to grow based on the observed temperature trends between 2015 and 2018.

2019 is an anomaly within the steady 4-year trend of increased hours at high temperatures. The anomaly will need to be revaluated after a few more years (2020, 2021, 2022) of data has been collected in order to confidently set aside this year’s weather trends as an anomaly. The anomaly is most likely due to the irregularly occurring and complex series of climatic changes affecting the equatorial Pacific region and beyond every few years, also known as the El Niño effect.

Building Load & Ambient Temperature Trends

Daily Total & HVAC Peak Demand as a Function of Ambient Temperature

Residential The sampled Residential building (1303289) displayed an increased trend in average HVAC peak demand. In the month of May the mean peak HVAC demand was 1.43 kW, 2.24 kW, 3.61 kW, 3.28 kW, & 2.47 kW from 2015 – 2019 respectively. That results in a 129% increase from 2015 to 2018.

In the month of June, the mean peak HVAC demand was 4.75 kW, 3.82 kW, 6.53 kW, 5.24 kW, & 4.52 kW from 2015 – 2019 respectively. That results in a 10.3% increase from 2015 to 2018.

In the month of July, the mean peak HVAC demand was 4.75 kW, 4.3 kW, 6.67 kW, 5.26 kW, & 5.14 kW, from 2015 – 2019 respectively. That results in a 10.7% increase from 2015 to 2018.

In the month of August, the mean peak HVAC demand was 5.04 kW, 3.83 kW, 6.42 kW, 5.23 kW, & 5.72 kW, from 2015 – 2019 respectively. That results in a 3.8% increase from 2015 to 2018.

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Commercial HM The sampled Commercial HM (1306838) displayed an increased trend in average HVAC peak demand. In the month of May the mean peak HVAC demand was 62.12 kW, 87.66 kW, 88.51 kW, 103.4 kW & 84.49 kW from 2015 – 2019 respectively. That results in a 66.5% increase from 2015 to 2018.

In the month of June, the mean peak HVAC demand was 143.22 kW, 179.47 kW, 177.29 kW, 166.58 kW & 166.46 kW from 2015 – 2019 respectively. That results in a 16.3% increase from 2015 to 2018.

In the month of July, the mean peak HVAC demand was 149.86 kW, 198.38 kW, 185.66 kW, 209.85 kW & 222.1 kW from 2015 – 2019. That results in a 39.4% increase from 2015 to 2018.

In the month of August, the mean peak HVAC demand was 167.28 kW, 168.56 kW, 158.92 kW, 188.52 kW & 215.12 kW from 2015 – 2019. That results in a 12.7% increase from 2015 to 2018.

Commercial RS The sampled Commercial RS building (1109413) displayed an increased trend in average HVAC peak demand. In the month of May the mean peak HVAC demand was 77.75 kW, N/A, 63.53 kW, 46.61 kW, 66.44 kW from 2015 – 2019 respectively. That results in a -14.56% decrease from 2015 to 2019.

In the month of June, the mean peak HVAC demand was 134.13 kW, 123.32 kW, 126.8 kW, 131.48 kW & 95.31 kW from 2015 – 2019 respectively. That results in a -2.3% decrease from 2015 to 2018.

In the month of July, the mean peak HVAC demand was 61.27 kW, 52.09 kW, 81.28 kW, 93.3 kW & 102.63 kW from 2015 – 2019 respectively. That results in a 51.8% increase from 2015 to 2019.

In the month of August, the mean peak HVAC demand was 123.08 kW, 110.87 kW, 151.48 kW, 118.43 kW & 177.98 kW from 2015 – 2019 respectively. That results in a 44% increase from 2015 to 2019.


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Hourly Total Peak & HVAC Peak Demand as a Function of Ambient Temperature The increase in runtime from an upsurge in hours at 90°F and above between 2015 – 2019 has resulted in an increased set of peak demand points as well as prolonged HVAC energy consumption into the late hours of the day.

Residential The sampled Residential building (1303289) experienced ambient temperatures of 90°F or above on the sampled date of July, 29th for 14 hrs., 24 hrs., 20 hrs., 23 hrs., 24 hrs. from 2015 – 2019 respectively resulting in a 71.4% increase in run hours between 2015 to 2019.

Commercial HM The sampled Commercial HM building (1306838) experienced ambient temperatures of 90°F or above on the sampled date of July, 29th for 15 hrs., 24 hrs., 20 hrs., 23 hrs., 24 hrs. from 2015 – 2019 respectively resulting in a 60% increase in run hours between 2015 to 2019.

Commercial RS The sampled Commercial RS building (1109413) experienced ambient temperatures of 90°F or above on the sampled date of July, 29th for 15 hrs., 24 hrs., 20 hrs., 23 hrs., 24 hrs. from 2015 – 2019 respectively resulting in a 60% increase in run hours between 2015 to 2019.

Peak HVAC Demand as a Function of Ambient Temperature

The sampled Residential, Commercial HM and Commercial RS buildings all displayed consistent grouping relationships between the measured, peak building load (including isolated HVAC load) as well as the measured ambient temperature in the area. A majority of the measured peak demand for the sampled 15min interval, hour and/or day corresponds to a measured temperature well within cooling range >80°F with a majority of the measured temperatures occurring at >90°F.

Residential The sampled Residential building (1303289) experiences some of its highest building peak demand points for the month of May at 85.42°F, 86.77°F, 89.78°F, 90.67°F & 82.06°F for 2015 – 2019 respectively.

The sampled Residential building (1303289) experiences some of its highest building peak demand points for the month of June at 103.82°F, 105.45°F, 104.97°F, 103.73°F & 99.54°F for 2015 – 2019 respectively.

The sampled Residential building (1303289) experiences some of its highest building peak demand points for the month of July at 101.25°F, 107.3°F, 107.05°F, 107.48°F & 105.16°F for 2015 – 2019 respectively.


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The sampled Residential building (1303289) experiences some of its highest building peak demand points for the month of July at 104.26°F, 102.47°F, 103.35°F, 105.42°F & 107.0°F for 2015 – 2019 respectively.

Commercial HM The sampled Commercial HM building (1306838) experiences some of its highest building peak demand points for the month of May at 85.42°F, 86.77°F, 89.78°F, 90.67°F & 82.06°F for 2015 – 2019 respectively.

The sampled Commercial HM building (1306838) experiences some of its highest building peak demand points for the month of June at 103.85°F, 105.45°F, 104.97°F, 103.73°F & 99.54°F for 2015 – 2019 respectively.

The sampled Commercial HM building (1306838) experiences some of its highest building peak demand points for the month of July at 101.49°F, 107.3°F, 107.05°F, 107.48°F & 105.16°F for 2015 – 2019 respectively.

The sampled Commercial HM building (1306838) experiences some of its highest building peak demand points for the month of July at 104.28°F, 102.47°F, 103.35°F, 105.42°F & 107.0°F for 2015 – 2019 respectively.

Commercial RS The sampled Commercial RS building (1109413) experiences some of its highest building peak demand points for the month of May at 85.42°F, 86.77°F, 89.78°F, 90.67°F & 82.06°F for 2015 – 2019 respectively.

The sampled Commercial RS building (1109413) experiences some of its highest building peak demand points for the month of June at 103.85°F, 105.45°F, 104.97°F, 103.73°F & 99.54°F for 2015 – 2019 respectively.

The sampled Commercial RS building (1109413) experiences some of its highest building peak demand points for the month of July at 101.49°F, 107.3°F, 107.05°F, 107.48°F & 105.16°F for 2015 – 2019 respectively.

The sampled Commercial RS building (1109413) experiences some of its highest building peak demand points for the month of July at 104.28°F, 102.47°F, 103.35°F, 105.42°F & 107.0°F for 2015 – 2019 respectively.


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BBuilding Energy Intensity Tables

The sampled residential building (1303289) in Las Vegas, NV experienced an energy intensity (kWh/sq-ft) change between 2015 – 2018. The energy intensity change (+/- kWh/sq-ft) resulted in + 59% in May, + 25% in June, + 35% in July, +10% in August and an annual change of +13%.

The sampled Commercial HM building (1306838), a hotel in Laughlin, NV experienced an energy intensity (kWh/sq-ft) change between 2015 – 2018. The energy intensity change (+/- kWh/sq-ft) resulted in + 21.3% in May, + 9% in June, + 23% in July, +13% in August and an annual change of +14%.

The sampled Commercial RS building (1109413), a public school in North Las Vegas, NV experienced an energy intensity (kWh/sq-ft) change between 2015 – 2017. The energy intensity change (+/- kWh/sq-ft) resulted in + 22% in May, + 19% in June, + 52% in July, +11% in August and an annual change of +9%.

Manufacturing and light industrial facilities do not follow the same trends as occupancy dense spaces such as common areas, hotels and shopping centers. This is most likely do to a change in manufacturing/industrial business needs that either resulted in a reduction or increase in building loads that are not necessary in sync with an upward trend in ambient temperature.


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TTable of Contents Executive Summary ................................................................................................................................................ 2

Weather Trends................................................................................................................................................................ 2 High Temperature Trends (>=90°F, >=95°F, >=100°F, >=105°F, >=110°F)............................................................... 2

Building Load & Ambient Temperature Trends............................................................................................................... 2 Daily Total & HVAC Peak Demand as a Function of Ambient Temperature ............................................................ 2

Residential .............................................................................................................................................................. 2 Commercial HM ..................................................................................................................................................... 3 Commercial RS ....................................................................................................................................................... 3

Hourly Total Peak & HVAC Peak Demand as a Function of Ambient Temperature ................................................ 4 Residential .............................................................................................................................................................. 4 Commercial HM ..................................................................................................................................................... 4 Commercial RS ....................................................................................................................................................... 4

Peak HVAC Demand as a Function of Ambient Temperature .................................................................................. 4 Residential .............................................................................................................................................................. 4 Commercial HM ..................................................................................................................................................... 5 Commercial RS ....................................................................................................................................................... 5

Building Energy Intensity Tables...................................................................................................................................... 6

Table of Contents ................................................................................................................................................... 7

Abbreviations, Acronyms, & Terminology .............................................................................................................. 10

Purpose................................................................................................................................................................ 11

Data Analysis Parameters & Approach................................................................................................................... 11

Data Analysis Plots & Tools Descriptions................................................................................................................ 12

High Temperature Trends (>=90°F, >=95°F, >=100°F, >=105°F, >=110°F)............................................................. 12 Building Load & Ambient Temperature Trends....................................................................................................... 12

Daily Total & HVAC Peak Demand as a Function of Ambient Temperature ..................................................... 12 Hourly Total Peak & HVAC Peak Demand as a Function of Ambient Temperature ......................................... 12 Peak HVAC Demand as a Function of Ambient Temperature ........................................................................... 12

Energy Intensity Tables............................................................................................................................................. 12

Weather Trends.................................................................................................................................................... 13

High Temperature Trends (>=90°F, >=95°F, >=100°F, >=105°F, >=110°F) .................................................................. 13

Building Load & Ambient Temperature Trends ...................................................................................................... 16

Residential ...................................................................................................................................................................... 16 Residential (> 2000 sq.-ft. & < 5000 sq.-ft.)............................................................................................................. 16

Daily Total & HVAC Peak Demand as a Function of Ambient Temperature ..................................................... 16 May (2015, 2016, 2017, 2018, 2019)............................................................................................................. 16 June (2015, 2016, 2017, 2018, 2019) ............................................................................................................ 19 August (2015, 2016, 2017, 2018, 2019) ........................................................................................................ 25

Single Day, Hourly Total Peak & HVAC Peak Demand as a Function of Ambient Temperature ...................... 28 Peak HVAC Demand as a Function of Ambient Temperature for 2015, 2016, 2017, 2018, & 2019 ............... 31


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Commercial HM.............................................................................................................................................................. 33 Hotel .......................................................................................................................................................................... 33

Daily Total & HVAC Peak Demand as a Function of Ambient Temperature ..................................................... 33 May (2015, 2016, 2017, 2018, 2019)............................................................................................................. 33 June (2015, 2016, 2017, 2018, 2019) ............................................................................................................ 36 July (2015, 2016, 2017, 2018, 2019).............................................................................................................. 39 August (2015, 2016, 2017, 2018, 2019) ........................................................................................................ 42


Commercial RS................................................................................................................................................................ 50 Public School ............................................................................................................................................................. 50

Daily Total & HVAC Peak Demand as a Function of Ambient Temperature ..................................................... 50 May (2015, 2016, 2017, 2018, 2019)............................................................................................................. 50 June (2015, 2016, 2017, 2018, 2019) ............................................................................................................ 53 July (2015, 2016, 2017, 2018, 2019).............................................................................................................. 56 August (2015, 2016, 2017, 2018, 2019) ........................................................................................................ 59


Building Energy Intensity Tables ............................................................................................................................ 67

Residential ...................................................................................................................................................................... 67

Commercial HM.............................................................................................................................................................. 70

Commercial RS................................................................................................................................................................ 73

Effects of Ambient Temperature on Energy Sector ................................................................................................ 75

Impact of High Ambient Temperatures on Photovoltaic Systems ......................................................................... 75 Impact of High Ambient Temperatures on Photovoltaic Inverter Systems ........................................................... 75 Impact of High Ambient Temperatures on Battery Storage Systems .................................................................... 76

Demand Response Strategies for Extreme Climate Conditions ............................................................................... 77

Thermostat Controlled Demand Response Strategies.................................................................................................. 77 Thermostat Cycling & Neighborhood Load Desynchronization Strategies ............................................................ 77 Building Overcooling Strategies ............................................................................................................................... 78

Thermal Storage Demand Response Strategies............................................................................................................ 80

Non-Vapor Compression AC Systems ............................................................................................................................ 81

Battery Storage Demand Response Strategies ............................................................................................................. 82 Electrical Vehicles...................................................................................................................................................... 82 Electrical Vehicle Bi-Directional Charging................................................................................................................ 82 Second Hand Battery Utilization .............................................................................................................................. 83

Conclusion............................................................................................................................................................ 84

Recommendations................................................................................................................................................ 84

Managing Building and Experiment Variables......................................................................................................... 84 Performing a larger energy intensity table by industry sector ............................................................................... 84 Performing an energy intensity map........................................................................................................................ 84 Performing further evaluation on demand response strategies ............................................................................ 84


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RReferences ........................................................................................................................................................... 85

[11]. Hasnain SM. Review on sustainable thermal energy storage technologies. Part II: cool thermal storage. J Energy Convers Manage 1998; 39: 1139–53....................................... 85 [12]. Sinitsyn, N. A., Kundu, S., & Backhaus, S. (2013). Safe protocols for generating power pulses with heterogeneous populations of thermostatically controlled loads. Energy Conversion and Management, 67, 297-308............................................................................................................................. 86 [13]. Ramchurn, S. D., Vytelingum, P., Rogers, A., and Jennings, N. “Agent-Based Control for Decentralised Demand Side Management in the Smart Grid”. p. 8............................................................. 86


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AAbbreviations, Acronyms, & Terminology

AC Air Conditioning CEMP Community Environmental Monitoring Program DRI Desert Research Institute kW Kilowatt kWh Kilowatt - hour NAN Not A Number STC Standard Test Condition(s) CdTe Cadmium telluride - Photovoltaic technology EV Electrical Vehicle TCL Thermostatically Control Load TES Thermal Energy Storage CWS Chilled Water Storage COP Coefficient Of Performance DR Demand Response DRS Demand Response Strategy


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PPurpose This report examines extreme weather trends and their effects on residential and commercial energy usage as it relates to cooling. The report makes use of both measured climate and utility data in combination with simulated energy models. The combined data used to both depict extreme weather induced demand/energy trends as well as possible demand response strategies to mitigate grid strain as extreme weather trends rise.

Data Analysis Parameters & Approach Weather Data Parameters

o Las Vegas, NV Desert Research Institute (DRI) CEMP Weather Station

Ambient Temperature TMY3 - Typical Meteorological Year 3

Ambient Temperature

NV Energy Data Extract o Building location o Building demand meter measurements o Building calculated meter energy

ATTOM Residential & Commercial Real Estate Data Extract o Building location o Building Area

Conditioned Area Total Area

o Building HVAC Equipment Listings o Building Purpose/Type

Limitations o Missing kW data for several buildings after 2015 o Missing meter data for one or more years on several buildings o Energy intensity calculations are based on estimated building/complex footprint o Building operational patterns are not controlled which can lead to some

abnormalities in kW and/or kWh meter readings from month to month and year to year as well as between two similar buildings in terms of building type and area.

Less building usage in some months and/or years vs other months and/or years 0 demand and/or energy readings on some buildings which could be signs of meter data collection issues


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DData Analysis Plots & Tools Descriptions Data grouping, analysis and calculations were performed on 3 weather data sets spanning ~10 years (2009 – 2019) alongside 25 Residential and 40 Commercial buildings whose electricity meter data spanned ~5 years (2015 – 2019). Weather data samples included both 1min, and 1-hour data sets while meter data was collected at 15 min increments.

High Temperature Trends (>=90°F, >=95°F, >=100°F, >=105°F, >=110°F) Temperature data across 10 years was analyzed to identify high temperature trends from year to year. These trends were identified by sampling measured high temperature points from 90°F up to and beyond 110°F at increments of 5°F. Each year’s number of hours in which these temperatures set points are present are shown as well as any increasing trends of hours spent in these high temperatures from year to year.

Building Load & Ambient Temperature Trends Building load (Total & HVAC) and ambient trends across residential and commercial buildings have been plotted in order to identify the relation between these two readings.

Daily Total & HVAC Peak Demand as a Function of Ambient Temperature Depicts a single building’s (residential or commercial) recorded daily peak demand and peak isolated HVAC demand alongside the recorded peak ambient temperature for each day across the available month and year data sample range (May – Aug & 2015 – 2019).

Hourly Total Peak & HVAC Peak Demand as a Function of Ambient Temperature Depicts a single building’s (residential or commercial) recorded hourly peak demand and peak isolated HVAC demand alongside the recorded ambient temperature for each hour of a specified day of the year across the available data sample range (2015 – 2019).

Peak HVAC Demand as a Function of Ambient Temperature Depicts a single building’s (residential or commercial) recorded peak isolated HVAC demand as a function of the recorded ambient temperature for each of the summer cooling months (May – Aug) across each of the available data set years (2015 – 2019).

Energy Intensity Tables Depicts several single buildings’ (residential or commercial) calculated energy intensity footprint (kWh/sqft) for each of the summer cooling months (May – Aug) across each of the available data set years (2015 – 2019). Each building’s identification number (premise code), city, industry type and estimated area (~sqft) are displayed alongside the energy intensity footprint. A heat map has been implemented in an isolated (per building, per month) vertical pattern. This allows for the identification of energy intensity trends for each summer cooling months (May – Aug) across each of the available data set years (2015 – 2019).


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WWeather Trends High Temperature Trends (>=90°F, >=95°F, >=100°F, >=105°F, >=110°F)


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BBuilding Load & Ambient Temperature Trends Residential Residential (> 2000 sq.-ft. & < 5000 sq.-ft.)

Building ID: 1303289 Location: Henderson, NV Building Type: Residential Building Area: 3,652 sq. -ft

Daily Total & HVAC Peak Demand as a Function of Ambient Temperature

May (2015, 2016, 2017, 2018, 2019)


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June (2015, 2016, 2017, 2018, 2019)


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July (2015, 2016, 2017, 2018, 2019)


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August (2015, 2016, 2017, 2018, 2019)


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SSingle Day, Hourly Total Peak & HVAC Peak Demand as a Function of Ambient Temperature


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PPeak HVAC Demand as a Function of Ambient Temperature for 2015, 2016, 2017, 2018, & 2019


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Commercial HM Hotel

Building ID: 1306838 Location: Laughlin, NV Building Type: Hotel Building Area: 850,418 sq. -ft

Daily Total & HVAC Peak Demand as a Function of Ambient Temperature May (2015, 2016, 2017, 2018, 2019)


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June (2015, 2016, 2017, 2018, 2019)


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July (2015, 2016, 2017, 2018, 2019)


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August (2015, 2016, 2017, 2018, 2019)


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Commercial RS

Public School Building ID: 1109413 Location: North Las Vegas, NV Building Type: Public School Building Area: 53,988 sq. -ft

Daily Total & HVAC Peak Demand as a Function of Ambient Temperature May (2015, 2016, 2017, 2018, 2019)


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June (2015, 2016, 2017, 2018, 2019)


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July (2015, 2016, 2017, 2018, 2019)


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August (2015, 2016, 2017, 2018, 2019)


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BBuilding Energy Intensity Tables Residential *Area figures where acquired from a real estate data bank and should be considered as near estimates in identifying building size as well as building energy intensity (kWh/ft2)

Building City Type Area(ft2) Year May (kWh/ft2)

Jun (kWh/ft2)

Jul (kWh/ft2)

Aug (kWh/ft2)

Annual (kWh/ft2)

1426728 1426728 1426728 1426728 1426728

Henderson Henderson Henderson Henderson Henderson

Residential Residential Residential Residential Residential

2,053 2,053 2,053 2,053 2,053

2015 2016 2017 2018 2019

2.54 1.84 3.47 2.66 1.74

4.36 3.30 4.84 3.54 2.66

4.36 3.61 5.54 4.47 3.21

5.47 3.59 4.98 3.88 3.08

33.53 27.07 36.92 27.28 18.47

1460799 Henderson Residential 2,442 2015 0.86 1.64 2.13 2.00 17.90 1460799 Henderson Residential 2,442 2016 1.10 2.61 3.29 2.64 15.36 1460799 Henderson Residential 2,442 2017 1.12 2.79 3.33 2.78 15.57 1460799 Henderson Residential 2,442 2018 1.26 2.68 3.45 3.40 17.04 1460799 Henderson Residential 2,442 2019 1.22 2.59 3.67 3.45 18.12

1605445 Las Vegas Residential 2,819 2015 1.39 4.36 1.59 3.70 32.50 1605445 Las Vegas Residential 2,819 2016 3.39 4.13 3.95 3.56 24.44 1605445 Las Vegas Residential 2,819 2017 1.69 3.00 3.84 3.13 20.25 1605445 Las Vegas Residential 2,819 2018 0.85 3.98 14.41 10.66 53.52 1605445 Las Vegas Residential 2,819 2019 14.73 13.67 13.63 15.86 156.61


1303289 1303289 1303289 1303289 1303289



3,652 3,652 3,652 3,652 3,652

2015 2016 2017 2018 2019

1.51 1.68 1.77 2.41 1.88

2.89 2.88 3.61 3.61 2.78

2.93 3.62 4.12 3.95 3.70

3.26 3.07 3.57 3.59 3.68

22.01 23.59 25.56 25.15 24.74



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Jun (kWh/ft2)

Jul (kWh/ft2)

Aug (kWh/ft2)

Annual (kWh/ft2)


1265908 1265908 1265908 1265908 1265908

Las Vegas Las Vegas Las Vegas Las Vegas Las Vegas


5,954 5,954 5,954 5,954 5,954

2015 2016 2017 2018 2019

2.55 2.59 3.12 3.86 4.59

3.61 4.05 5.28 5.66 5.53

3.98 4.53 5.69 6.67 6.32

4.13 3.85 5.23 6.50 6.25

32.76 33.51 41.22 48.35 51.21




1329038 1329038 1329038 1329038 1329038



6,687 6,687 6,687 6,687 6,687

2015 2016 2017 2018 2019

3.65 0.51 0.80 3.08 3.63

5.43 0.48 3.65 4.21 5.64

5.82 1.22 5.11 5.03 7.65

6.14 3.86 4.04 2.98 6.64

46.23 19.09 25.04 31.96 55.37

33.26 1776363 Henderson Residential 6,701 2015 2.66 4.44 4.46 4.55 1776363 Henderson Residential 6,701 2016 2.9 4.46 4.44 4.27 31.27 1776363 Henderson Residential 6,701 2017 2.98 4.35 5 4.43 32.86 1776363 Henderson Residential 6,701 2018 3.44 4.23 4.68 4.73 31.8 1776363 Henderson Residential 6,701 2019 2.05 4.37 5.96 5.79 33.68


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Jun (kWh/ft2)

Jul (kWh/ft2)

Aug (kWh/ft2)

Annual (kWh/ft2)


1355742 1355742 1355742 1355742 1355742



7,582 7,582 7,582 7,582 7,582

2015 2016 2017 2018 2019

1.45 1.66 3.23 2.88 2.94

2.48 4.73 5.52 4.47 4.52

2.35 4.55 5.03 5.68 5.59

3.03 4.03 4.44 5.51 5.32

26.5 32.16 39.29 45.07 46.61




1722591 1722591 1722591 1722591 1722591



11,350 11,350 11,350 11,350 11,350

2015 2016 2017 2018 2019

2.23 1.31 1.63 2.45 2.41

3.88 3.08 2.73 3.09 2.36

2.56 2.79 3.51 4.09 2.89

2.5 3.3

3.09 3.65 4.34

24.56 19.9

19.89 25.22 24.75

11.98 2253113 Las Vegas Residential 11,373 2015 0.69 1.64 1.83 1.71 2253113 Las Vegas Residential 11,373 2016 0.82 2.07 2.61 2.17 13.83 2253113 Las Vegas Residential 11,373 2017 1.04 2.29 2.85 2.42 14.76 2253113 Las Vegas Residential 11,373 2018 1.23 2.27 3.21 2.94 17.14 2253113 Las Vegas Residential 11,373 2019 0.92 1.94 1.33 2.01 13.06


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CCommercial HM *Area figures where acquired from a real estate data bank and should be considered as near estimates in identifying building size as well as building energy intensity (kWh/ft2)

Building City Type Area (ft2) Year May

(kWh/ft2) Jun

(kWh/ft2) Jul

(kWh/ft2) Aug

(kWh/ft2) Annual

(kWh/ft2) 1311539 Laughlin Hotel 42,706 2015 46.25 49.40 47.04 48.66 561.61

1311539

1311539

1311539

Laughlin

Laughlin

Laughlin

Hotel

Hotel

Hotel

42,706

42,706

42,706

2016

2017

2018

54.62

50.55

53.28

69.17

62.66

60.34

78.75

74.18

71.90

79.37

73.11

66.48

642.04

594.56

544.33

1311539 Laughlin Hotel 42,706 2019 47.75 58.92 67.10 66.86 574.62

1311540 Laughlin Hotel 42,706 2015 49.39 49.36 50.98 51.50 580.30

1311540 Laughlin Hotel 42,706 2016 50.14 49.76 55.68 56.61 598.09

1311540 Laughlin Hotel 42,706 2017 53.33 52.76 56.52 54.29 628.23

1311540 Laughlin Hotel 42,706 2018 53.81 53.82 57.17 56.17 624.74

1311540 Laughlin Hotel 42,706 2019 51.90 51.30 51.24 13.80 563.13

1311542 Laughlin Hotel 42,706 2015 59.43 63.22 71.42 76.62 627.09

1311542 Laughlin Hotel 42,706 2016 33.00 35.71 38.46 31.62 365.77

1311542 Laughlin Hotel 42,706 2017 25.37 32.24 36.33 33.42 335.11

1311542 Laughlin Hotel 42,706 2018 25.72 34.13 54.25 55.97 435.21

1311542 Laughlin Hotel 42,706 2019 24.36 26.82 48.91 48.56 346.46

2088448 Las Vegas Hotel 108,000 2015 18.56 21.02 20.86 21.21 225.83

2088448 Las Vegas Hotel 108,000 2016 18.76 21.37 24.06 23.27 231.90

2088448

2088448

2088448

1372898

Las Vegas

Las Vegas

Las Vegas

North Las Vegas

Hotel

Hotel

Hotel

Light Industrial

108,000

108,000

108,000

156,800

2017

2018

2019

2015

23.18

22.16

19.98

65.78

25.28

22.84

20.43

68.53

26.82

25.68

22.25

71.25

24.82

25.08

21.93

71.37

268.65

265.53

243.40

672.80

1372898 North Las Vegas Light Industrial 156,800 2016 31.65 34.97 35.09 34.47 353.49





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(kWh/ft2) Jun

(kWh/ft2) Jul

(kWh/ft2) Aug

(kWh/ft2) Annual

(kWh/ft2) 1525385 Henderson Light Industrial 212,437 2015 28.56 35.97 36.46 36.87 365.77

1525385 Henderson Light Industrial 212,437 2016 28.73 34.09 36.28 31.20 361.88




1515481

1515481

1515481

Las Vegas

Las Vegas

Las Vegas

Manufacturing

Manufacturing

Manufacturing

92,400

92,400

92,400

2015

2016

2017

88.65

68.89

34.71

89.14

59.64

41.98

93.38

39.33

46.74

94.90

41.07

46.81

940.12

431.21

427.06

1515481 Las Vegas Manufacturing 92,400 2018 41.72 43.78 47.43 47.03 483.91

1515481 Las Vegas Manufacturing 92,400 2019 43.16 44.85 46.16 45.09 478.50

1538994 Las Vegas Manufacturing 24,645 2015 145.12 155.45 155.95 158.24 1,666.42





2106105 North Las Vegas Manufacturing 58,322 2015 39.82 39.69 41.54 41.28 427.60





1306836

1306836

1306836

1306836

1306836

Laughlin

Laughlin

Laughlin

Laughlin

Laughlin

Hotel

Hotel

Hotel

Hotel

Hotel

850,418

850,418

850,418

850,418

850,418

2015

2016

2017

2018

2019

0.75

0.80

0.83

1.11

0.92

1.30

1.35

1.32

1.57

1.53

1.48

1.60

1.75

2.00

1.95

1.49

1.43

1.61

1.82

1.87

9.89

9.55

10.08

12.19

11.73

1306838 Laughlin Hotel 850,418 2015 1.17 1.58 1.72 1.82 14.06

1306838 Laughlin Hotel 850,418 2016 1.28 1.75 1.96 1.78 13.97

1306838 Laughlin Hotel 850,418 2017 1.33 1.81 2.02 1.83 14.36

1306838 Laughlin Hotel 850,418 2018 1.42 1.73 2.13 2.01 16.10

1306838 Laughlin Hotel 850,418 2019 1.31 1.73 2.16 2.06 16.08


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(kWh/ft2) Jun

(kWh/ft2) Jul

(kWh/ft2) Aug

(kWh/ft2) Annual

(kWh/ft2) 1309940 Laughlin Motel 850,418 2015 1.49 2.29 2.50 2.53 17.71

1309940 Laughlin Motel 850,418 2016 1.61 2.35 2.62 2.36 17.37

1309940 Laughlin Motel 850,418 2017 1.68 2.38 2.79 2.39 18.30

1309940 Laughlin Motel 850,418 2018 1.76 2.27 2.89 2.79 19.01

1309940 Laughlin Motel 850,418 2019 1.55 2.23 2.86 2.66 16.94

1432381

1432381

1432381

Laughlin

Laughlin

Laughlin

Motel

Motel

Motel

1,061,263

1,061,263

1,061,263

2015

2016

2017

2.91

3.26

3.45

4.17

4.69

4.81

4.43

5.40

5.46

4.27

4.85

4.76

35.31

38.70

39.17

1432381 Laughlin Motel 1,061,263 2018 3.52 4.54 5.72 5.35 39.27

1432381 Laughlin Motel 1,061,263 2019 2.96 4.49 5.62 5.32 38.97

2156090 Henderson Warehouse 61,680 2015 9.00 9.35 9.35 9.91 105.02






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CCommercial RS *Area figures where acquired from a real estate data bank and should be considered as near estimates in identifying building size as well as building energy intensity (kWh/ft2)

Building City Types Area (ft2) Year May

(kWh/ft2) Jun

(kWh/ft2) Jul

(kWh/ft2) Aug

(kWh/ft2) Annual

(kWh/ft2) 1919186 Las Vegas Common Area 140,491 2015 6.25 7.77 8.09 8.29 75.72

1919186 Las Vegas Common Area 140,491 2016 6.41 6.63 0.65 9.97 88.97

1919186

1919186

1919186

Las Vegas

Las Vegas

Las Vegas

Common Area

Common Area

Common Area

140,491

140,491

140,491

2017

2018

2019

13.15

13.16

11.59

15.46

15.68

13.47

17.20

17.52

16.34

15.72

16.43

15.74

147.22

150.33

141.64

1109413 North Las Vegas Public School 53,988 2015 17.65 22.64 16.61 24.83 203.57





1786153 Las Vegas Shopping Center 134,104 2015 13.77 16.33 16.70 16.95 162.31





1827519 Henderson Shopping Center 138,655 2015 13.73 16.25 16.67 16.99 158.90

1827519

1827519

1827519

1827519

Henderson

Henderson

Henderson

Henderson

Shopping Center

Shopping Center

Shopping Center

Shopping Center

138,655

138,655

138,655

138,655

2016

2017

2018

2019

14.44

13.86

14.20

11.59

17.19

15.78

15.44

13.15

17.88

17.01

17.31

14.86

16.74

16.06

16.95

15.04

130.37

152.98

154.60

125.69







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Building City Types Area (ft2) Year May

(kWh/ft2) Jun

(kWh/ft2) Jul

(kWh/ft2) Aug

(kWh/ft2) Annual

(kWh/ft2) 1973817 Las Vegas Store Building 131,355 2015 14.64 17.25 17.49 18.05 172.20

1973817 Las Vegas Store Building 131,355 2016 14.14 17.10 17.41 16.84 162.38




2202879

2202879

North Las Vegas

North Las Vegas

Store Building

Store Building

130,335

130,335

2015

2016

4.95

9.96

5.96

11.99

6.32

12.86

6.53

12.02

59.84

101.70

2202879 North Las Vegas Store Building 130,335 2017 10.58 11.72 13.09 12.39 114.18




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EEffects of Ambient Temperature on Energy Sector

Impact of High Ambient Temperatures on Photovoltaic Systems

Photovoltaic (PV) modules are factory rated at standard test condition (STC). These STCs usually consist of parameters such as, an irradiance of 1000 W/m2, temperature at 25°C and solar spectrum of Air Mass 1.5G. The actual output from the PV module in the field varies from its rated output due to changes in ambient environmental conditions from the STC. The reduction in output due to temperature is determined by a temperature coefficient which can vary based upon different types of solar module technologies. In a study performed for Int. Journal of Engineering Research and Applications, the results showed that the average temperature coefficient of power for mono-crystalline, multi-crystalline and CdTe based photovoltaic modules are -0.446 %/°C, -0.387 %/°C and -0.172 %/°C respectively. In case of amorphous silicon module, only one sample was measured, and the temperature coefficient was -0.234 %/°C. Dash, P. K., & Gupta, N. C. [1]. Based upon these findings the average temperature coefficient of power for mono-crystalline, multi-crystalline and CdTe based photovoltaic modules would be -0.446 %/ 1.8°F, -0.387 %/1.8°F and -0.172 %/ 1.8°F respectively for every 1.8°F increase above 77°F. Based upon results from the high temperature weather data analysis the following table depicts the minimum degradation in performance based upon the specified high ambient temperature.

Change in Performance % Outdoor

Temperature (°F) Mono-Crystalline (-0.446%/1.8°F)

Multi-Crystalline (-0.387%/1.8°F)

CdTe (-0.172%/1.8°F)

90 -3.22 -2.80 -1.24 95 -4.46 -3.87 -1.72

100 -5.70 -4.95 -2.20 105 -6.94 -6.02 -2.68 110 -8.18 -7.10 -3.15 115 -9.42 -8.17 -3.63 120 -10.65 -9.25 -4.11 125 -11.89 -10.32 -4.59 130 -13.13 -11.40 -5.06

Impact of High Ambient Temperatures on Photovoltaic Inverter Systems

Photovoltaic Inverts on the other hand deal with larger issues than degradation of performance at high ambient temperatures. Photovoltaic inverts that are exposed to extreme (high) ambient weather conditions have increased odds of operational failure over time. According to a study by the National Renewable Energy Laboratories (NREL), high ambient temperatures combined with inverter heat sink design, specifications, and other weather conditions such as wind can be used to identify possible points of failure with specific field deployed inverters Sorensen [2].


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IImpact of High Ambient Temperatures on Battery Storage Systems

Similar to energy production, energy storage is also subject to degradation in overall performance as a result of increased ambient temperature conditions. In a study performed for the Journal of Power Sources, laboratory-size lithium-ion pouch cells were cycled over 100% depth of discharge (DOD) at room temperature 25°C (77°F) and 60°C (140°F) in order to investigate high-temperature degradation mechanisms of capacity fading for individual battery cell components. The high-temperature cell lost 65% of its initial capacity after 140 cycles at 60°C (140°F) compared to only a 4% loss for the cell cycled at room temperature Shim, J[3].


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DDemand Response Strategies for Extreme Climate Conditions

Thermostat Controlled Demand Response Strategies

Thermostat Cycling & Neighborhood Load Desynchronization Strategies

Thermostatically controlled loads (TCLs), devices such as water heaters and air conditioners, can be controlled to provide ancillary services by assisting in balancing generation and load. By adding simple imbedded instructions and a small amount of memory to temperature controllers of TCLs, it is possible to design open-loop control algorithms capable of creating short-term pulses for demand response strategies. Meanwhile, avoiding temporary synchronization of the TCLs following the completion of a demand response activity [12]. Such demand response activities could include but are not limited to, delayed thermostat call for cooling, over cooling activities as well as adjusted cooling set point parameters. The end goal of these cooling demand response conditions is to reduce the on cycle of a building’s AC compressor. However, AC compressors mitigating response strategies that are implemented at the same time can result in a synchronized on cycle between multiple compressors within a single neighborhood. This can result in oscillations in demand that arise before and/or after a peak demand schedule depending on weather a pre or post peak demand activity occurred. A pre activity being over cooling while a post activity being some form of a thermostat set back which occurs during peak load hours.

Peak demand load management within a neighborhood and/or substation level can be properly managed in order to avoid pre/post demand response activity synchronization of loads. These oscillations in demand can lead to further peak demand occurrences as building compressors’ syncronize across neighborhoods and substations. The decoupling and desynchronization of building peak loads can be attained through a decentralized ensemble of communicating smart TCLs [13]. The TCLs can work together in order to identify the necessary delays that will result in the most ideal peak demand shift/shave while avoiding load synchronization as a byproduct.


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BBuilding Overcooling Strategies

Thermal storage in the form of a tight and/or adequately sealed building can offer the ability to pre-condition a space in anticipation for an expected cooling/heating condition. A pre-conditioned (over cooled/heated) space can offer peak shifting and/or peak shaving benefits by using excess power from distributed generation and/or available utility power in order to better manage an incoming cooling/heating cycle.

An example can be seen below. A plot depicts the possible off solar demand shift that can be achieved with over cooling a residential building during the hours of 1PM – 5PM utilizing excess PV generation in order to reduce the cooling demand during typical evening home occupation hours. Note, utility power can also be utilized to in place of an onsite disturbed generation source. Based on the plots below, the over cooled home on excess PV generation results in a cooling demand that is less than a base case where over cooling is not applied, and solar generation has seized for the day.

The current scenario utilizes a PV system that is sized to adequately perform the demand shift during one of the harshest cooling months (July). During less intense cooling months, over cooling can be combined with other self-consumption practices to maximize the potential excess PV that is generated.

The building profile is: 2,160 sq-ft property with a SEER 16 Central AC & a 4kW PV system.

Off Solar Demand Shift With Over Cooling Using Excess PV

0

20

40

60

80

100

120

0

0.5

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1.5

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2.5

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3.5

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pera

ture

°F

Pow

er (k

W)

Generation Profile Day in July

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Time of Day

Building Load At 78°F Building Load At 78°F With 72° Over Cooling PV Generation Indoor Air Temperature at 78°F Indoor Air Temperatue At 78°F With 72° Over Cooling



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17 10 11 12 13 14 15 -16 18 19 20 21 22 -23 24 25 26 -27 28 29 30 31 1 3 4 5 7 8 9 -2 -6 - - - - - - - - - - - - - - - - - - - - - - - - - -Jul l l l ul ul ul l l l l ul ul ul l l l l ul ul ul ul l l l Jul Jul Jul l l lJu Ju Ju Ju Ju Ju Ju u u Ju u Ju Ju u u u JuJ J J J J J J J J J J J J J J J

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Jul-21 Jul-22

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Jul-25 Jul-26

Jul-27 Jul-28

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Jul-31

Building Over Cooling Using Excess PV Generation: Daily Energy Consumption July

60 115

110

105 55 100

95 50

90

85

45 80

75

70

65

60

40

35 55

50

30 45

40

25 35

30

25 20

20

15 15

10

5

10 0

kWh

Day Of Month

Building at 78°F Cooling Set Point: Building Load (kWh) Building at 78°F Cooling Set Point w/ 72°F Over Cooling: Building Load (kWh) PV Generation ( kWh)

Building at 78°F Cooling Set Point: Indoor Air Temperature (°F) Building at 78°F Cooling Set Point w/ 72°F Over Cooling: Indoor Temperature (F) Outdoor Drybulb (F)


Tem

pera

ture

°F

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TThermal Storage Demand Response Strategies

The principal idea in using thermal energy storage (TES) is shifting the electricity peak load associated with buildings' cooling from peak time to off-peak periods. In general, TES is considered by utilities as demand management strategy, which is suitable for specific applications. The concept of thermal energy storage has been employed long ago for solar energy applications; recently with the vast increase of A/C energy demand, cold TES technology appeared to provide a feasible solution for solving peak load problems [7-9]. In hot climate areas where reliance on air conditioning increases, the maximum cooling loads of buildings occur during midday period. At the same time the performance of generating units, especially gas turbine plants, drops because of the high inlet air temperature to the compressor [10]. Though use of cold storage seems to be a promising technology, its implementation depends on the variation of the daily cooling load. The latter depends on the features of the building and occupants activities. Thermal storage systems for cooling application are generally categorized in three types, which are chilled water, ice storage and eutectic salt TES systems [11]. Between these techniques, the Chilled Water Storage (CWS) and the Ice Thermal Storage (ITS) systems are the most promising ones in case of the normal applications. Table 1 shows some of the main differences between these three cool storage systems.

Chilled water Ice storage Eutectic salt

Specific heat kJ/kg K 4.19 2.04 Latent heat of fusion kJ/kg 334 80-250 Chiller cost $/kW 57-85 57-142 57-85

Tank Volume cu.

m/kWh 0.089-0.17 0.019-0.02 0.048 Storage installed cost $/kWh 8.5-28 14-20 28-43 Charging temperature deg. F 39-43 21-27 39-43 Charging efficiency COP 4.0-6.0 2.7-4.0 5.0-6.0 Discharge temperature deg. F 34-39 34-37 48-50

Based upon the desired BTU shift that would need to be conducted on a specified residential or commercial building, an appropriately sized thermal storage system could assist in shaving, and or completely shifting otherwise expensive peak leads to off peak hours in order to reduce the overall stress on the grid during peak cooling hours of an extreme climate condition day.


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NNon-Vapor Compression AC Systems

Advanced evaporative cooling technologies have been commercialized for many years, and offer substantial potential for energy savings in hot-dry climate zones. Nevada’s generally hot-dry climate with lower humidity points make it an ideal location for non-vapor compression system implementation in both residential and commercial markets. According to a study performed by the US Department of Energy focusing on Savings Potential and RD&D Opportunities for NonVapor-Compression HVAC Technologies [6], evaporative cooling was shown to have a 75% estimated energy savings over vapor-compression systems. Non Vapor Compression HVAC technologies were also categorized as low cost/complexity systems due to having fewer components than equivalently-sized vapor-compression systems and therefore potentially being smaller in size. Furthermore, the study suggests that indirect and IDEC systems can typically achieve 50% and 80% savings, respectively, over equivalent vapor-compression systems. These savings can be attributed to overall energy reduction as well as peak cooling load mitigation.


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BBattery Storage Demand Response Strategies

Electrical Vehicles There has been a significant growth in the electric vehicle (EV) market over the past 8 years. Since 2012, the stock of EV’s (i.e. the number of EVs on the road) has reached over 1 million, with the next 1 million EVs expected to be sold in the next 3 years. Furthermore, the annual sales of EVs is expected to reach over 3.5 million vehicles in 2030. Lastly, it is estimated that 18.7 million EVs will be on the road by 2030, making up 7% of the expected 259 million vehicles (cars and light trucks) that will be on U.S. roads that year [4].

Electrical Vehicle Bi-Directional Charging Electrical vehicle (EV) battery capacities can range between 17.6 kWh in compact, low range (58 mile) vehicles to 100 kWh sports cars and SUVS with increased range (up to 300 mile) and performance capabilities.

The Average Nevada driver will drive ~ 12,869 miles/year. That equates to ~ 35 miles/day. Assuming drivers maintain a 20% +/- deviation from the average, an average EV owner could potentially have ~5kWh – 80kWh of capacity that can be utilized to respond to extreme weather condition induced peak demands in the afternoon using a bi-directional inverter. When residents return home and cooling loads are likely to increase or continue to be present, photovoltaic potential continues to dwindle into the evening.

Alternatively, commercial buildings that maintain a fleet of electrical vehicles (such as government agencies) or house customer vehicles for a prolonged period of time can utilize the parked electrical vehicles to assist in the mitigation of peak loads at desired intervals and then continuing to charge their vehicles once the peak shifting routine is complete.

Vehicle-to-grid (V2G) technology via a bi directional inverter can help bridge the gap between time of power supply and time of demand while helping regulate energy on the grid.


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SSecond Hand Battery Utilization Given the significant growth of electrical vehicles in United Sates as well the globe, a similar trend is to be expected of second hand electrical vehicles and their parts. The average electrical vehicle’s life span is estimated to be between 15-17 years at ~12,000 miles/year.

Due to the rapid rise of EVs in recent years and even faster expected growth over the next ten years in some scenarios, the second-life-battery supply for stationary applications could exceed 200 gigawatt-hours per year by 2030. This volume will exceed the demand for lithium-ion utility-scale storage for low- and high-cycle applications combined, which by 2030 will constitute a market with global value north of $30 billion [5].

A rise in potential residential battery storage capabilities via second hand electrical vehicle batteries could significantly reduce the cost of implementing battery storage based demand response strategies during extreme climate conditions and/or year round.

[5].


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CConclusion The NV Energy market has experienced an upsurge in high ambient temperature run hours between 2009 - 2019. There has been a 16%, 20%, 35%, 73%, and 107% increase in ambient temperature run hours of 90°F, 95°F, 100°F, 105°F & 110°F respectively across 2015 - 2018 alone. The upsurge in prolonged, high ambient temperature run hours has resulted in an increased HVAC peak demand across residential and commercial sectors. Baring external business and occupancy variables, both residential and commercial sectors have experienced an increase in their peak cooling demand as well their overall cooling energy utilization. The increase in residential and commercial sector demand & energy footprints can range between 4% – 60% on average. However, there are buildings who have fallen outside of this range due to the possible introduction of uncontrolled variables such as the addition and/or subtraction of non-HVAC machinery, human occupancy or HVAC systems. Lastly, there are several demand response strategies’ that can be implemented in order to reduce, shave or shift excess peak demand loads during extreme climate conditions. These include but are not limited to; thermostat set back and thermostat synchronization across substations, building over cooling strategies, using thermal storage systems, using electrical storage systems (electrical vehicles and battery storage systems from second hand EVs), and alternative non-vapor compressed air conditioning systems.

Recommendations Managing Building and Experiment Variables A smaller sample of residential and commercial buildings should be thoroughly studied. Sample buildings should be identified based on industry type, footprint (overall conditioned space), Cooling Unit Size and specifications as well as known building operational consistency. Ideally buildings that have been consistently enrolled in a specific energy monitoring program for >5 years with very few building and occupancy changes to limit as many outside variables as possible while evaluating the long-term effects of high temperature climate trends on the building’s HVAC load trends.

Performing a larger energy intensity table by industry sector A larger set of buildings with identified industry types, footprints and load profiles can be evaluated to provide a more detailed industry level energy intensity breakdown.

Performing an energy intensity map A larger set of buildings with identified industry types, footprints, load profiles and location can be evaluated to provide a more detailed zip code and/or city level energy intensity breakdown.

Performing further evaluation on demand response strategies Further research should be conducted in relation to the recommended demand response strategies (DRS) as well as any other applicable DRS that may have a significant impact on mitigating extreme climate induced peak demand trends.


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RReferences

[1]. Dash, P. K., & Gupta, N. C. (2015). Effect of temperature on power output from different commercially available photovoltaic modules. International Journal of Engineering Research and Applications, 5(1), 148-151.

[2]. Sorensen, N. R., Thomas, E. V., Quintana, M. A., Barkaszi, S., Rosenthal, A., Zhang, Z., & Kurtz, S. (2013). Thermal study of inverter components. IEEE journal of photovoltaics, 3(2), 807-813.

[3]. Shim, J., Kostecki, R., Richardson, T., Song, X., & Striebel, K. A. (2002). Electrochemical analysis for cycle performance and capacity fading of a lithium-ion battery cycled at elevated temperature. Journal of power sources, 112(1), 222-230.

[4]. The 2018 forecast is an update to: Plug-in Electric Vehicles Sales Forecast Through 2025 and the Charging Infrastructure Required. Edison Electric Institute and Institute for Electric Innovation. July 2017. https://www.edisonfoundation.net/iei/publications/Documents/IEI_EEI%20EV%20Forecast% 20Report_Nov2018.pdf

[5]. Hauke Engel, Patrick Hertzke, Giulia Siccardo. Second-life EV batteries: The newest value pool in energy storage https://www.mckinsey.com/industries/automotive-and-assembly/our-insights/second-life-ev-batteries-the-newest-value-pool-in-energy-storage

[6]. William Goetzler, Robert Zogg, Jim Young, Caitlin Johnson (2014). Energy Savings Potential and RD&D Opportunities for NonVapor-Compression HVAC Technologies

[7]. Lehman, T., Jones, D. D. and Vogel D. R., Off-peak HVAC is once again hot, J.Consulting Specifying Eng., pp 46-49, Nov. 2001.

[8]. ASHRAE, Thermal Energy Storage, ASHRAE Handbook of applications, American society of heating ventilation and air conditioning, Atlanta Georgia, (1995).

[9]. Zhou, G., Krarti, M. and Henze, G. P., Parametric analysis of active and passive building thermal storage utilization, ASME J. Solar Engineering, 127, pp 37-64, (2005).

[10]. Al Hazmy, M. and Najjar, Y. S., Augmentation of gas turbine performance using air coolers, Applied thermal engineering, 24, pp 415-429, (2004).

[11]. Hasnain SM. Review on sustainable thermal energy storage technologies. Part II: cool thermal storage. J Energy Convers Manage 1998; 39: 1139–53.


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[12]. Sinitsyn, N. A., Kundu, S., & Backhaus, S. (2013). Safe protocols for generating power pulses with heterogeneous populations of thermostatically controlled loads. Energy Conversion and Management, 67, 297-308.

[13]. Ramchurn, S. D., Vytelingum, P., Rogers, A., and Jennings, N. “Agent-Based Control for Decentralised Demand Side Management in the Smart Grid”. p. 8.


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0

EV Charging Station Demand Limiting Events BY CALDER CHISM IN RENEWABLE ENERGY · WHAT'S NEW — 31 AUG, 2020 0

Do you leave your electric vehicle charging while it’s parked when you come into the o ce? If your answer is yes, your EV may be engaged in an innovative demand limiting demonstration.

With EVs becoming more and more widespread, the increased demand from charging will be important to control to provide exibility to our electric grid. We will be testing the demand limiting capabilities of our EV charging stations by setting 10-25 percent reductions on the amount of power delivered to your car. While this adds an additional charging time of about 12-20 minutes per hour, you may not notice it at all when you’re in the o ce while your electric vehicle continues to charge.

These demand limiting events, no more than 10, will occur between Tuesday, September 1 and Friday, September 30 at the Beltway employee entrance and Pearson solar carport parking locations. If you are charging your electric vehicle between these dates, please contact [email protected] to maintain communication on the event details. The schedule and further testing details will also be posted on the actual EV charging stations.

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SUBMIT A STORY TO MYNVE | © 2020 NV ENERGY. ALL RIGHTS RESERVED.SUBMIT A STORY TO MYNVE 2020 NV ENERGY. ALL RIGHTS RESERVED.


AAdvanced Demand Response Strategies for A Renewable Future

Prepared For: HDR Consulting

Prepared By: ESSG – Energy Studies and Services Group

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Techinical Appendix DSM-18TTable of Contents Table of Contents ................................................................................................................................................... 2

Abbreviations and Acronyms................................................................................................................................... 3

Purpose.................................................................................................................................................................. 4

Data Analysis Parameters & Approach..................................................................................................................... 4

Data Analysis Flowchart................................................................................................................................................... 5

Data Analysis Plots & Tools Descriptions.................................................................................................................. 6

Nevada Daily & Monthly Load & Solar Power Plots. 2019, ~2025 & ~2030 ........................................................... 6 Nevada Daily & Monthly Load, Solar Power & Shifted Power Plots 2030 ............................................................... 6

Nevada Daily Load & Solar Power Plots.................................................................................................................... 7

2019 (Spring, Summer, Fall, Winter).......................................................................................................................... 7 ~2025 (Spring, Summer, Fall, Winter)...................................................................................................................... 10 ~2030 (Spring, Summer, Fall, Winter)...................................................................................................................... 13

Nevada Daily Load, Solar Power, & Shifted Power Plots.......................................................................................... 16

~2030 (Spring, Summer, Fall, Winter)...................................................................................................................... 16

Demand Response Strategies for a Renewable Future ........................................................................................... 19

Load Distribution with Electrical Storage...................................................................................................................... 21 Electrical Vehicles...................................................................................................................................................... 21 Electrical Vehicle Bi-Directional Charging................................................................................................................ 21 Non Commercial Electrical Vehicle Load Shifting Capability .................................................................................. 21 Recycled Electrical Vehicle Batteries........................................................................................................................ 22

Thermal Storage Load Shifting Strategies..................................................................................................................... 23

Impacts of Temperature on Renewable Future ...................................................................................................... 24

Impact of High Ambient Temperatures on Photovoltaic Systems ......................................................................... 24 Impact of High Ambient Temperatures on Photovoltaic Inverter Systems ........................................................... 25 Impact of High Ambient Temperatures on Battery Storage Systems .................................................................... 25

References ........................................................................................................................................................... 30

ESSG - Advanced Demand Response Strategies for a Renewable Future 2

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Techinical Appendix DSM-18AAbbreviations and Acronyms

AC Air Conditioning CEMP Community Environmental Monitoring Program DR Demand Response DRI Desert Research Institute EV Electric Vehicle kW Kilowatt kWh Kilowatt - hour NAN Not A Number PV Photovoltaic RE Renewable Energy


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Techinical Appendix DSM-18PPurpose This report examines local, national and global utilities that are currently experiencing an increase in renewable energy technologies within their market. Furthermore, the report examines the feasibility of applying current demand response strategies in order to manage future renewable energy growth within the local utility market. Lastly, the report examines the results of modeling renewable generation and storage trends alongside proposed demand response (DR) strategies to mitigate potential utility grid overload within the local utility market.


o Las Vegas, NV Desert Research Institute (DRI) CEMP Weather Station

Ambient Temperature TMY3 - Typical Meteorological Year 3

Ambient Temperature

NV Energy Data Extract o Annual Load Profile o Annual Generation Profile


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Techinical Appendix DSM-18DData Analysis Flowchart


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Techinical Appendix DSM-18DData Analysis Plots & Tools Descriptions

Nevada Daily & Monthly Load & Solar Power Plots. 2019, ~2025 & ~2030 Depicts the daily and monthly Nevada total load, ambient temperature and isolated renewable (solar) generation profiles for 2019. The data for 2025 & 2030 is then interpolated by applying an annual percentage increase in load based upon the last 10 years (2009 – 2019) of annual load deltas. Subsequently, renewable generation is increased at a specific annual rate which reflects Nevada’s renewable capacity trajectory of 50% generation capacity from renewables by 2030.

Nevada Daily & Monthly Load, Solar Power & Shifted Power Plots 2030 Depicts the interpolated daily and monthly Nevada total load, isolated renewable (solar) generation, and shifted daily excess power profiles for 2030 applying an annual percentage increase in load based upon the last 10 years (2009 – 2019) of annual load deltas. Subsequently, renewable generation is increased at a specific annual rate which reflects Nevada’s renewable capacity trajectory of 50% generation capacity from renewables by 2030. Finally, excess power generation is isolated and shifted to hour where solar generation begins to fade or has already seized.


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Techinical Appendix DSM-18NNevada Daily Load & Solar Power Plots. 2019 (Spring, Summer, Fall, Winter) Nevada 2019 daily load & solar generation plots depict the current load to generation ratios experienced in both southern and northern Nevada. The plots show the current solar generation capacity at about 14-16 % penetration. This leaves the remaining 84-86% generation up to other renewable resources and fossil fuels. Sampled spring months show steady load patterns due to cooler shoulder months with considerable solar generation from an increase in available irradiance during this season. These shoulder months are typically ideal candidates for excess generation shifting as well as duck curve identification. However, the current solar penetration does not require an aggressive load shifting plan. Sampled summer months show a single sign wave load pattern resulting from cooler mornings that transition into much warmer afternoon and evening hours. The resulting increase in temperatures has a negative effect on both overall load as well as solar generation for the state (Ref impact of ambient temperature on renewable future). Cooler Fall & Winter months will result in an overall reduction in state wide load due to reduced need for cooling, while the cooling temperatures will assist in solar generation. However, the reduced irradiance during cooler Fall and Winter months will impact the overall potential solar generation.


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Techinical Appendix DSM-18~~2025 (Spring, Summer, Fall, Winter) Nevada 2025 daily load & solar generation plots depict the current load to generation ratios experienced in both southern and northern Nevada. The plots show the current solar generation capacity at about 33-35 % penetration. This leaves the remaining 77-75% generation up to other renewable resources and fossil fuels. Sampled spring months show steady load patterns due to cooler shoulder months with considerable solar generation from an increase in available irradiance during this season. These shoulder months are typically ideal candidates for excess generation shifting as well as duck curve identification. The current solar penetration may have a few opportunities during these months to all for excess peak shifting from over generation during the peak hours of the day. Sampled summer months show a single sign wave load pattern resulting from cooler mornings that transition into much warmer afternoon and evening hours. The resulting increase in temperatures has a negative effect on both overall load as well as solar generation for the state (Ref impact of ambient temperature on renewable future). Cooler Fall & Winter months will result in an overall reduction in state wide load due to reduced need for cooling, while the cooling temperatures will assist in solar generation. However, the reduced irradiance during cooler Fall and Winter months will impact the overall potential solar generation.


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Techinical Appendix DSM-18~~2030 (Spring, Summer, Fall, Winter) Nevada 2030 daily load & solar generation plots depict the current load to generation ratios experienced in both southern and northern Nevada. The plots show the current solar generation capacity at about 50-52 % penetration. This leaves the remaining 50-48% generation up to other renewable resources and fossil fuels. Sampled spring months show steady load patterns due to cooler shoulder months with considerable solar generation from an increase in available irradiance during this season. These shoulder months are typically ideal candidates for excess generation shifting as well as duck curve identification. With a 50% solar renewable penetration and assuming another 50-70% other renewable/fossil fuel penetration, 2030 would present opportunities during shoulder months in the spring and fall where duck curve like, over generation and underutilization events can occur. Ideal shifting scenarios are presented below (~2030 Shifted Power Scenarios). Sampled summer months show a single sign wave load pattern resulting from cooler mornings that transition into much warmer afternoon and evening hours. The resulting increase in temperatures has a negative effect on both overall load as well as solar generation for the state (Ref impact of ambient temperature on renewable future). Cooler Fall & Winter months will result in an overall reduction in state wide load due to reduced need for cooling, while the cooling temperatures will assist in solar generation. However, the reduced irradiance during cooler Fall and Winter months will impact the overall potential solar generation.


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NNevada Daily Load, Solar Power, & Shifted Power Plots. Techinical Appendix DSM-18

~2030 (Spring, Summer, Fall, Winter)

Nevada 2030 daily load & solar generation plots with shifting depict the current load to generation ratios experienced in both southern and northern Nevada. The plots show the current solar generation capacity at about 50-52 % penetration. This leaves the remaining 50-48% generation up to other renewable resources and fossil fuels. In this scenario a constant fossil fuel generation is present that is equivalent to 50% of the average daily load, with solar generation being added on top. Sampled spring months show steady load patterns due to cooler shoulder months with considerable solar generation from an increase in available irradiance during this season. These shoulder months are typically ideal candidates for excess generation shifting as well as duck curve identification. With a 50% solar renewable penetration and assuming another 50-70% other renewable/fossil fuel penetration, 2030 will present several MWh of shifting capabilities. Sampled plots depict the shifted MWh numerical and graphical figures for each day in a sampled season. Sampled summer months show a single sign wave load pattern resulting from cooler mornings that transition into much warmer afternoon and evening hours. The resulting increase in temperatures has a negative effect on both overall load as well as solar generation for the state (Ref impact of ambient temperature on renewable future). Cooler Fall & Winter months will result in an overall reduction in state wide load due to reduced need for cooling, while the cooling temperatures will assist in solar generation. However, the reduced irradiance during cooler Fall and Winter months will impact the overall potential solar generation.


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Techinical Appendix DSM-18NNevada Duck Curve Simulations ~2030 (Spring, Summer, Fall, Winter)


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Demand Response Strategies for a Renewable Future Techinical Appendix DSM-18

Load Distribution with Electrical Storage Electrical Vehicles There has been a significant growth in the electric vehicle (EV) market over the past 8 years. Since 2012, the stock of EV’s (i.e. the number of EVs on the road) has reached over 1 million, with the next 1 million EVs expected to be sold in the next 3 years. Furthermore, the annual sales of EVs is expected to reach over 3.5 million vehicles in 2030. Lastly, it is estimated that 18.7 million EVs will be on the road by 2030, making up 7% of the expected 259 million vehicles (cars and light trucks) that will be on U.S. roads that year [4].

Electrical Vehicle Bi-Directional Charging Electrical vehicle (EV) battery capacities can range between 17.6 kWh in compact, low range (58 mile) vehicles to 100 kWh sports cars and SUVS with increased range (up to 300 mile) and performance capabilities.

The Average Nevada driver will drive ~ 12,869 miles/year. That equates to ~ 35 miles/day. Assuming drivers maintain a 20% +/- deviation from the average, an average EV owner could potentially have ~5kWh – 80kWh of capacity that can be utilized to respond to off solar demands in the evening and night using a bi-directional inverter. This will assist utilities in transitioning from using solar power in the day to limited fossil fuels at night.

Alternatively, commercial buildings that maintain a fleet of electrical vehicles (such as government agencies) or house customer vehicles for a prolonged period of time can utilize the parked electrical vehicles to assist in the mitigation of peak loads at desired intervals and then continuing to charge their vehicles once the peak shifting routine is complete.

Vehicle-to-grid (V2G) technology via a bi directional inverter can help bridge the gap between time of power supply and time of demand while helping regulate energy on the grid.

Non Commercial Electrical Vehicle Load Shifting Capability

Year Conservative

Estimated Number of EVs On The Street

Avg. Load Shifting Capability (kWh) Per Day After Daily Commute

(~25 kWh/vehicle)

Equivalent number of Tesla Power Wall 2.0 Units

(~13 kWh/unit) 2019 3,549 88,725 kWh 6,825 Units

2025 6,975 174,375 kWh 13,413 Units

2030 9,830 245,750 kWh 18,903 Units

2040 15,540 388,500 kWh 29,884 Units

High Estimate

2040 50,000 1,250,000 kWh 96,153 Units


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Techinical Appendix DSM-18RRecycled Electrical Vehicle Batteries Second Hand Battery Utilization Given the significant growth of electrical vehicles in United Sates as well the globe, a similar trend is to be expected of second hand electrical vehicles and their parts. The average electrical vehicle’s life span is estimated to be between 15-17 years at ~12,000 miles/year.

Due to the rapid rise of EVs in recent years and even faster expected growth over the next ten years in some scenarios, the second-life-battery supply for stationary applications could exceed 200 gigawatt-hours per year by 2030. This volume will exceed the demand for lithium-ion utility-scale storage for low- and high-cycle applications combined, which by 2030 will constitute a market with global value north of $30 billion [5].


[5].


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Techinical Appendix DSM-18T

S ).

Thermal Storage Load Shifting Strategies

Thermal energy storage (TES) can be used to shift electricity peak generation associated with shoulder months. In general, TES is considered by utilities as demand management strategy, which is suitable for specific applications. The concept of thermal energy storage has been employed long ago for solar energy applications; recently with the vast increase of A/C energy demand, cold TES technology appeared to provide a feasible solution for solving peak load problems [5-7].

Thermal storage in buildings can occur within a building’s physical structure—parts made of steel, concrete and masonry—have thermal inertia and allow a building to deploy pre-cooling or pre-heating strategies to shift HVAC energy loads to off-peak periods while maintaining comfortable temperature ranges for occupants. New materials such as phase change materials (PCMs) significantly increase thermal mass and load shifting capacity. Heat can also be stored in water. Ice or chilled water tanks can be actively controlled to shift HVAC loads. Electric water heaters—resistance or heat-pump--can also shift load or modulate electricity use.

Table 1 shows some of the differences between three thermal storage systems.

Chilled water Ice storage Eutectic salt Specific heat kJ/kg K 4.19 2.04 Latent heat of fusion kJ/kg 334 80-250 Chiller cost $/kW 57-85 57-142 57-85

Tank Volume cu.

m/kWh 0.089-0.17 0.019-0.02 0.048 Storage installed cost $/kWh 8.5-28 14-20 28-43 Charging temperature deg. F 39-43 21-27 39-43 Charging efficiency COP 4.0-6.0 2.7-4.0 5.0-6.0 Discharge temperature deg. F 34-39 34-37 48-50

Based upon the desired excess solar generation shift, a corresponding BTU shift can be administered on several residential or commercial buildings with an appropriately sized thermal storage system to assist in shifting otherwise wasted excess solar generation.

Example: Excess solar generation shift with ICE thermal storage

Date Excess Peak Shifted (MWh)

PCM Storage (Ton-Hr)

BTU/Hr. Equivalent Electrical Energy

Equivalent # of Ice Bear 40 Ton ICE - PCM Units

03/17/2020 7,465 3,392,443 41,396,436,000 84,811 06/02/2020 9,577 4,352,234 52,226,808,000 108,805 10/12/2020 7,591 3,449,703 41,396,436,000 86,242


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Techinical Appendix DSM-18IImpacts of Temperature on Renewable Future

An increase in renewable capacity (specifically from solar) will be subject to the effects of non-ideal weather conditions. Extreme heat induced performance degradation, cloud coverage hindering sunlight and performance, as well as soiling due to excessive rain can all factor into poor performance in solar power systems. The effects of these weather patterns become more apparent as a utilities energy portfolio begins to lean into more solar generation.

Impact of High Ambient Temperatures on Photovoltaic Systems

Photovoltaic (PV) modules are factory rated at standard test condition (STC). These STCs usually consist of parameters such as, an irradiance of 1000 W/m2, temperature at 25°C and solar spectrum of Air Mass 1.5G. The actual output from the PV module in the field varies from its rated output due to changes in ambient environmental conditions from the STC. The reduction in output due to temperature is determined by a temperature coefficient which can vary based upon different types of solar module technologies. In a study performed for Int. Journal of Engineering Research and Applications, the results showed that the average temperature coefficient of power for mono-crystalline, multi-crystalline and CdTe based photovoltaic modules are -0.446 %/°C, -0.387 %/°C and -0.172 %/°C respectively. In case of amorphous silicon module, only one sample was measured, and the temperature coefficient was -0.234 %/°C. Dash, P. K., & Gupta, N. C. [1]. Based upon these findings the average temperature coefficient of power for mono-crystalline, multi-crystalline and CdTe based photovoltaic modules would be -0.446 %/ 1.8°F, -0.387 %/1.8°F and -0.172 %/ 1.8°F respectively for every 1.8°F increase above 77°F. Based upon results from the high temperature weather data analysis the following table depicts the minimum degradation in performance based upon the specified high ambient temperature.

Change in Performance %

Temperature (°F) Mono-Crystalline (-0.446%/1.8°F)

Multi-Crystalline (-0.387%/1.8°F)

CdTe (-0.172%/1.8°F)

90 -3.22 -2.80 -1.24 95 -4.46 -3.87 -1.72

100 -5.70 -4.95 -2.20 105 -6.94 -6.02 -2.68 110 -8.18 -7.10 -3.15 115 -9.42 -8.17 -3.63 120 -10.65 -9.25 -4.11 125 -11.89 -10.32 -4.59 130 -13.13 -11.40 -5.06


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IImpact of High Ambient Temperatures on Photovoltaic Inverter Systems Techinical Appendix DSM-18

Photovoltaic Inverts on the other hand deal with larger issues than degradation of performance at high ambient temperatures. Photovoltaic inverts that are exposed to extreme (high) ambient weather conditions have increased odds of operational failure over time. According to a study by the National Renewable Energy Laboratories (NREL), high ambient temperatures combined with inverter heat sink design, specifications, and other weather conditions such as wind can be used to identify possible points of failure with specific field deployed inverters Sorensen [2].

Impact of High Ambient Temperatures on Battery Storage Systems

Similar to energy production, energy storage is also subject to degradation in overall performance as a result of increased ambient temperature conditions. In a study performed for the Journal of Power Sources, laboratory-size lithium-ion pouch cells were cycled over 100% depth of discharge (DOD) at room temperature 25°C (77°F) and 60°C (140°F) in order to investigate high-temperature degradation mechanisms of capacity fading for individual battery cell components. The high-temperature cell lost 65% of its initial capacity after 140 cycles at 60°C (140°F) compared to only a 4% loss for the cell cycled at room temperature Shim, J[3].


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Techinical Appendix DSM-18BBuilding Demand Flexibility

As shown in previous sections, Grid needs can vary by, time of day, day of week, and season. Accordingly, a building may need to manage its electricity load in different ways during these times by reducing load through year-round energy efficiency, shifting load to different times of the day, or even increasing load to store for later use. In total, there are five modes with which buildings can provide demand flexibility: efficiency, shedding load, shifting load, modulating load, and generating electricity. These are defined below.

1. Efficiency: the ongoing reduction in energy use while providing the same or improved level of building function. This would have the greatest impact for the gird during high-cost periods and minimize utilization of costly generation resources. At a given level of energy efficiency and a given baseline load, a building can provide additional value by changing its load in various ways in response to grid signals. These are commonly referred to as shed, shift, and modulate.

2. Load Shed: the ability to reduce electricity use for a short time period and typically on short notice. Shedding is typically dispatched during peak demand periods and during emergencies.

3. Load Shift: the ability to change the timing of electricity use to minimize demand during peak periods or to take advantage of the cheapest electricity prices. A shift may lead to using more electricity during the cheapest time period and using thermal or battery storage at another time period when electricity prices increase.

4. Modulate: the ability to balance power supply/demand or reactive power draw/supply autonomously (within seconds to sub-seconds) in response to a signal from the grid operator during the dispatch period.

Below are examples of building technologies and strategies that provide energy flexibility:

Thermal storage. Ice or chilled water tanks can be actively controlled to shift HVAC loads. Electric water heaters—resistance or heat-pump--can also shift load or modulate electricity use. After decades of research, phase change materials (PCMs) are now finding their way into many applications. PCMs are substances that change phases from one to another (solid-liquid) at a specific temperature. As they change phase, they absorb or release thermal energy keeping the surrounding at fixed temperature. Unlike latent heat of water/ice, which can only occur at 0°C/32°F. PCMs can be utilized for higher or lower temperatures based on the applications due the flexibility of their engineered phase change transition temperature. The project team has long history in developing PCM products and known to have the world’s first commercialized patented solid-solid “non-leaking” PCM at room and low temperatures.

Comparing between latent heat of water/ice and flexible PCMs for thermal storage


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C

Due to the wide range of PCMs available at different phase transition temperatures, PCM solutions have the benefits of flexibility in terms of scalability to fit various applications for load shifting purposes and thermal energy storage.


Demonstration of peak load demand shifting (1PM-6PM) using PCM integrated HVAC system yielding

Current state of the art embedded thermal energy storage for building units include sensible heat chilled water and ice harvester or ice banks.

Cold Water Storage: the storage of chilled water in a tank provides additional capacity at reduced energy costs due to avoidance of peak demand charges. Unfortunately, this technology is often limited by spatial requirements due to waters low specific heat capacity. In addition, tanks often need to be oversized due to thermal stratification.

Hot Water Storage: the storage of hot water in an open vented or pressurized tank provides additional capacity at reduced energy costs due to avoidance of peak demand charges; hot water storage is the most common form of thermal storage in use today. Like all sensible heat storage systems, it is limited by spatial requirements due to waters low specific heat capacity.

Ice Storage: offers many of the same benefits that water storage provides, while using a much smaller footprint. However, can only be made at 0°C/32°F or lower, many existing chillers cannot make ice and, for those that can, the loss of chiller efficiency and chiller capacity at the low temperature required to make ice (23°F/(-5)°C to 27°F/(-3)°C), along with higher pumping costs, a need for glycol and increased system complexity, often offset the full benefit of using ice as a TES medium.

Unused real estate can provide valuable space for large chilled water tanks. A major plant replacement can come via low-temperature chiller and primary glycol loop to produce ice, which shifts the energy load. This in turn allows an economical response to demand. The challenge with traditional thermal storage applications, is that they often only address energy costs without achieving real energy reductions. Furthermore, traditional storage mediums are not “smart” and works in a single mode (cooling or heating) only. While system sizes can be adjusted to shift a desired load, the mediums themselves are fixed and cannot be individually optimized for each application. A comparison summary is listed below.

A comparison between conventional and dual (heating/cooling) PCM storage. Chilled water Ice Storage Dual PCM Storage

Latent heat Storage 0 J/g (4.18 J/g.K sensible) 334 J/g 200-240 J/g Heating or cooling Cooling only Cooling only Cooling and heating (wide PCM temp range) Chiller Cost 57-85 $/kW 85-142 $/kW 57-85 $/kW Tank volume 0.8-1.6 m3/kWh 0.17-0.35 m3/kWh 0.2-0.4 m3/kWh


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I

o

Techinical Appendix DSM-18Installed tank cost Retrofit additional cost or complexities

Charging temp

20-40 $/kWh 1. Require sizable tanks 2. Can be integrated into existing utilities without the need to increase existing chiller capacity or glycol loop 39-43 °F

14-20 $/kWh 1. Require new chiller installation that can make ice 2. Require addition of glycol secondary loop system 3. Require addition of intermediate heat exchanger 21-25 °F (limited by ice)

28-43 $/kWh Can be integrated into existing utilities without the need to increase existing chiller capacity or glycol loop.

35-43 °F (due to flexible/higher PCM temp) or 100-135 °F for heating

Efficiency (COP) 3.0 – 5.0 2.4 - 3.0 3.0 - 6.0 Discharge temp 35 – 45 °F 32 - 35°F (limited by ice temp) 42 - 50°F (flexible/varies depending on PCM)

or 110-130°F for heating Structural stresses Minimal Higher due to ice expansion PCMs expand only upon melting “in liquid

form” with mobility

Thermal mass. The thermally massive parts of a building’s physical structure—parts made of steel, concrete and masonry—have thermal inertia and allow a building to deploy pre-cooling or pre-heating strategies to shift HVAC energy loads to off-peak periods while maintaining comfortable temperature ranges for occupants. New materials such as phase change materials (PCMs) significantly increase thermal mass and load shifting capacity. Thermostat setpoints. Occupants have preferred thermal comfort ranges but may be willing to expand those ranges for short periods or under certain conditions. There are different strategies to curtail load within acceptable setpoints ranges including adjusting HVAC equipment speed, turning the equipment off for a short period, or reducing equipment run time if optimizing across a set of equipment. • Refrigeration setpoints. Refrigeration setpoints. Refrigerated goods also have ideal thermal ranges that can be expanded for short periods or under certain conditions to shed or shift load. Multi-speed and variable-speed motors. Multi-speed motors in fans, compressors, and pumps allow HVAC, refrigeration, and other equipment to shed load more flexibly by adjusting motor speeds with minimal impact on occupants and goods; (continuous) variable speed motors can also modulate load Lighting. Lighting can be dimmed to shed load. Connected lighting technologies can also be used to modulate load, though in limited capacities to avoid impacting occupant comfort. Smart appliances. Smart appliances including dishwashers, clothes washers, and clothes dryers can be scheduled to shift load [12]. Electronics and other plug loads. PCs, laptops, and other electronics—themselves solid-state devices— can run at various frequency and voltage settings to shed and modulate load.


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KKnowledge Gaps and Future Research Opportunities Techinical Appendix DSM-18

Little research has been done on technologies and strategies that optimize the interplay of high renewable energy sources deployment, energy efficiency and demand response, much less fully explore an optimized approach to demand flexibility. Some of studies suggest that demand response may conflict with energy efficiency by increasing overall energy use. In addition, energy efficiency has also been shown to limit the overall peak reduction potential of a building, known as ‘shed erosion’. As integrated energy efficiency and demand flexibility is a nascent and rapidly developing area of research, no consensus has been reached on the co-impacts. There are no known tools that perform such quantification or analysis for a large-scale study of buildings and grid interactions. Many researchers also note a lack of available data to study the combined effects of energy efficiency and demand flexibility. There are opportunities for improving building load management strategies, but additional work is needed to better understand the interactions and capacity of energy efficiency and demand flexibility to provide grid services. Major research needs include:

• Characterizing the type and amount of demand flexibility in different types of energy assets (traditional loads, on-site generation, and storage) and matching assets and grid services

• Determining which technologies (both emerging and on the market) have the greatest potential to provide demand flexibility of different modes

• Quantifying the impacts of different modes of demand flexibility on energy efficiency • Quantifying the impacts of different modes of demand flexibility on one another • Quantifying the impact of different modes of demand flexibility on occupant preferences

as well as the value of those impacts • Determining modes and mechanisms for engaging occupants in valuing and activating

demand • Identifying the trade-offs between functionality and security • Determining how to incorporate and update state-of-the-art security features into the

design process of control architectures • Determining the impacts of demand flexibility on equipment lifetime • Quantify how different modes of demand flexibility impact building envelope durability

(e.g., missing latent cooling with "shut down" sensible cooling) • Determining the right implementation level (device, end-use, zone or building) for

different modes of demand flexibility and different grid services


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Techinical Appendix DSM-18RReferences [1]. Dash, P. K., & Gupta, N. C. (2015). Effect of temperature on power output from different

commercially available photovoltaic modules. International Journal of Engineering Research and Applications, 5(1), 148-151.

[2]. Sorensen, N. R., Thomas, E. V., Quintana, M. A., Barkaszi, S., Rosenthal, A., Zhang, Z., & Kurtz, S. (2013). Thermal study of inverter components. IEEE journal of photovoltaics, 3(2), 807-813.

[3]. Shim, J., Kostecki, R., Richardson, T., Song, X., & Striebel, K. A. (2002). Electrochemical analysis for cycle performance and capacity fading of a lithium-ion battery cycled at elevated temperature. Journal of power sources, 112(1), 222-230.

[4]. The 2018 forecast is an update to: Plug-in Electric Vehicles Sales Forecast Through 2025 and the Charging Infrastructure Required. Edison Electric Institute and Institute for Electric Innovation. July 2017. https://www.edisonfoundation.net/iei/publications/Documents/IEI_EEI%20EV%20Forecast% 20Report_Nov2018.pdf

[5]. Lehman, T., Jones, D. D. and Vogel D. R., Off-peak HVAC is once again hot, J.Consulting Specifying Eng., pp 46-49, Nov. 2001.

[6]. ASHRAE, Thermal Energy Storage, ASHRAE Handbook of applications, American society of heating ventilation and air conditioning, Atlanta Georgia, (1995).

[7]. Zhou, G., Krarti, M. and Henze, G. P., Parametric analysis of active and passive building thermal storage utilization, ASME J. Solar Engineering, 127, pp 37-64, (2005).


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Swarm Logic enablesRTUs to provide coolingin a coordinated way.

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In accordance with pre-set parameters, building controls apply recommended decision set to RTUs.5

RTU data sent to Encycle Swarm Logic via the cloud.2 Swarm Logic algorithm

interprets data and defines a recommended decision set to optimize RTU operations.

3

Swarm Logic returns recommended decision set to the building controls.4

0001 100001111

000001100

��

Enroll with EASE™ and Reap the Rewards Your smart thermostats, when enabled with Swarm Logic by Encycle, will bring you a simple, elegant, inexpensive, and transformational way to immediately improve your financial bottom line. Take a look at your annual energy costs and calculate what a 10-20% savings on HVAC energy usage would mean to you. Then give us a call. We’ll show you what SwarmStat can do for your organization.

About Encycle Encycle provides unique benefits to many Fortune 500 companies. The EASE (Energy-as-a-Service by Encycle) program maximizes the performance of HVAC systems to save commercial and industrial companies money while reducing their carbon footprint. Based on patented Swarm Logic technology, Encycle’s platform uses biomimicry techniques to reduce HVAC energy costs, participate in demand response programs, and gain valuable insight into HVAC performance. Swarm Logic is simple and easy to install, optionally integrates with leading IoT solutions, and delivers ROIs that far exceed most solar, storage and lighting initiatives focused on the same goals. Encycle’s EASE program brings better results, at a lower annual cost and faster payback – it’s a win-win.

Encycle Corporation 1850 Diamond Street, Suite 105, San Marcos, CA, USA 92078 1 855-875-4031 [email protected] www.encycle.com

© 2019. All rights reserved. Encycle, Energy-as-a-Service by Encycle, Swarm Logic,SwarmStat, Swarm Service, Swarm IoT, Swarm Technology and Swarm Cloud are registered or pending trademarks of Encycle Corporation.

3


Cle

an E

nerg

y Pr

ogra

ms

of 342

Maj

or A

ccou

nt E

xecu

tives

Upd

ate

July

202

0

Agen

da

of 342

Intr

oduc

tions

Clea

n En

ergy

Pro

gram

s Rev

iew

2020

-202

1 Ne

w P

rogr

am Ye

ar

Cust

omer

Web

Tool

s

Que

stio

ns


2

DE

R St

rate

gy Techinical Appendix DSM-18

Legi

slat

ive

/ Reg

ulat

ory

Driv

ers

The

% o

f ret

ail s

ales

kW

h ta

rget

is o

ut o

f alig

nmen

t with

the

goal

s of

the

broa

der s

et o

f DER

rela

ted

legi

slatio

n. It

doe

s not

:

• pr

omot

e co

ordi

natio

n of

cust

omer

pro

gram

s for

ene

rgy

stor

age,

EV

, and

PV

adop

tion

• su

ppor

t ren

ewab

le e

nerg

y in

tegr

atio

n •

acce

lera

te d

ecar

boni

zatio

n SB145

50%

rene

wab

le e

nerg

y su

pply

by

2030

•

chan

ging

net

-load

shap

e •

over

supp

ly co

nditi

ons d

riven

by

exce

ss so

lar m

id-d

ay

• pe

ak o

ccur

s lat

er in

the

day

• in

crea

sed

ener

gy su

pply

and

who

lesa

le p

rice

vola

tility

The

% o

f ret

ail s

ales

kW

h ta

rget

doe

s not

valu

e or

pro

mot

e DS

M in

vest

men

ts in

load

flex

ibili

ty m

easu

res*

*Giv

en fi

nite

reso

urce

s, th

e cu

rren

t tar

get h

as p

rom

oted

a sh

ort t

erm

focu

s on

the

low

est c

ost m

easu

res i

n te

rms o

f firs

t yea

r ene

rgy

savi

ngs,

whi

ch p

rovi

de li

mite

d as

sista

nce

with

the

man

agem

ent o

f mid

-day

ov

ersu

pply

cond

ition

s, ea

rly e

veni

ng p

eaks

, and

incr

ease

d su

pply

vola

tility

.

of 342

Evol

ving

Loa

d C

urve

No M

ust-T

ake

Utili

ty-

leve

l Sol

ar G

ener

atio

n

/ / / / / / / / / / / / / /

/ / / / / / / / / / / / / /

With

Mus

t-Tak

e Ut

ility

-le

vel S

olar

Gen

erat

ion

/ / / / / / / / / / / / /

/ / / / / / / / / / / / /

of 342

2000

2500

3000

3500

4000

4500

5000

5500

6000

6500

7000

7500

13

57

911

1315

1719

2123

MW

NPC

Annu

al P

eakD

ayLo

ad P

rofil

e20

19-4

9

0701

/201

9

0714

/202

0

0705

/202

1

0704

/202

2

0703

/202

3

0701

/202

4

0715

/202

5

0714

/202

6

0705

/202

7

0703

/202

8

0702

/202

9

0701

/203

0

0715

/203

1

0713

/203

2

800

1000

1200

1400

1600

1800

2000

2200

13

57

911

1315

1719

2123

MW

SPPC

Ann

ual P

eak

Day

Load

Prof

ile20

19-4

9

0724

/201

9

0722

/202

0

0728

/202

1

0727

/202

2

0726

/202

3

0724

/202

4

0723

/202

5

0722

/202

6

0728

/202

7

0726

/202

8

0725

/202

9

0724

/203

0

0723

/203

1

0721

/203

2

0

1000

2000

3000

4000

5000

6000

7000

13

57

911

1315

1719

2123

MW

NPC

IRP

Plan

ned

Sola

rGen

erat

ion

Impa

cted

Sy

stem

Pea

k Lo

ad20

19-4

907

01/2

019

0714

/202

0

0705

/202

1

0704

/202

2

0703

/202

3

0701

/202

4

0715

/202

5

0714

/202

6

0705

/202

7

0703

/202

8

0702

/202

9

0701

/203

0

0715

/203

1

0

500

1000

1500

2000

13

57

911

1315

1719

2123

MW

SPPC

IRP

Plan

ned

Sola

r Gen

erat

ion

Impa

cted

Sy

stem

Pea

k Lo

ad20

19-4

907

24/2

019

0722

/202

0

0728

/202

1

0727

/202

2

0726

/202

3

0724

/202

4

0723

/202

5

0722

/202

6

0728

/202

7

0726

/202

8

0725

/202

9

0724

/203

0

0723

/203

1



1 0 Ba

se Lo

ad G

ener

atio

n

Sola

r Ge

nera

tion

38%

12 p

m

5 pm

Driv

ers

for I

nnov

atio

n C

urre

nt S

tate

DR

MS

Syst

em P

eak

Shap

ing

10-m

in s

pinn

ing

rese

rve

140,

000+

DR

dev

ices

(p

rimar

ily th

erm

osta

ts)

Full

DER

Inte

grat

ion

Feed

er P

eak

Shap

ing

Anci

llary

Ser

vice

s Sm

art I

nver

ter C

ontro

l PV

+ S

tora

ge

Futu

re S

tate

of 342

Ope

ratio

nal I

mpr

ovem

ents

Ope

ratin

g Re

serv

e Di

spat

ch

Econ

omic

Disp

atch

of 342


6

• En

ergy

Effi

cienc

y •

Load

Fle

xibili

ty /

Dem

a •

Educ

atio

n &

Out

reac

h

ctric

Veh

icles

er

gy S

tora

ge

new

able

s du

catio

n &

Out

reac

h

• Ti

me-

of-U

se

• Hi

gh L

oad

Fact

or

• Tr

ansit

iona

l Dem

and

Ch•

Optio

nal C

ritica

l Pea

k •

Optio

nal R

esid

entia

l D

Char

ges

• EV

Tim

e-of

-Use

Rat

es

Key

Acco

unt R

epre

sent

ativ

es

24/7

Cus

tom

er S

ervi

ce

Busin

ess S

olut

ions

Cen

ter

Cus

tom

er P

rogr

am O

fferin

gs

• El

ec

• En

e •

Ren

• Ed

u

Clea

n En

ergy

Pr

ogra

ms

• K

• 2

• B

Conc

ierg

e Se

rvice

Ch

arge

Pr

icing

De

man

d

Rate

Opt

ions

and

Resp

onse

Dem

and

Side

M

anag

emen

t Pr

ogra

ms

Affo

rdab

ility

Awar

enes

s

of 342

nd R

espo

nse

•El

e•

En•

Re•

E

arge

Prici

ngem

and

• • •

Acce

ssib

ilityTechinical Appendix DSM-18

Prog

ram

Del

iver

y O

verv

iew

Out

reac

h

TV C

omm

ercia

ls

Pres

s Rel

ease

s

Socia

l Med

ia

Tabl

ing

Even

ts

Conc

ierg

e Se

rvice

Surv

eys

Trai

ning

EV R

ide

and

Drive

s

EV10

1 Cl

asse

s

Ener

gy St

orag

e 10

1

Onl

ine

Tool

s

Wor

kfor

ce

Deve

lopm

ent

Deal

ersh

ip

Prog

ram

s

Ince

ntiv

es

Ener

gy St

orag

e

Char

ging

Stat

ion

/ M

ake-

Read

y In

cent

ives

EV C

usto

m G

rant

Corr

idor

De

velo

pmen

t

Tech

nica

l As

sista

nce

Tech

nolo

gy

Eval

uatio

n

Bill

Impa

ct A

naly

sis

Busin

ess C

ase

Deve

lopm

ent

EV S

uita

bilit

y

Rate

O

ptio

ns

EV T

ime-

Of-U

se

EV C

omm

ercia

l De

man

d Ch

arge

Rate

Criti

cal P

eak

Prici

ng

Daily

Dem

and

Prici

ng

of 342


Cle

an E

nerg

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ogra

ms

Rene

wab

le In

terc

onne

ctio

ns

Ener

gy S

tora

ge

Smal

l En

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St

orag

e

Larg

e

Stor

age

Ener

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Elec

tric

Veh

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Pro

gram

s

Char

ging

St

atio

n In

cent

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Tech

nica

l Ad

viso

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Serv

ices

Nev

ada

Elec

tric

Elec

tric

Ve

hicl

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stom

Gra

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Hig

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tric

Sc

hool

Bus

In

cent

ives

Educ

atio

n &

O

utre

ach

Rene

wab

le E

nerg

y In

terc

onne

ctio

n

Low

er In

com

e So

lar

Ener

gy

Prog

ram

of 342


9

Cle

an E

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ogra

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Rev

iew

of 342


Sola

r Sal

es V

ideo

of 342


POLL

: Int

erco

nnec

tion

Appl

icat

ions

of 342


Inte

rcon

nect

ion

Appl

icat

ions

12 M

onth

Ave

rage

: 27

2 ap

plica

tions

pe

r wee

k

of 342


13

Net

Met

erin

g Tr

anch

e Su

mm

ary

of 342


14

Sola

r Ins

talla

tion

Hea

t Map

*All

sola

r app

licat

ions

from

Sep

t. 20

04 th

roug

h Ju

ne 2

020

of 342


15

Ener

gy S

tora

ge P

rogr

ams

Rule

15

Ener

gy S

tora

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stal

ls by

Mon

th

(July

1, 2

019

to Ju

ne 3

0, 2

019)

55

50

45

40

35

30

25

20

15

10 5 0

21

31

7

23

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19

25

22

29

47

13

25

2

4

1

5 2

3

1

1

4

4

Jul

Aug

Sep

Oct

Nov

Dec

Jan

Feb

Mar

Ap

r M

ay

Jun

2019

20

20

315

inst

alls

durin

g PY

20

19-2

020

Ince

ntiv

e No

n-In

cent

ive

of 342


16

Ener

gy S

tora

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ergy

Sto

rage

Inst

alls

by R

egio

n (Ju

ly 1

, 201

9 to

June

30,

202

0)

Ener

gy S

tora

ge In

stal

ls by

Man

ufac

ture

r (Ju

ly 1

, 201

9 to

June

30,

202

0)

200

175

180

Tesla

, 242

, 77%

LG C

hem

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20%

160

PIKA

ENE

RGY,

2,

1%

14

0 So

l-Ark

, 1, 0

%

120

Sonn

enBa

tter

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, 1%

Nevo

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0 Si

mpl

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20

Tesla

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*as o

f Sep

tem

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0, 2

019

140

Nort

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ada

Sout

hern

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ada

of 342


17

POLL

: Ele

ctric

Veh

icle

s

of 342


Elec

tric

Vehi

cles

in N

evad

a

2018

EV

2019

EV

6,00

0 5,

598

5,00

0

4,00

0

3,33

1

3,00

0

2,00

0

1,36

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0

7441

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6 3

3

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2019

EV

Regi

stra

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bro

ken

by C

ount

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of 342


Elec

tric

Vehi

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in N

evad

a

ELEC

TRIC

VEH

ICLE

REG

ISTR

ATIO

N

% R

etai

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1971

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FY2

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FY2

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1

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2

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3

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01

4

FY2

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FY20

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FY20

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FY20

12

FY20

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FY20

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FY20

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19

of 342


Elec

tric

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Ince

ntiv

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plic

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EV In

cent

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Appl

icatio

n Su

bmiss

ion

Tren

d 20 18 16 14 12 10 8 6 4 2 0

2018

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2019

Q1

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2019

Q3

2019

Q4

2020

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Application Volume

of 342


Elec

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Prog

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22

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of 342


Cle

an E

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July

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020 –

June

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Cle

an E

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Rene

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25

Low

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Lo

w In

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f Jul

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, 202

0)

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Nort

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Rese

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00

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759

$59,

241

of 342


26

Low

er In

com

e So

lar E

nerg

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m

-El

igib

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: -

Fede

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IHTC

des

igna

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for L

ower

Inco

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mul

ti-fa

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hou

sing.

-

Serv

e a

signi

fican

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ulat

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of lo

wer

inco

me

indi

vidu

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the

first

six

mon

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fter t

he st

art o

f LIS

EP, i

f an

appl

icant

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s the

ir in

cent

ive,

th

en th

e in

cent

ive

amou

nt w

ill b

e m

ade

avai

labl

e w

ithin

the

Bloc

k th

at it

was

or

igin

ally

allo

cate

d.

-Af

ter s

ix (6

) mon

ths,

unre

serv

ed in

cent

ive

fund

s with

in a

Blo

ck w

ill b

ecom

e av

aila

ble

to o

ther

Blo

cks i

n ac

cord

ance

with

the

Cond

ition

al R

eser

vatio

n No

tice.

of 342


27

Ener

gy S

tora

ge P

rogr

ams

• In

cent

ivize

s 4kW

-100

kW

stor

age

• Re

siden

tial,

smal

l bus

ines

s, ne

w co

nstr

uctio

n •

Resid

entia

l mus

t be

inte

grat

ed w

ith re

new

able

s •

Stan

dalo

ne o

ptio

n fo

r bus

ines

s cus

tom

ers

• St

anda

lone

ince

ntiv

e re

quire

s at l

east

5 y

ears

on

a TO

U ra

te

• 12

mon

th re

serv

atio

n •

May

app

ly fo

r 2 si

x-m

onth

ext

ensio

ns

Smal

l Ene

rgy

Stor

age

Prog

ram

• In

cent

ivize

s 100

-100

0 kW

stor

age

• M

ust b

e in

tegr

ated

with

rene

wab

les

• La

rge

com

mer

cial a

nd in

dust

rial

• Pr

iorit

izes c

ritica

l inf

rast

ruct

ure

• 18

mon

th re

serv

atio

n •

May

app

ly fo

r 3 si

x-m

onth

ext

ensio

ns

Larg

e En

ergy

Sto

rage

Pro

gram

of 342


28

Smal

l Ene

rgy

Stor

age

Prog

ram

s 7/

1/20

19 –

6/3

0/20

20

7/1/

2020

– 6

/30/

2021

• In

cent

ive C

ap (L

esso

r of)

• 50

% o

f ins

talle

d sy

stem

cos

ts; o

r •

TOU:

$3,

000

per p

rem

ise

• No

n-TO

U: $

3,00

0 pe

r pre

mise

(

TOU

$0.2

2/w

att-h

our

Non

-TO

U $0

.11/

wat

t-ho

ur

• In

cent

ive C

ap (L

esso

r of)

• 50

% o

f ins

talle

d sy

stem

cos

ts; o

r •

TOU:

$3,

000

per p

rem

ise

• No

n-TO

U: $

1,50

0 pe

r pre

mise

TOU

$0.1

9/w

att-h

our

Non-

TOU

$0.0

95/w

att-

hour

• In

cent

ive C

ap (L

esso

r of)

• $5

0,00

0 pe

r pre

mise

; or

• Re

new

able

Inte

grat

ed: 5

0% o

f in

stal

led

syst

em c

osts

;

ITC

Elig

ible

$0

.25/

wat

t-hou

r N

on-IT

C El

igib

le

$0.3

5/w

att-h

our

Rene

wab

le In

tegr

ated

Residential 4kW – 100kW

Co mmerci al 4kW – 100kW

of 342

• In

cent

ive C

ap (L

esso

r of)

• $5

0,00

0 pe

r pre

mise

; or

• Re

new

able

Inte

grat

ed: 5

0% o

fin

stal

led

syst

em c

osts

; •

Stan

dalo

ne E

nerg

y St

orag

e: 7

0% o

fin

stal

led

syst

em c

osts

*Sta

ndal

one

stor

age

mus

t be

on a

TO

U Ra

te fo

r 5 y

ears

ITC

Elig

ible

$0.3

2/w

att-h

our

Non

-ITC

Elig

ible

$0.4

2/w

att-h

our

i (

For P

rofit

$0.4

5/w

att-h

our

f)

Non-

Prof

it$0

.55/

wat

t-hou

r

Stan

dalo

ne E

nerg

y St

orag

e*

Rene

wab

le In

tegr

ated


29

100kW – 1MW

Larg

e En

ergy

Sto

rage

Pro

gram

s

7/1/

2019

– 6

/30/

2020

7/

1/20

20 –

6/3

0/20

21

• In

cent

ive C

ap (L

esso

r of)

• 50

% o

f ins

talle

d sy

stem

cost

s; or

• $3

00,0

00 p

er p

rem

ise fo

r non

-cr

itica

l inf

rast

ruct

ure;

or

• $4

00,0

00 p

er p

rem

ise fo

rcr

itica

l inf

rast

ruct

ure

ITC

Elig

ible

$0.4

0/w

att-h

our

Non-

ITC

Elig

ible

$0.5

0/w

att-h

our

(

ITC

Elig

ible

$0.3

0/w

att-h

our

Non-

ITC

Elig

ible

$0.4

0/w

att-

hour

Non-

Criti

cal I

nfra

stru

ctur

e

Criti

cal I

nfra

stru

ctur

e

• In

cent

ive C

ap (L

esso

r of)

• 70

% o

f ins

talle

d sy

stem

cost

s; o

r•

$300

,000

per

pre

mise

for n

on-

criti

cal i

nfra

stru

ctur

e; o

r•

$400

,000

per

pre

mise

for c

ritica

lin

frast

ruct

ure

ITC

Elig

ible

$0.5

0/w

att-

hour

N

on-IT

C El

igib

le$0

.60/

wat

t-ho

ur

(

ITC

Elig

ible

$0.4

0/w

att-h

our

f)

Non-

ITC

Elig

ible

$0.5

0/w

att-

hour

Non-

Criti

cal I

nfra

stru

ctur

e

Criti

cal I

nfra

stru

ctur

e

of 342


30

Cha

rgin

g St

atio

n In

cent

ives

• Ch

argi

ng st

atio

ns a

re lo

cate

d at

com

mer

cial f

acili

ties a

n in

tend

ed to

be

used

for c

ompa

ny ve

hicle

s

Mul

ti-Fa

mily

• Ch

argi

ng st

atio

ns a

re lo

cate

d at

resid

entia

l dw

ellin

gs w

ith tw

o or

mor

e un

its a

nd in

tend

ed fo

r res

iden

ts

Flee

t

Wor

kpla

ce

• Ch

argi

ng st

atio

ns a

re lo

cate

d at

com

mer

cial w

orkp

lace

lo

catio

ns a

nd m

ust b

e ac

cess

ible

and

use

d by

em

ploy

ees

Publ

ic

• Ch

argi

ng st

atio

ns m

ust b

e av

aila

ble

to th

e pu

blic

in p

ublic

are

as

and

cann

ot b

e lo

cate

d in

are

as u

nava

ilabl

e to

the

publ

ic

of 342


31

Cha

rgin

g St

atio

n In

cent

ives

Wor

kpla

ce

Publ

ic

Flee

t

Mul

tifam

ily

Prog

ram

Co

nstr

aint

s

of 342

Min

imum

# P

orts

$3,0

00 /

pe

r por

t

$3,0

00 /

pe

r por

t

$5,0

00 /

pe

r por

t

$5,0

00 /

pe

r por

t

L2 In

cent

ive =

less

er o

f:

$30,

000

$50,

000

75%

of

tota

l pr

ojec

t co

sts

75%

of

tota

l pr

ojec

t co

sts

Ince

ntive

M

axim

um

$400

/kW

$4

0,00

0 m

axim

um

$400

/kW

$4

0,00

0 m

axim

um

DCFC

Ince

ntiv

e

Leve

l 2

DC Fa

st C

harg

er

2 1

Max

imum

# P

orts

10

5


32

Cha

rgin

g St

atio

n In

cent

ives

$7,5

00 /

pe

r por

t

$2,5

00 /

pe

r por

t

L2 In

cent

ive =

less

er o

f:

100%

of

tota

l pro

ject

co

sts*

$7

,500

/

per p

ort

$2,5

00 /

pe

r por

t

NV E

nerg

y

GOE*

NV E

nerg

y

GOE*

Mul

tifam

ily

Low

er In

com

e

Gove

rnm

enta

l Ag

encie

s

$40,

000

Ince

ntive

M

axim

um

*GO

E Fu

ndin

g is

subj

ect t

o Le

gisla

tive

appr

oval

that

is e

stim

ated

for s

omet

ime

in A

ugus

t.

Prog

ram

Co

nstr

aint

s

of 342

Max

imum

# P

orts

4

Leve

l 2

Min

imum

# P

orts

2


33

Cha

rgin

g St

atio

n In

cent

ives

Cust

omer

Elig

ibili

ty

• Fu

ll Se

rvice

NV

Ener

gy C

usto

mer

Equi

pmen

t Elig

ibili

ty

• Ne

twor

ked

Char

ger

• UL

List

ing

• Ne

w /

Neve

r Use

d •

Inst

alle

d by

a Li

cens

ed C

2 El

ectr

ical C

ontr

acto

r •

50kW

min

imum

for D

CFC

of 342

50kW

min

imum

for D

CFC


34

Cha

rgin

g St

atio

n In

cent

ives

Leve

l 2 ch

arge

rs th

at ca

n on

ly ch

arge

one

indi

vidu

al v

ehicl

e m

ake

can

be in

cent

ivize

d in

non

-pub

lic c

harg

ing

cate

gorie

s

Max

# o

f Pr

oprie

tary

Po

rts

Num

ber o

f Pro

prie

tary

Por

ts A

utho

rized

Num

ber o

f Tot

al P

orts

2

3 4

5 6

7 8

9 10

Fl

eet

10

2 3

4 5

6 7

8 9

10

Mul

tifam

ily

5 1

1 2

2 3

3 4

4 5

Wor

kpla

ce

3 1

1 2

2 2

2 3

3 3

Low

er-In

com

e M

ulitf

amily

2

1 1

2 N/

A N/

A N/

A N/

A N/

A N/

A Pu

blic

0 0

0 0

0 0

0 0

0 0

Gove

rnm

enta

l 0

0 0

0 0

0 0

0 0

0


of 342

35

Elec

tric

Scho

ol B

us In

cent

ives

Sena

te B

ill 2

99, a

ppro

ved

on M

ay 2

4th

2019

$ 3,

000,

000.

00 in

ince

ntiv

es a

vaila

ble.

Publ

ic S

choo

ls in

NV

Ener

gy s

ervi

ce a

rea.

75%

of t

otal

cos

ts o

f ele

ctric

sch

ool b

us a

nd/o

r cha

rgin

g in

fras

truc

ture

.

Cha

rgin

g in

fras

truc

ture

sup

port

ed b

y th

e in

cent

ive

prog

ram

may

incl

ude:

cha

rger

s,

tran

sfor

mer

s, e

lect

ric p

anel

s, in

stal

latio

n la

bor &

mat

eria

ls, p

lann

ing

and

engi

neer

ing

serv

ices

, sig

nage

and

logo

s, u

nder

grou

nd w

ork.

DC

Fas

t Cha

rger

s 50

kW m

inim

um

5% o

f tot

al c

osts

may

be

used

for t

rain

ing

(ope

ratio

ns a

nd m

aint

enan

ce)

App

rove

d ap

plic

atio

ns w

ill h

ave

fund

s re

serv

ed fo

r 24

mon

ths

plus

and

one

-yea

r, on

e-

time

exte

nsio

n.

of 342


36

Tech

nica

l Adv

isor

y Se

rvic

es

Tech

nolo

gy

Cust

omer

Edu

catio

n

Inte

grat

ing

Mul

tiple

Te

chno

logi

es (s

izing

so

lar/

stor

age)

Ope

ratio

nal

Use

Case

s Rev

iew

EV S

uita

bilit

y

EV M

ake-

read

y ne

eds

Bill

Impa

cts

Rene

wab

les

Ener

gy S

tora

ge

Anal

ysis

Rene

wab

les

+ En

ergy

Sto

rage

Elec

tric

Vehi

cles

Rate

An

alys

is

Eval

uatin

g Av

aila

ble

Rate

Opt

ions

Rate

Opt

imiza

tion

/Se

nsiti

vity

Ana

lysis

Busin

ess

Case

Su

ppor

t

Bene

fit/C

ost

Anal

ysis

EV R

oute

Ana

lysis

Retu

rn o

n In

vest

men

t An

d Pa

ybac

k Pe

riod

Avai

labl

e Fu

ndin

gSo

urce

s

of 342


37

Que

stio

ns?

of 342


Web

Too

ls

of 342


MyA

ccou

nt T

ools

Ener

gy

Usag

e

Mul

tiple

dat

a po

ints

Mul

tiple

dat

a in

crem

ents

Data

Do

wnl

oad

Vary

ing

dow

nloa

d fo

rmat

s

Up to

24

mon

ths o

f dat

a

of 342

Bill

Stat

emen

ts

Onl

ine

Stat

emen

t

PDF

Dow

nloa

d

Ener

gy

Savi

ng

Prog

ram

s O

nlin

e En

ergy

As

sess

men

ts

In H

ome

Ener

gy

Asse

ssm

ents

Smar

t Th

erm

osta

ts

Resid

entia

l A/C

Di

scou

nts


40

MyA

ccou

nt T

ools

of 342


41

Sola

r Cal

cula

tor

of 342


42

Elec

tric

Vehi

cle

Com

paris

on T

ool

of 342


43

Elec

tric

Vehi

cle

Com

paris

on T

ool

of 342


44

Crit

ical

Pea

k Pr

icin

g R

ate –

Res

iden

tial –

Nev

ada

Pow

SU

MM

ER

WEE

KDAY

RAT

ES:

June

thro

ugh

Sept

embe

r

OFF-

PEAK

$0.

0577

6

CRIT

ICAL

PEA

K $

0.65

102

$0.0

5776

ON

PEA

K $0

.387

00

12:0

0 a.

m.

01:0

0 p.

m.

07:0

0 p.

m.

Mid

nigh

t

WEE

KEND

RAT

ES:

June

thro

ugh

Sept

embe

r OFF-

PEAK

$0.

0577

6 12

:00

a.m

. N

oon

Mid

nigh

t

WIN

TER

WEE

KDAY

RAT

ES:

Oct

ober

thro

ugh

May

WIN

TER

RATE

$0.

0466

0 12

:00

a.m

. 01

:00

p.m

. 07

:00

p.m

. M

idni

ght

WEE

KEND

RAT

ES:

Oct

ober

thro

ugh

May

OFF-

PEAK

$0.

0466

0 12

:00

a.m

. N

oon

Mid

nigh

t

Basic

Ser

vice

Cha

rge

-$12

.50

Jun-

Sep

1:01

-7:0

0 pm

non

-hol

iday

wee

kday

s

Basic

Ser

vice

Cha

rge

-$12

.50

er


** T

hese

rate

s are

as o

f Jul

y 20

20

of 342

Crit

ical

Pea

k Pr

icin

g R

ate –

Res

iden

tial –

Sie

rra

Paci

fic

SUM

MER

W

EEKD

AY R

ATES

: Ju

ly th

roug

h Se

ptem

ber

OFF-

PEAK

$0.

0490

0

CRIT

ICAL

PEA

K $

0.54

541

$0.0

4900

ON

PEA

K $0

.464

37

12:0

0 a.

m.

01:0

0 p.

m.

06:0

0 p.

m.

Mid

nigh

t

WEE

KEND

RAT

ES:

June

thro

ugh

Sept

embe

r OFF-

PEAK

$0.

0490

0 12

:00

a.m

. N

oon

Mid

nigh

t

WIN

TER

WEE

KDAY

RAT

ES:

Oct

ober

thro

ugh

June

Jul-S

ep 1

:01-

6:00

pm

non

-hol

iday

wee

kday

s

Basic

Ser

vice

Cha

rge

-$15

.25

Basic

Ser

vice

Cha

rge

-$15

.25

OFF

-PEA

K $0

.049

00

$0.0

4900

ON

PEA

K $0

.081

76

12:0

0 a.

m.

05:0

0 p.

m.

09:0

0 p.

m.

Mid

nigh

t

WEE

KEND

RAT

ES:

June

thro

ugh

Sept

embe

r OFF-

PEAK

$0.

0490

0

12:0

0 a.

m.

Noo

n M

idni

ght

Oct

-Jun

5:0

1 pm

-9:

00 p

m d

aily


** T

hese

rate

s are

as o

f Jul

y 20

20

of 342

Dai

ly D

eman

d Pr

icin

g –

Res

iden

tial –

Nev

ada

Pow

er

SUM

MER

W

EEKD

AY R

ATES

: Ju

ne th

roug

h Se

ptem

ber

Rate

/kW

h =

$0.0

7832

12:0

0 a.

m.

01:0

0 p.

m.

07:0

0 p.

m.

Mid

nigh

t

WEE

KEND

RAT

ES:

June

thro

ugh

Sept

embe

r

Rate

/kW

h =

$0.0

7832

12:0

0 a.

m.

Noo

n M

idni

ght

WIN

TER

WEE

KDAY

RAT

ES:

Octo

ber t

hrou

gh M

ay Ra

te/k

Wh

= $0

.078

32

12:0

0 a.

m.

01:0

0 p.

m.

07:0

0 p.

m.

Mid

nigh

t

Basic

Ser

vice

Cha

rge

-$10

.25

Jun-

Sep

1:01

-7:0

0 pm

no

n-ho

liday

wee

kday

s

Basic

Ser

vice

Cha

rge

-$10

.25

Faci

lity

Char

ge -

$0.1

5 Fa

cilit

y Ch

arge

-$0

.15

Dem

and

Char

ge -

$0.0

6

On

Peak

Dem

and

Char

ge -

$0.2

2


** T

hese

rate

s are

as o

f Jul

y 20

20

of 342

Dai

ly D

eman

d Pr

icin

g –

Res

iden

tial –

Sie

rra P

acifi

c

SUM

MER

W

EEKD

AY R

ATES

: Ju

ly th

roug

h Se

ptem

ber Rate

/kW

h –

0.05

204

12:0

0 a.

m.

01:0

0 p.

m.

06:0

0 p.

m.

Mid

nigh

t

WEE

KEND

RAT

ES:

July

thro

ugh

Sept

embe

r Rate

/kW

h –

0.05

204

12:0

0 a.

m.

Noo

n M

idni

ght

WIN

TER

WEE

KDAY

RAT

ES:

Octo

ber t

hrou

gh Ju

ne Ra

te/k

Wh

– 0.

0520

4 12

:00

a.m

. 05

:00

p.m

. 09

:00

p.m

. M

idni

ght

WEE

KEND

RAT

ES:

Oct

ober

thro

ugh

June

Rate

/kW

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of 342

CT4000 Level 2 Commercial Charging Station Specifications and Ordering Information

Ordering Information Specify model number followed by the applicable code(s). The order code sequence is: Model-Options. Software, Services and Misc are ordered as separate line items.

Hardware

*Substitute n for desired years (1, 2, 3, 4, or 5 years).

Order Code Examples If ordering this the order code is

1830 mm (6 ft) Dual Port Bollard Networked Station with Concrete Mounting Kit

ChargePoint Commercial Service Plan, 3 Year Subscription

ChargePoint Station Installation and Validation

3 Years of Assure Coverage

1830 mm (6 ft) Single Port Wall Mount Networked CT4013-GW1 Station CPCLD-COMMERCIAL-5 ChargePoint Commercial Service Plan, 5 Year Subscription CT4000-ASSURE5 5 Years of Assure Coverage CPSUPPORT-ACTIVE Station Activation and Configuration

CT4021

Model 1830 mm (6 ft) Single Port Bollard Mount 1830 mm (6 ft) Dual Port Bollard Mount

1830 mm (6 ft) Single Port Wall Mount 1830 mm (6 ft) Dual Port Wall Mount

2440 mm (8 ft) Dual Port Bollard Mount 2440 mm (8 ft) Dual Port Wall Mount

CT4011-GW1 CT4021-GW1

CT4013-GW1 CT4023-GW1

CT4025-GW1 CT4027-GW1

Integral Modem - North America -GW1

Misc Power Management Kit Bollard Concrete Mounting Kit

CT4000-PMGMT CT4001-CCM

Note: All CT4000 stations include Intergral Modem -GW1.

Software & Services Description

ChargePoint Commercial Service Plan

ChargePoint Enterprise Plan CPCLD-ENTERPRISE-n*

ChargePoint Assure

Station Activation and Configuration CPSUPPORT-ACTIVE

ChargePoint Station Installation and Validation

CPCLD-COMMERCIAL-n*

CT4000-ASSUREn*

CT4000-INSTALLVALID

CT4021-GW1 CT4001-CCM

CPCLD-COMMERCIAL-5

CT4000-INSTALLVALID

CT4000-ASSURE5

of 342


The First ENERGY STAR® Certified EV Charger

Description Order Code

Included

Order Code

Note: All CT4000 stations require a network service plan per port.


of 342

ChargePoint CT4000 Family

CT4021 1830 mm (6') CT4023 1830 mm (6') CT4025 2440 mm (8') CT4027 2440 mm (8') Bollard Wall Mount

289 mm (11.4") 470 mm

(18.5")

302 mm (11.9") 483 mm

(19.0")

347 mm (13.7")

233 mm (9.2")

347 mm (13.7")

233 mm (9.2")

CT4025 2426 mm

(95.5")

CT4021 1811 mm (71.3")

CT4027 2426 mm

(95.5")

CT4023 1811 mm (71.3")

1186 mm (46.7")

1184 mm (46.6")

2 chargepoint.com

CT4000 Family Specifications


Electrical Input Single Port (AC Voltage 208/240V AC) Dual Port (AC Voltage 208/240V AC)

Input Current

Input Power Connection

Required Service Panel Breaker

input Current

Input Power Connection

Required Service Panel Breaker

Standard 30A One 40A branch circuit 40A dual pole (non-GFCI type) 30A x 2 Two independent 40A

branch circuits 40A dual pole

(non-GFCI type) x 2

Standard Power Share n/a n/a n/a 32A One 40A branch circuit 40A dual pole (non-GFCI type)

Power Select 24A 24A One 30A branch circuit 30A dual pole (non-GFCI type)

24A x 2 Two independent 30A branch circuits

30A dual pole (non-GFCI type) x 2

Power Select 24A Power Share n/a n/a n/a 24A One 30A branch circuit 30A dual pole (non-GFCI type)

Power Select 16A 16A One 20A branch circuit 20A dual pole (non-GFCI type)

16A x 2 Two independent 20A branch circuits

20A dual pole (non-GFCI type) x 2

Power Select 16A Power Share n/a n/a n/a 16A One 20A branch circuit 20A dual pole (non-GFCI type)

Service Panel GFCI Do not provide external GFCI as it may conflict with internal GFCI (CCID)

Wiring - Standard 3-wire (L1, L2, Earth) 5-wire (L1, L1, L2, L2, Earth)

Wiring - Power Share n/a 3-wire (L1, L2, Earth)

Station Power 8 W typical (standby), 15 W maximum (operation)

Electrical Output Standard

Standard Power Share

7.2 kW (240V AC @ 30A)

n/a

7.2 kW (240V AC @ 30A) x 2

7.2 kW (240V AC @ 30A) x 1 or 3.8 kW (240V AC @ 16A) x 2

Power Select 24A 5.8 kW (240V AC @ 24A) 5.8 kW (240V AC @ 24A) x 2

Power Select 24A Power Share n/a 5.8 kW (240V AC @ 24A) x 1 or 2.9 kW (240V AC @ 12A) x 2

Power Select 16A 3.8 kW (240V AC @ 16A) 3.8 kW (240V AC @ 16A) x 2

Power Select 24A Power Share n/a 3.8 kW (240V AC @ 16A) x 1 or 1.9 kW (240V AC @ 8A) x 2

Functional Interfaces Connector(s) Type

Cable Length - 1830 mm (6 ft) Cable Management

SAE J1772™ SAE J1772™ x 2

5.5 m (18 ft) 5.5 m (18 ft) x 2

Cable Length - 2440 mm (8 ft) Cable Management

n/a 7 m (23 ft)

Overhead Cable Management System

Yes

LCD Display 145 mm (5.7 in) full color, 640 x 480, 30 fps full motion video, active matrix, UV protected

Card Reader

Locking Holster

ISO 15693, ISO 14443, NFC

Yes Yes x 2

chargepoint.com 3 of 342

of 342

Techinical Appendix DSM-18ChargePoint CT4000 Family

Safety and Connectivity Features Ground Fault Detection

Open Safety Ground Detection

20 mA CCID with auto retry

Continuously monitors presence of safety (green wire) ground connection

Plug-Out Detection Power terminated per SAE J1772™ specifications

Power Measurement Accuracy +/- 2% from 2% to full scale (30A)

Power Report/Store Interval 15 minute, aligned to hour

Local Area Network

Wide Area Network

2.4 GHz WiFi (802.11 b/g/n)

LTE Category 4

Safety and Operational Ratings Enclosure Rating

Safety Compliance

Type 3R per UL 50E

UL listed and cUL certified; complies with UL 2594, UL 2231-1, UL 2231-2, and NEC Article 625

Surge Protection 6 kV @ 3,000A. In geographic areas subject to frequent thunder storms, supplemental surge protection at the service panel is recommended.

EMC Compliance FCC Part 15 Class A

Operating Temperature -40°C to 50°C (-40°F to 122°F)

Storage Temperature -40°C to 60°C (-40°F to 140°F)

Non-Operating Temperature -40°C to 60°C (-40°F to 140°F)

Operating Humidity Up to 85% @ 50°C (122°F) non-condensing

Non-Operating Humidity Up to 95% @ 50°C (122°F) non-condensing

Terminal Block Temperature Rating

Network

105°C (221°F)

All stations include integral LTE modem and will be automatically configured to operate as gateway or non-gateway as needed

ChargePoint, Inc. reserves the right to alter product offerings and specifications at any time without notice, and is not responsible for typographical or graphical errors that may appear in this document.

Contact Us Visit chargepoint.com

Call +1.408.705.1992

Email [email protected]

ChargePoint, Inc. Copyright © 2019 ChargePoint, Inc. All rights reserved. CHARGEPOINT is a U.S. registered trademark/service mark, and an EU 240 East Hacienda Avenue registered logo mark of ChargePoint, Inc. All other products or services mentioned are the trademarks, service marks, registered

trademarks or registered service marks of their respective owners. DS-CT4000-07. March 2019. PN 73-001020-03-1. Campbell, CA 95008-6617 USA

+1.408.841.4500 or +1.877.370.3802 US and Canada toll-free Listed by Underwriters

Laboratories Inc. chargepoint.com

Techinical Appendix DSM-18Demand Response Strategy Effectiveness Under Extreme Climate

Conditions

Prepared For:HDR

Prepared By:Isaac Mahderekal

ESSG – Energy Studies and Services Group

of 342

of 342

Techinical Appendix DSM-18Table of Contents

Abbreviations, Acronyms, & Terminology .......................................................................................................... 3

Purpose ................................................................................................................................................................. 4

Data Analysis Parameters & Approach............................................................................................................... 4

Weather Data implementation ............................................................................................................................. 4

Cooling Power (kW) As a Function of Outdoor Dry Bulb Temperature(F) .......................................................... 5 75°F cooling set point vs. 75°F cooling set point with 79°F set back temperature (1PM – 7PM) ..................... 5

Cooling Power (kW) & Ambient Temperature(F) as a Function of Time ............................................................. 8 75°F cooling set point vs. 75°F cooling set point with 79°F set back temperature (1PM – 7PM) ..................... 8

Cooling Power (kW) As a Function of Outdoor Dry Bulb Temperature(F): Extreme Weather Simulation (20XX) ........................................................................................................................................................................ 13

75°F cooling set point vs. 75°F cooling set point with 79°F set back temperature (1PM – 7PM) ................... 13

Cooling Power (kW) & Ambient Temperature(F) as a Function of Time: Extreme Weather Simulation (20XX) . 14 75°F cooling set point vs. 75°F cooling set point with 79°F set back temperature (1PM – 7PM) ................... 14

Conclusion .......................................................................................................................................................... 15

Comparing effectiveness of thermostat set back 2018 to 20XX (Extreme Weather Simulation) ..................... 15

ESSG - Demand Response Strategy Effectiveness Under Extreme Climate Conditions 2

of 342

Techinical Appendix DSM-18AAbbreviations, Acronyms, & Terminology

AC Air Conditioning CEMP Community Environmental Monitoring Program DRI Desert Research Institute kW Kilowatt kWh Kilowatt - hour NAN Not A Number DR Demand Response DRS Demand Response Strategy


of 342

Techinical Appendix DSM-18PPurpose This report examines the effectiveness of thermostat set back schedule in demand response strategies, more specifically during high temperature days. Furthermore, the report examines the feasibility of thermostat set back strategies in simulated EXTREME high temperature days.


o Las Vegas, NV Desert Research Institute (DRI) CEMP Weather Station EPW – Energy Plus Weather Data

Limitations & Simulation Disclosure o Simulated high temperature days are merely fictitious estimations and may not

necessary represent exact - expected temperature ranges in the near future. Thus, date markers have been specified as 20XX.

Weather Data implementation In order to accurately depict actual weather profiles (localized measured data) as opposed to averaged weather data (TMY – Typical Meteorological Year), local weather station data was used from 2018. DOE – Energy Plus software utilizes proprietary & specifically formatted weather data files (EPW – Energy Plus Weather Data). These files are the only accepted format within Energy Plus based applications and thus, intricate conversions needed to occur in order to convert localized (measured – not averaged) data into DOE – Energy Plus Weather files. The local weather data required data scrubbing for small interval inconsistences such as instrument noise and would need to be resampled at EPW accepted time stamps. Furthermore, empirical measurements within the localized data file were converted to metric as per the DOE EPW data requirements.

Finally, a DOE Energy Plus tool was utilized to create a TMY data file for Las Vegas (CSV of the EPW file), the data within this file would then be replaced by our weather station data and recompiled as an EPW type weather file to be used in any DOE Energy Plus application.

In order to simulate extreme – future weather conditions, offsets were formulaically added to our local weather data and recompiled for us in our DOE Energy Plus application.


of 342

CCooling Power (kW) As a Function of Outdoor Dry Bulb Temperature(F) Techinical Appendix DSM-18

75°F cooling set point vs. 75°F cooling set point with 79°F set back temperature (1PM – 7PM)

The Cooling Power (kW) As a Function of Outdoor Dry Bulb Temperature(F) depicts the relationship between the total cooling power experienced as a function of outdoor temperature, based upon a specific thermostat set point. Although these plots are informative as to the relationship between the buildings cooling load in relation to the outdoor dry bulb temperature, some of the values may be misleading. In these plots, there are instances where the Total Cooling (kw) at 75°F SetPoint & 79°F SetBack seem to be extremely efficient at higher and higher temperatures. This is not always the case. A majority of this high temperature to low power relationship for the Total Cooling (kw) at 75°F SetPoint & 79°F SetBack is due to the built up, cool thermal mass that is left over from when the building was at 75°F. As the building approaches it’s new set point of 79°F, the building’s cooling load will significantly drop until the building leaks all of the remaining cold thermal load and the thermostat experiences temperatures above 79°F. As such The Cooling Power (kW) & Ambient Temperature(F) as a Function of Time plots are able to tell a much clearer picture of how these varying set points affect the building’s cooling load a function of temperature over time.


of 342



of 342

CCooling Power (kW) & Ambient Temperature(F) as a Function of Time Techinical Appendix DSM-18


The Cooling Power (kW) & Ambient Temperature(F) as a Function of Time plots depict a much clearer picture of how these varying set points affect the building’s cooling load a function of temperature over time. The plots below are able to convey each setback’s relationship to outdoor temperatures as well as how remaining thermal loads over time can play a huge part in shifting the building’s load to off-peak hours, alongside the implemented thermostat setback.

Cooling Power (KW) By Set Point Over Time (On Peak Summer Schedule) 1PM 2PM 3PM 4PM 5PM 6PM 7PM

Dry Bulb Temperature °F 88.87 89.56 89.78 89.47 88.19 86.39 84.38 Power (kW) at 75°F 2.57 2.79 3.08 3.51 3.57 3.22 2.53 75°F w/ 79°F Set--Back 0.74 1.38 1.72 2.53 2.43 2.34 4.01 Delta (kw) Savings 1.83 1.41 1.36 0.98 1.14 0.88 1.48


of 342


C (1 2 3 4 5 6 7

DP7 S -B

D


Dry Bulb Temperature °F 105.76 107.51 108.59 109.57 109.84 108.40 105.53 Power (kW) at 75°F 4.80 5.71 6.26 7.12 7.78 7.18 6.17 75°F w/ 79°F Set-Back 1.96 3.61 4.21 5.47 6.15 5.84 8.36 Delta (kw) Savings 2.84 2.10 2.05 1.65 1.63 1.34 2.18


of 342


C (1 2 3 4 5 6 7

DP7 -BD




of 342


C (1 2 3 4 5 6 7

DP7 -B

D (




of 342


C (1 2 3 4 5 6 7

DP7 -BD




of 342

CCooling Power (kW) As a Function of Outdoor Dry Bulb Temperature(F): Techinical Appendix DSM-18

Extreme Weather Simulation (20XX) 75°F cooling set point vs. 75°F cooling set point with 79°F set back temperature (1PM – 7PM)


of 342

C

-

Cooling Power (kW) & Ambient Temperature(F) as a Function of Time: Extreme Weather Simulation (20XX)




Dry Bulb Temperature °F 122.44 124.16 125.32 127.30 128.12 128.12 126.44 Power (kW) at 75°F 8.98 10.02 10.74 11.46 11.33 11.32 11.32 75°F w/ 79°F Set-Back 9.18 5.30 7.64 9.04 10.34 10.82 10.13 Delta (kw) Savings 0.20 4.72 3.09 2.42 0.99 0.50 1.19 Delta (% kw) Savings 2.2% 47.1% 28.8% 21.1% 8.7% 4.4% 10.5%


of 342

Techinical Appendix DSM-18C

-

S B

-

Conclusion

Comparing effectiveness of thermostat set back 2018 to 20XX (Extreme Weather Simulation) August 2018



Delta (% kw) Savings 57.7% 38.4% 33.2% 23.4% 25.3% 21.5% 51.7%

July 2018 Cooling Power (KW) By Set Point Over Time (On Peak Summer Schedule)

1PM 2PM 3PM 4PM 5PM 6PM 7PM Dry Bulb Temperature °F 109.96 112.75 113.93 114.61 114.98 114.04 111.68 Power (kW) at 75°F 6.01 6.86 7.92 8.50 9.03 8.29 7.29 75°F w/ 79°F Set-Back 2.66 4.39 5.51 6.73 7.26 6.87 9.66 Delta (kw) Savings 3.36 2.47 2.41 1.77 1.77 1.42 2.37 Delta (% kw) Savings 55.9% 36.0% 30.4% 20.8% 19.6% 17.1% 32.5%

July 20XX – Extreme Climate Simulation Cooling Power (KW) By Set Point Over Time (On Peak Summer Schedule)

1PM 2PM 3PM 4PM 5PM 6PM 7PM Dry Bulb Temperature °F 122.44 124.16 125.32 127.30 128.12 128.12 126.44 Power (kW) at 75°F 8.98 10.02 10.74 11.46 11.33 11.32 11.32 75°F w/ 79°F Set-Back 9.18 5.30 7.64 9.04 10.34 10.82 10.13 Delta (kw) Savings 0.20 4.72 3.09 2.42 0.99 0.50 1.19 Delta (% kw) Savings 2.2% 47.1% 28.8% 21.1% 8.7% 4.4% 10.5%

In conclusion, as dry bulb ambient temperatures dramatically increase under extreme weather conditions, the effectiveness and thus the feasibility of thermostat setback is shown to diminish as the day continues and the initial thermal mass that was built up within the home at cooler temperatures is depleted. This is evident in the table above where the Delta (kW) savings drop significantly in the later half of On-Peak hours at which point most if not all cooler thermal load has been depleted and the building begins to work it’s way back to lower cooling set points.


of 342


VisionPRO® 8000 Smart Thermostat FOR RESIDENTIAL OR LIGHT COMMERCIAL APPLICATIONS

Job Name

Engineer

Mechanical Contractor

Contractor’s P.O. No.

Representative

Notes

APPLICATION VisionPRO® 8000 Smart Thermostat allows remote access through

a smartphone, tablet or computer. It controls up to 3H/2C heat

pump or dual fuel systems and up to 2H/2C conventional systems.

Thermostat is selectable for residential or light commercial

applications. The thermostat has a relay to control humidification,

humidification or ventilation.

The thermostat is equipped with a touchscreen display with a

2-line message center.

Provides remote access through smartphone, tablet or

computer when connected to Wi-Fi and registered to

mytotalconnectcomfort.com

Provides compressor lockout in heat pump system with aux

heat (electric or dual fuel). Provides lockout temperatures for

auxiliary heat in heat pump system with electric aux heat.

(Lockouts require a wired outdoor sensor or outdoor

information from the cloud with thermostat connected to

Wi-Fi and registered).

SPECIFICATIONS

Terminal Designations:

TH8321WF Thermostat: R, RC, S C, W-O/B, W2-AUX/E, Y, Y2, G,

A-L/A, K, U1 U1, S1 S1

Electrical Ratings:

Terminal Voltage

(50/60Hz)

Max. Current

Rating

W - O/B 18 to 30 VAC 1.00A

Y (cooling) 18 to 30 VAC 1.00A

G (fan) 18 to 30 VAC 0.50A

W2 – Aux/E 18 to 30 VAC 0.60A

Y2 (Cooling) 18 to 30 VAC 0.60A

A-LA (Output) 18 to 30 VAC 1.00A

U1, U1 18 to 30 VAC 0.50A

Power Consumption of TH8321WF Thermostat:

Backlight on: 2.35 VA

Backlight off: 1.40 VA

Wi-Fi Communication Requirements:

802.11 b/g/n routers

Android or IOS Smartphone, tablet or device

SUBMITTAL SHEET

Model(s)

TH8321WF1001 Qty. Notes

Approval

Service

Tag No.

Temperature Setting Range:

(Adjustable high limit for heat. Adjustable low limit for cool.)

Heating: 40 to 90 °F (4.5 to 32 °C)

Cooling: 50 to 99 °F (10 to 37 °C)

Temperature Sensor Accuracy:

± 1.5 °F at 70 °F (0.75 °C at 21.0 °C)

Humidification Setting Range: 10% to 60% RH

Dehumidification Setting Range: 40% to 80% RH

Humidity Display Range: 0% to 99%

Humidity Sensor Accuracy:

± 5% RH from 30% to 50% RH at 75 °F (24 °C)

Cool Indication:

Displays “Cool On” when the thermostat turns the cooling on.

Heat Indication:

Displays “Heat On” when the thermostat turns the heating on.

Auxiliary Heat Indication:

Displays “Aux Heat On” when the thermostat turns the auxiliary

heat on.

Interstage Differential:

Comfort: The thermostat keeps the indoor temperature within 1

degree of the setpoint (droop less control). The thermostat

turns on stage 2 when the capacity on stage 1 reaches 90%.

When the interstage differential is set to 1.0 or higher, the

thermostat stages the equipment based on how far the indoor

temperature is from the setpoint.

Economizer:

Economizer output and FDD economizer fault input.

Clock Accuracy:

If not connected to Wi-Fi: 1 minute per month at 77 °F (25 °C). ± 2

minutes per month over the operating ambient temperature

range.

If connected to Wi-Fi and registered to Total Connect Comfort: the

current time is synced via the Internet.

Mounting Means:

Thermostat mounts directly on the wall in the living space using

mounting screws and anchors provided. Fits a horizontal 2 x 4

in. junction box. Use a cover plate and its mounting bracket to

mount the thermostat onto a vertical 2 x 4 in. junction box.


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Operating Operating Relative

Product Part Number Shipping Temperature Physical Dimensions in Inches (mm) Color(s) Ambient Humidity

Temperature

32 to 120 °F 5% to 90% -20 to 120 °F 4-15/16 x 4-5/8 x 1-1/8 ArcticThermostat TH8321WF1001

White(0 to 48.9 °C) Non-Condensing (-28.9 to 48.9 °C) (126 x 118 x 29)

Dimensions of thermostat in in. (mm). Dimensions of VisionPRO cover plate in in. (mm).

By using this Resideo literature, you agree that Resideo will have no liability for any damages arising out of your use or modification to, the literature. You will defend and indemnify Resideo, its affiliates and

subsidiaries, from and against any liability, cost, or damages, including attorneys’ fees, arising out of, or resulting from, any modification to the literature by you.

Resideo Technologies, Inc.

1985 Douglas Drive North, Golden Valley, MN 55422

1-800-468-1502

www.resideo.com 33-00072-----03 M.S. Rev. 04-20 | Printed in United States

© 2020 Resideo Technologies, Inc. All rights reserved.

The Honeywell Home trademark is used under license from Honeywell International, Inc. This product is manufactured by Resideo Technologies, Inc. and its affiliates.

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Comfort Control for Your Building

Wi-Fi® Commercial Thermostat Part Number: 33CONNECTSTAT43, 33CONNECTSTAT


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This powerful, yet elegantly designed thermostat gives you total control of your

occupant’s comfort. With its ‘anytime from anywhere’ access through Connect’s

Smartphone mobile App, your PC or Tablet, precise real time adjustments are

fast and simple...24/7/365.

Easy to Install

• Connectors: Tool-free and color-coded.

• Direct Wi-Fi Access: Easily connect to your

building’s network.

• Smartphone App and Web Portal: Configure using

your internet-connected Smartphone, PC or Tablet

from anywhere or anytime…in real time.

• Web Portal: Allows you to access the Connect™

stat with any device connected to the web. Visit

connectstat.carrier.com and create an account.

• Humidity Sensor: Built in to help monitor and manage

occupant comfort.

• Compatibility: Supports up to 4 heating and 3 cooling

stages with various new or existing HVAC equipment

with 24 VAC control.

• Slim Profile: Blends easily into any building’s decor and

environmental surroundings.

Easy to Set-Up

• 7-Day Scheduling: Precisely adjust optimum comfort

setpoints to achieve energy savings.

• Demand Response Ready: For participation in local

demand response programs.

• Local Access Security: Provides selective access for

authorized individuals.

• Precision Setpoints: Achieve maximum energy savings

during occupied or unoccupied periods.

Easy to Use

• Color Capacitive Touchscreen: 4.3” / 2.8” vibrant full-color

LCD swipe and scroll screen that’s easy to navigate and

view from any angle.

• Over-the-Air Software Updates: Directly sent over Wi-Fi®

network without having to manually check for updates.

• Control: Make basic temperature, comfort and efficiency

adjustments easily and quickly.

• Alerts: Notification through SMS text and eMail of any

critical issues.

• Proximity Sensor: Detects your approach, lights up its

easy-to-see display.

• Color-Coded Display quickly and easily shows the

current operating modes of the occupied space.

• Embedded Contractor Info: Lets you know who to

contact for service.

With the Carrier® Connect™ Commercial Thermostat, your building’s temperature, ventilation,

humidity and indoor air quality can be controlled quickly, easily and in real time.


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Wi-Fi® Commercial Thermostat Part Number: 33CONNECTSTAT43, 33CONNECTSTAT

Specifications Physical Characteristics

Weight:

Power Requirements

Connectivity

Environmental Requirements

Humidity

Agency Approvals

Compliance

Electrical Characteristics

Accessories

33CONNECT43 Dimensions: (H x W x D): 3.23 in. (82mm) x 5.13 in. (130mm) x .94 in. (24mm)

33CONNECT Dimensions: (H x W x D): 2.524 in. (64mm) x 3.525 in. (90mm) x .83 in. (21mm)

Lead-free design

Approximately .75 lb (340g)

24V 50/60 Hz at 6va

Wi-Fi: 802.11 a/b/g/n

Temperature: Operating: 32 to 104 F (0 to 40 C) Storage: –40 to 135 F (–40 to 57 C)

10 to 95% RH (relative humidity), non-condensing

FCC Part 15, Subpart J compliant

Energy Star® Title 24 compliant IECC 2015 compliant

Outputs: Minimum Load = 1000 ohms Maximum Load = 1.25 A, 50% power factor

Remote space sensor (33ZCT55SPT)

Outdoor temperature sensor (33ZCSENOAT)

Duct temperature sensor (33ZCSENSAT)

For indoor installations only. Back plate, mounting and security screw, and remote sensor wiring harness included.

With the Carrier® Connect™ Commercial Thermostat, your building’s comfort control is simple and precise.

3.2500 (82.55mm)

Get the ’Carrier Connect Thermostat’ App at: 3.8455 (97.67mm)

2.5205 5.1250 (64.02mm)

(130.175mm) 0.6999 (17.77mm)

2.5919 (65.83mm)

2.4389 (61.95mm)

1.5719 (39.93mm)

3.2280 (81.99mm)

33CONNECTSTAT43

33CONNECTSTAT43 2.1665 (55.029mm)

5.125 (130.175mm)

3.525 0.563 2.301 (90mm) (14mm) (58mm)

33CONNECTSTAT43

(21m

m)

0.83

0 (24m

m)

0.94

0

3.525 (90mm)

(64m

m)

2.52

4

(29m

m)

1.15

8

33CONNECTSTAT

33CONNECTSTAT

33CONNECTSTAT For more information, contact your local Carrier Controls Expert.

© Carrier 2020. All Rights Reserved. Cat. No. 11-808-570-01 Rev. 10/20 Manufacturer reserves the right to discontinue, or change at any time, specifications or designs, without notice and Controls Expert Locator: without incurring obligations. Trademarks are properties of their respective companies and are hereby acknowledged. www.carrier.com/controls-experts

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Average Seasonal Demand Profiles, Demand Limiting Test Stations

PRIVATELINDELL 1 and 2 Stations

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NV ENERGY 01, 02, 03, 06 Stations

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SwarmStatTM Configuration Instructions

The intention of this document is to provide contractors with the instructions on how to manually configure each site thermostat to enable them to function as a SwarmStat™ system and enable Swarm Logic®. The steps below can be performed by any on-site HVAC technician or an electrician with minimum knowledge of HVAC operations. These instructions assume that the physical thermostat hardware has been installed at the site. For thermostat installation, please follow the manufacturer instructions.

Configuration Procedure

1) Notify Encycle Support, by calling 1-855-875-4031, Option 2, upon commencing thermostat configuration. Encycle Support will perform initial system setup in the Encycle Swarm PortalTM .

2) Perform the configuration steps as provided in the Product Data Document (PDM) (https://customer.resideo.com/resources/Techlit/TechLitDocuments/33-00000s/33-00096.pdf) paying particular attention to the Swarm Logic® related Setup Step #’s below and the associated instructions. The full list of installer options is on page 11 of the PDM

3) Connect all thermostats to the site’s Wi-Fi network. Ensure the network selected is not the Guest or Public network.

Setup # 101

Setup Name Application

Setting/Instruction Select COMMERCIAL

103 Thermostat Name (Optional) If applicable, select an appropriate name from the pre-defined options (Lobby, Office, Breakroom). This can help identify new devices as they come online

104 Thermostat Type Select PROGRAMMABLE to enable scheduling 106 Use Outdoor Temp Select WIRED / INTERNET 300 System Changeover Select AUTOMATIC 400 Schedule Periods Select 4 PERIODS PER DAY 403 Override Duration 3 hours maximum, 2 hours is recommended 410 Minimum Cool Setpoint 75F 410 Maximum Heat Setpoint 66F 411 Keypad Lockout Only set after connected to Wi-Fi. Set to Partially

Locked

4) Complete Site Information, Operating Schedule & Setpoints and Zone Information forms and send to [email protected] (forms can be found at the end of this document) a) Zone Information form must be completed for EACH thermostat

5) Call Encycle Support once the configuration is complete to confirm all devices are communicating

Encycle confidential and privileged information. Encycle Corporation 1850 Diamond Street, Suite 105 San Marcos, CA, USA 92078 1 855-875-40311 www.encycle.com

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Site Information

This form contains the key information required for Encycle to setup a site for Encycle Swarm Logic™. Complete all information. Retain a copy for you records and submit a copy to Encycle via email at [email protected]

Site Name

e.g. MyCompany Store #1234

Site Address

e.g. 500 Main St, Los Angeles, California

Contractor Company Name & Contact Information

Technician Name & Contact Information


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Operating Schedule & Setpoints If you have more than one schedule, make extra copies of this page as required. Fill with Occupied and Unoccupied periods. Please complete the table accordingly and email to [email protected]

The first column is an example.

Four Period

Period 1

Period 2

Period 3

Period 4

Example 6am – 8am 72F Occupied Fan ON 8am – 1pm 74F Occupied Fan ON 1pm – 6pm 73F Occupied Fan ON 6pm – 6am 71F Unoccupied Fan Auto

Mon Tue Wed Thu Fri Sat Sun

Apply schedule to which thermostat(s)? (all or specific units)


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Zone Information Please complete the table for each individual RTU. Print extra copies as needed for additional RTUs. All forms must be emailed to [email protected]

* indicates the field is REQUIRED for SwarmStat™ site setup. Other fields are crucial for verification and future energy analysis and should be gathered if available. In many cases these additional details can be obtained from the physical RTU nameplate, the model #, as well as any manufacturer documentation.

RTU Name / Number* Zone Name*

Manufacturer* ☐ Trane ☐ Carrier ☐ Aaon ☐ York ☐ Lennox ☐ Other ______________________

Model #* Tonnage

# of Cooling Stages* 1 2 ☐ 3 Compressors Per Stage ☐ 1 ☐ 2

Heating Type ☐ Heat Pump ☐ Natural Gas ☐ Electric ☐ None (cooling only)

Thermostat Model* ☐ Honeywell 8000 Thermostat Name*

MAC ID* CRC*

RTU Name / Number* Zone Name*

Manufacturer ☐ Trane ☐ Carrier ☐ Aaon ☐ York ☐ Lennox ☐ Other

Model # Tonnage*

# of Cooling Stages* 1 2 ☐ 3 Compressors Per Stage ☐ 1 ☐ 2

Heating Type ☐ Heat Pump ☐ Natural Gas ☐ Electric ☐ None (cooling only)

Thermostat Model* ☐ Honeywell 8000 Thermostat Name*

MAC ID* CRC*


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Encycle Customer Support Contact Information Support Hours of Operation: Regular Business Hours: Monday thru Friday 6am-5pm Pacific Time Non-Business Hours: Monday thru Friday 5pm-6am Pacific Time

Saturday and Sunday all day Company Holidays

Email Support:

Non-emergency issues may be reported by email submission to [email protected]. An automated reply will be provided to the sender acknowledging receipt of the email and a response will be provided during regular business hours.

Phone Support:

Issues may be reported by calling Encycle and selecting Option 2 from the automated attendant. During regular business hours, calls will be answered by a live Operations Support Technician. For all calls received during non-business hours, calls will be answered by a live on-call support technician. Non-emergency issues will be handled the following business day. An escalation chart is provided below and may be used as needed.

Operations Support Contact Listing Contact Name Business Hours Non-Business Hours

Encycle Support Main Line 855-875-4031 or 760-481-7878,

Select Option 2

855-875-4031 or 760-481-7878,

Select Option 2

Tim Hagler: Operations Support Manager

Ext 7850 626-638-8810

Katrina Nelson: Project Manager, New Installations.

Ext 7847 760-533-1429

Igor Marin: Field Operations Manager

Ext 7848 905-467-2131


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ChargePoint Load Profiles by Sectors

Normalized by a Single Charging Station

Hotel

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Included in Average:

Circus Circus Reno JW Marriott Las Vegas Resort and Spa MGM Grand Aria Hotel New York New York Hotel Mirage Hotel 8 Total Charging Stations

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School

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Time Period: 10/1/2019 – 9/30/2020

Included in average:

University of Nevada Las Vegas University of Nevada Reno College of Southern Nevada 17 Charging Stations

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EDUCATION / HEALTHCARE / LODGING / GOVERNMENT / OFFICE BUILDING / RETAIL / SPECIAL

AIRSIDE / APPLIED / CONTROLS / SERVICE / SPECIAL SOLUTION / TOTAL SYSTEM / UNITARY

Case Study – Sigler Brea Sales Office

Improving HVAC Efficiency

with Machine Learning

Annual Electric Bill Savings: $5,989

HVAC Electric Usage Reduction: 29%

Electric Consumption Reduction: 43,671 kWh

Average Peak HVAC Electric demand Reduction:

13%

Overall Building Electric Usage Reduction:

8%

Encycle’s Swarm Logic technology, synchronizes HVAC RTU operation on a real-time basis to lower electricity costs, maximize energy efficiency and maintain desired building comfort levels.

Connect™ Wi-Fi® Commercial Thermostats and Swarm Logic® Seamlessly Blend to Create a ‘Virtual’ Building Automation System

OBJECTIVES: For over 50 years, Russell Sigler Inc. has been an independent distributor of Carrier heating, ventilating and air-conditioning (HVAC) products, systems and controls solutions for multiple market segments throughout the southwestern United States. In 2010, in a joint venture with Carrier, Sigler acquired the California territory making them one of the largest HVAC distributors in the world.

Of the 20 sales, administration and warehousing facilities located throughout California, the management team at the Sigler Brea location determined that the 27,000ft2 sales and operations workspace was due for an upgrade to improve its overall efficiency and HVAC equipment performance. Their goals included lowering overall building energy usage, improving occupant comfort, monitoring and enhancing the performance of their existing rooftop units (RTUs), and replacing outdated thermostats. Well versed in offering comprehensive HVAC equipment, system and controls solutions, Sigler Brea also wanted to enhance the functionality, precision, monitoring and real-time management of their own RTUs.

SOLUTION: Sigler Brea elected to upgrade their existing, outdated thermostats with Carrier® Connect™ commercial thermostats to control their RTUs. With a myriad of operating features built in, the Connect thermostats were easy to install, set-up and use. To further enhance the functionality, monitoring and real-time management of their RTUs, Sigler Brea chose to interface their new thermostats with Encycle’s Swarm Logic® energy savings technology. This optional interface connects Sigler’s thermostats to a networked, cloud-based system. Here, they are dynamically synchronized to operate most efficiently in response to changing conditions such as outdoor temperature and building occupancy levels.

By integrating the precise control, sensing and data collection features built into the Carrier Connect commercial thermostats with Swarm Logic’s technology, Sigler Brea created a ‘virtual’ building automation system (BAS), and achieved a new level of performance, comfort, energy efficiency, reporting and HVAC management.


EDUCATION / HEALTHCARE / LODGING / GOVERNMENT / OFFICE BUILDING / RETAIL / SPECIAL

AIRSIDE / APPLIED / CONTROLS / SERVICE / SPECIAL SOLUTION / TOTAL SYSTEM / UNITARY

Case Study – Sigler Brea Sales Office

“Integrating the Carrier Connect

thermostats with Swarm Logic

has clearly given us improved

energy management, comfort

and real-time reporting.”

Anthony Bermudez, Area Controls Sales Manager,

Russell Sigler, Inc.

Project Summary

Location: Brea, California

SYNOPSIS: Achieving building automation system (BAS) benefits often presents unique technical and financial challenges for small- to medium-sized buildings. These can include:

• HVAC system designs that do not justify the investment required for a BAS • Customers that may not want or need additional BAS features • Up-front costs and multi-year payback scenarios can be a deterrent • System complexity may drive the need for full-time management

Such was the case recently facing management at the Brea, California sales office of Russell Sigler, Inc. They wanted to improve their RTU’s performance, increase their employee’s comfort, lower energy usage and also achieve an innovative level of HVAC ‘system’ management. Additionally, compliance with Title 24 — California’s energy code designed to reduce wasteful and unnecessary energy consumption in newly constructed and existing buildings — was a significant contributing factor.

To begin the process, Sigler drew from their expertise and selected Carrier® Connect™ Wi-Fi® commercial thermostats to replace their existing ones. The Connect thermostat’s many features include:

• Intuitive 2.8” touchscreen interface • Smartphone mobile app (iPhone and Android) and web portal • Title 24 compliance • Energy use monitoring • Ease of installation, set-up and usage

Once the new Connect thermostats were installed, Sigler Brea wanted to further enhance their functionality, monitoring and real-time management. To accomplish this, they would synchronize Sigler’s RTUs, transforming them into smart, networked, energy-responsive assets. “Sigler Brea had 13 rooftop units with individually connected thermostats, and wanted to have the enhanced control and energy management benefits of a traditional BAS, but without the associated complexity and at 10-20% of the cost.” said Chris Hensley, Executive Vice President of Sales and Marketing at Encycle. “Our cloud technology and proprietary and patented algorithms made this a perfect solution to meet all of Sigler Brea’s needs.

Now, at Sigler Brea, the successful integration of the Carrier Connect thermostats with Swarm Logic Virtual BAS® technology has created which dramatically improved their building’s operational — and subsequently — energy efficiency, by executing the following protocol:

• Carrier Connect thermostats collect data from Sigler Brea’s RTUs every few minutes • This collected data is then sent to Swarm Logic via the cloud • This interpreted data defines decisions to optimize each RTU’s operations and returns the decision to Sigler Brea’s

Connect thermostats • The Connect thermostats then adjust the RTUs in accordance with pre-set comfort and energy usage parameters within

Sigler Brea’s office space.

“We were excited to maximize our facility’s RTU’s efficiency by extending the full operational capabilities of the Carrier Connect thermostats with an energy management system,” said Anthony Bermudez, Area Controls Sales Manager of Russell Sigler, Inc. “The dashboard is intuitive, easy to use and provides us with a wide range of management tools and important operating data.” he continued.

As a result, Sigler Brea has realized up to 20% reductions in HVAC kW, kWh, and CO2, which helps improve their bottom line. “Access to

the portal is easy and secure,” commented Anthony Cerrato, Controls Sales Manager of Sigler Brea. “Our authorized personnel log in on a weekly basis to review summarized trending issues such as energy savings, demand response and RTU performance issues. Right after the initial integration of our Connect thermostats, we were able to identify RTUs which were running when they shouldn’t

Project Type: Controls retrofit and RTU optimization Major Decision Drivers: Ability to fully utilize the

Building Size: 27,000ft2

built-in control capabilities of Carrier® Connect™ Wi-

Facility Usage: Corporate branch sales office Fi® thermostats. Ease of integration with Encycle Swarm Logic®. Compliance of Title 24 through

Objectives: Improve HVAC RTU performance, OpenADR features of Connect thermostats.reduce utility costs, increase occupant comfort, access overall performance data, comply with Unique Features: Integrates Carrier Connect

California’s revised Title 24 relative to OpenADR. Commercial Thermostats with Swarm Logic

have been and re-configured them immediately,” he continued.

Controls: Carrier® Connect™ Wi-Fi® Commercial Thermostats

technology to maximize RTU performance, occupant comfort and energy savings.

Installation Date: 2017

For more information, contact your nearest Carrier Representative, call 1.800.CARRIER or visit our web site at carrier.com/commercial

© 2020 Carrier Corporation 11-808-801-01 of 342

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Encycle OpenADR Log 5/1/2020 A VEN ID was created in the HDR Test VTN for Encycle (Dave & Busters site)

A Kyrio VEN client test certifacte was created and sent to Encycle. TEST_RSA_VEN_200501202703_certs.zip

Thumbprint: 8b:53:d3:e9:6c:7b:62:5f:8c:3c:5c:08:16:26:07:e5:d2:80:dd:dc

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6/1/2020

A group called NPC_Encycle was created for the Encycle thermostat pilot at the D&B site.

A Market Context group called NPC_Ecycle was created for Encycle thermostat pilot in the NPC.

The first test event for the D&B site as created on 6/1 for 6/4 2:00 PM – 4:00 PM. A Simple signal of 0 (NORMAL) was created. The thermostats are not expected to curtail any HVAC during this test.

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6/2/2020

The following event cancellation test schedule was agreed on: 6/3 2:00 PM – 4:00 PM: Cancel event from VEN prior to event start time 6/3 4:00 PM – 6:00 PM: Cancel event from VEN during the event

6/4 2:00 PM – 4:00 PM: Cancel event from VTN prior to event start time 6/4 4:00 PM – 6:00 PM: Cancel event from VTN during the event

6/3/2020 2:00 PM – 4:00 PM: Cancel event from VEN prior to event start time


of 342

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6/3/2020 4:00 PM – 6:00 PM: Cancel event from VEN during the event

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6/4/2020 4:00 PM – 6:00 PM: Cancel event from VTN during the event

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6/3/2020

6/3/2020 2:00 PM – 4:00 PM: Cancel event from VEN prior to event start time 6/3/2020 1:30 PM – Encycle opted out at VEN, the opt-out was registered on the VTN.

Before:

After:

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6/3/2020 4:00 PM – 6:00 PM: Cancel event from VEN during the event 6/3/2020 4:30 PM – Encycle opted out at VEN, the opt-out was registered on the VTN.

Before

After:

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3:55 PM

4:00 PM

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6/4/2020

6/4/2020 2:00 PM – 4:00 PM: Cancel event from VTN prior to event start time 6/3/2020 1:30 PM – Encycle opted out at VEN, the opt-out was registered on the VTN.

Before:

After:

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6/4/2020 4:00 PM – 6:00 PM: Cancel event from VTN during the event 6/3/2020 4:30 PM – Encycle opted out at VEN, the opt-out was registered on the VTN.

Before:

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After:

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6/4/2020 Created an event

of 342


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8/5/2020 Encycle successfully polled the VTN using both ENC-001-DNB and ENC-002-DNB VEN IDs using shared certificates.

Created test events for Friday 8/7/2020

Event 1


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Event 2


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Event 3

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Events ending


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Techinical Appendix DSM-18Q1 Page:

Quote

Quote Expires: 12/31/5999 Order Number: 0445196

Order Date: 3/2/2020

566 Route 390 Tafton, PA 18464

Phone: 800-282-8864

Sold To: Encycle 1850 Diamond Street, suite 105 San Marcos, CA 92078 Confirm To:

Ship Complete Salesperson: HSE

Customer Number: 02-ENCYC01

Ship To: Encycle 1850 Diamond Street, suite 105 San Marcos, CA 92078

Customer P.O. Ship VIA F.O.B. Terms UPS GROUND Credit Card

Item Code Unit Ordered Shipped Back Order Price Amount

HONTH8321WF1001 EACH 1.00 0.00 0.00 139.89 139.89 ALL NEW VISIONPRO WI-FI THERMO Whse: 000

HONTHP9045A1098 EACH 1.00 0.00 0.00 21.30 21.30 WIRE SAVER MODULE Whse: 000

HONC7189U1005 EACH 1.00 0.00 0.00 25.01 25.01 WIRED INDOOR TEMPERATURE SENSO Whse: 000

Thank you for your business. George

Net Order: 186.20 Less Discount: 0.00

Freight: 0.00 Sales Tax: 0.00Discount amount may change subject to final order value.

Order Total: 186.20

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$65


SwarmStat Utility Program Pricing

Site Information EASE SaaS Fee Prepaid Annual EASE Thermostat Fee:

Years 1-10

SMB Program Customers 1 3 5 10

$65 $62 $60 $54

Annual Fee Per Thermostat $65 $191 $312 $595 LEGAL NOTICE: This document includes information that ENCYCLE considers proprietary.

No part of this document may be disclosed in any manner to a third party without the prior and express written consent of ENCYCLE.

Program Notes: Multiyear discount contingent upon prepaid, non-refundable payment

Pricing based on agreed volumes and program roll-out strategy Utility customer enrollment and Swarm Portal administration to be provided by Utility Partner

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Demand Response Strategies For a Renewable Future

Final Report

Prepared For:

NV Energy Demand Side Management Services

6226 W. Sahara Avenue Las Vegas, NV 89146

Prepared By:

6445 W Sunset Rd. Suite 166

Las Vegas, NV 89118 702-412-6014

January 2021

of 342

Techinical Appendix DSM-18Demand Response Strategies for Extreme Climate Conditions – Final Report 2020

Disclaimer

This report was prepared with reasonable care and in accordance with professional standards. However, neither Company nor any entity performing the work pursuant to Company’s authority make any warranty or representation, expressed or implied, regarding this report, the merchantability or fitness for a particular purpose of the results of the work, or any analyses, or conclusions contained in this report. The results reflected in the work are generally representative of operating conditions; however, the results in any other situation may vary depending upon particular operating conditions.

2 | P a g e H D R C o n s u l t i n g L L C

Confidential – For NV Energy internal use only

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Table of Contents

Contents

Table of Contents .................................................................................................................................... 3

List of Figures and Tables ..................................................................................................................... 3

Abbreviations and Acronyms ..................................................................................................................... 4

1. Executive Summary........................................................................................................................... 5

1.1 Overview ................................................................................................................................... 5

1.2 Summary of Major Findings ......................................................................................................... 5

2. Introduction...................................................................................................................................... 6

2.1 Background................................................................................................................................ 6

2.2 Project Objectives....................................................................................................................... 6

2.3 Approach ................................................................................................................................... 6

3. Renewable Future Study .................................................................................................................... 7

4. Nevada’s Renewable Energy............................................................................................................... 8

5. Demand Response Strategies ........................................................................................................... 12

6. Appendices..................................................................................................................................... 16

Appendix A: Renewable Future Detailed Data Report ............................................................................. 16

Appendix B: Clean Energy Programs July 2020 Update .......................................................................... 17

List of Figures Figure 1. California Duck Chart Projection .................................................................................................. 7

Figure 2. Spring Current Situation System Load Model ................................................................................ 8

Figure 3. Summer Current Situation System Load Model ............................................................................. 9

Figure 4. Spring System Load 50% Renewable Model ............................................................................... 10

Figure 5. Beyond 50% Renewable ........................................................................................................... 11

Figure 6. Clean Energy Programs Projection............................................................................................. 12

Figure 7. Overcooling Model ................................................................................................................... 14



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Abbreviations and Acronyms AC Air Conditioning CEMP Community Environmental Monitoring Program DR Demand Response DRI Desert Research Institute EV Electric Vehicle kW Kilowatt kWh Kilowatt - hour NAN Not A Number PV Photovoltaic RE Renewable Energy



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1. Executive Summary 1.1 Overview

Increase in renewable energy resources pose significant changes in the overall system load shape, with the potential overgeneration in the midday and steep ramping as the sun sets. This pattern commonly known as the duck curve has been observed in many other utilities, such as in California and Hawaii where large-scale solar photovoltaic (PV) systems have been deployed. With increasing penetration of PV in Nevada, the problems seen in other utilities may soon be happening to the NV Energy utility grid. To prepare for the imminent load shape change, NV Energy can implement demand response strategies to mitigate the effects of a renewable future.

1.2 Summary of Major Findings

1.2.1. Renewable Future Study The penetration of renewable energy, particularly solar PV in Nevada, poses a significant change in the utility system load shape. The change may cause renewable integration problems that includes: overgeneration risk when there is too much solar generation relative to load, steep ramping rates when there is too fast of a change in net load of renewable production, and intermittency due the unpredictability of weather conditions that significantly affects the renewable generation. Incorporating strategies that increases the flexibility of the power grid will be key to adequately balance and maintain grid reliability.

1.2.2. Nevada’s Renewable Energy Renewable generation capacity is continuing to grow with the steady interest in solar PV and its decreasing cost. Currently not as evident, it is observed that the Nevada load shape begins to form a duck curve, which will continue to become more imminent as more renewable energy is adopted in the coming years. By applying DR strategies early, however, the forming of the duck curve may be slowed down and have the utility be prepared for the upcoming threats it presents.

1.2.1. Demand Response Strategies DR strategies and technologies that provide flexibility to the grid were explored, such as smart appliances, thermal storage, grid interactive water heating, and EV charging stations. The overgeneration risk can be addressed by increasing the system load, where smart appliances, water heating, and EV charging stations, could be controlled to turn on and utilize the excess generation. These strategies and thermal storage may also help mitigate and soften the steep ramping rates by scheduling and shifting the peak demand. DR provides lower cost and effective measures of flexibility to control the intermittency of integrating renewable production to the grid.



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2. Introduction 2.1 Background

With the Nevada Senate bill 358 passed to require the state to generate 50% of its electricity from renewable resources by 2030, a renewable future is inevitable. Solar PV dominates the renewable portfolio and will continue to increase. Having a clean energy portfolio is an important investment while it is as critical to understand the changes it will have on the system load. As observed in other utilities already, the imminent duck curve will be seen in Nevada in the coming years with its increasing renewable energy generation.

2.2 Project Objectives The following were identified as objectives for this project assessment.

Understand effects of increasing renewable resources on the grid and identify potential risk and threats of increasing renewables on NV Energy territories.

Observe the current situation of NV Energy grid and model potential duck curve. Research and identify demand response strategies to mitigate the effects.

2.3 Approach The main approach for this desk study was to research other utilities in states, such as California and Hawaii, who have experienced high adoptions of renewable energy. The southern Nevada load was also modeled to show using the data listed below.

• Weather Data Parameters Las Vegas, NV

Desert Research Institute (DRI) CEMP Weather Station o Ambient Temperature

TMY3 - Typical Meteorological Year 3 o Ambient Temperature

• NV Energy System Load Data Extract Annual Load Profile Annual Generation Profile



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3. Renewable Future Study Over the past few years, Nevada’s neighboring state California has experienced a surge in solar PV in their utility grid. The California CAISO conducted a study back in 2012 to model the potential effects of the increase in renewables when the “duck chart” shown in the figure below. Utilities began to question the long-term sustainability of variable energy resources as the general load shape is to significantly change the traditional balance between load and demand. The risk of overgeneration and steep ramping observed in the chart became a huge concern and the utility began to plan for strategies to mitigate these issues.

Figure 1. California Duck Chart Projection

3.1.1 Increased Ramp Rate

The “neck” of the duck shows the steep ramping in energy demand as the sun begins to set. At this time, renewable solar sources stop generating, while several people return home from work and increase their energy consumption. Though not as steep, the ramping also occurs early in the morning as the sun rises and people get ready for the day. Flattening out the curve so less ramping is needed from traditional generation will be key in adapting to the new load shape the renewable future poses.

3.1.2 Overgeneration Risk

The “belly” of the duck is where the abundance of renewable generation may exceed that actual demand needed in the middle of the day. Overgeneration may most likely happen during a sunny spring or early summer morning when the temperature-driven loads are moderate, but the solar generation is high due to longer day lengths. In this case, to balance the renewable resource variation, the strategy would be to increase the overall load to utilize the excess solar. Some states have added negative pricing rates during this period to encourage customers to use more energy. Some states have also led to the curtailment of renewable energy systems, which leaves the utility questioning the sustainability of renewable resources. Flexibility of the power grid to provide balancing capability is substantial to maintain system reliability.

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3.1.3 Intermittency

Solar generation is highly variable as it is quite dependent on the sun and unpredictable weather conditions that lead to reliability concerns. On some cloudy days, solar panels may output only 10% -25% of their normal generation. Power plants have high startup and shutdown costs, so it is not favorable for them to fill in when variable generation lags. A combination of energy efficiency and demand response would offer a more cost-effective approach in balancing the grid on these types of conditions.

4. Nevada’s Renewable Energy 4.1.1 Current Situation

The daily load & solar generation plots depict the current load to generation ratios experienced in southern Nevada. The plots show the current solar generation capacity at about 14-16 % penetration. This leaves the remaining 84-86% generation up to other renewable resources and fossil fuels. Sampled spring months show steady load patterns due to cooler shoulder months with considerable solar generation from an increase in available irradiance during this season. The current solar penetration and load shape do not require an aggressive load shifting plan, although it is observed that the “neck” of the duck is starting to form between 6 and 7 PM.

Figure 2. Spring Current Situation System Load Model



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The summer months typically show a single sine wave load pattern resulting from cooler mornings that transition into much hotter afternoon and evening hours. The shape from renewable energy resources is not as apparent in summer months since the higher temperatures increase the overall load while the PV systems are not as efficient and generating as much energy at this time. Winter and fall months can be found in Appendix A.

Figure 3. Summer Current Situation System Load Model

4.1.2 2030 Projection – 50% Renewable

Year 2030 was modeled with 50% renewable penetration to observe the shape of the southern Nevada system load when the state reaches the mandated clean energy target. (Year 2025 with 25% renewable can be found in Appendix A.) The plots show the current solar generation capacity at about 50-52% penetration, leaving the remaining 48-50% generation up to other renewable resources and fossil fuels. The spring months still show steady load patterns due to cooler shoulder months with slightly more of a downward curve midday with the ample the solar generation. While not as apparent, 2030 slowly continues to show the forming of the duck curve. Other seasons can be found in Appendix A.



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Figure 4. Spring System Load 50% Renewable Model

4.1.3 Beyond 50% Renewable

Beyond the mandate, renewable energy will continue to grow, driven by the interest in clean energy to combat climate change, the customer desire to generate their own power, and the reducing costs of solar panels. Below is a simulation of the duck curve in southern Nevada that shows the rapid steep ramping as the sun sets and the potential overgeneration in spring. If not enough DR and load shifting are implemented early, the overall system load may have an exaggerated duck belly as seen on the chart.



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Figure 5. Beyond 50% Renewable

In the summertime with the highest peak demand, however, the belly of the duck is not as deep as seen in Figure 6 from the projected load shape by the Clean Energy Programs from Appendix B. Much of the cooling load is being offset by the solar generation at this point, and the highest peaks are now projected to occur later in the day between 7 PM and 10 PM. Several customers are regularly home in these hours, so different DR strategies that are flexible were studied in the next section.



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Figure 6. Clean Energy Programs Projection

5. Demand Response Strategies 5.1.1 Smart Appliances

A significant increase in smart appliances has been contributing to realizing the full potential benefits of the Smart Grid which includes a wide array of communicating devices to enable customers and utilities to better manage energy use. Several appliance manufacturers are now incorporating DR functionality and beginning to release DR-ready products into the market. Appliances such as HVAC controlled by smart thermostats, dishwashers, clothes washers, refrigerators, pool pump, water heater, and lighting, provide value in flexibility of controlling the load on the utility grid at any given time.

Currently, the smart thermostat DR program in NV Energy focus on shedding the demand between the system peak around 3 PM and 7 PM. With the increasing renewable energy, smart thermostats may help mitigate the overgeneration risk and steep ramping by extending or shifting the program. The snapback from the decrease in four degrees contributes in the late peak time as the sun begins to set. There is potential in the future to shift the DR event time earlier to help smoothen the “neck” of the duck or ramping.

Other appliances are not yet as widespread in current DR programs but are beginning to be tested and incorporated. With refrigerators virtually in every residence and operating 24 hours every day, they are good candidates to control during peak load periods. Since they are quite efficient and need to maintain safe temperatures however, the level of control may be limited. Clothes washers and dishwashers may also be better candidates for DR since they can be interrupted without posing safety concerns as well as having higher resistance heater loads that operate in wash cycles.



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Another appliance and a form of thermal storage that can be effectively implemented as a DR strategy is a grid-interactive electric water heater. The thermal energy storage properties of a water tank works similar to a battery, and the grid interactive technology adds bi-directional control to allow the utility to rapidly and repeatedly turn on and off, or slowly ramp their power up and down. This type of control provides flexibility to help mitigate the overgeneration risk and steep ramping, as the water heater can increase or decreasing the load. In addition, grid interactive water heaters are currently the least expensive form of energy storage available.

Several homes that have swimming pools may also begin to incorporate their pool pump with their solar PV system when there is excess. Typical load profiles show the peak time the solar generation is right around the highest peak of a pool pump as well. Having the pool pump consume the excess generation at this time provides value in reducing the overgeneration risk that might happen in the spring or summer time.

Lighting controls have also been increasingly being implemented in new building requirements as lighting load has been recognized to be the second highest energy consumer next to HVAC load. With the more widespread advanced controls for lighting, DR can be implemented in commercial buildings especially, to schedule their lighting so that it is reduced during peak loads or be increased when there is excess PV. A few percentage change in the lighting output can go unnoticed by the human eye but the decrease in load especially in a large building provide great flexibility to the grid.

5.1.2 Building Overcooling with Thermal Storage

Thermal storage in the form of a tight or adequately sealed building can offer the ability to pre-condition a space in anticipation for an expected cooling/heating condition. A pre-conditioned (over cooled/heated) space can offer peak shifting and peak shaving benefits by using excess power from distributed generation or available utility power in order to better manage an incoming cooling/heating cycle.

An example can be seen below in Figure 8, a building profile of 2,160 sq-ft property with a SEER 16 Central AC system and a 4 kW solar PV System. The plot depicts the possible off solar demand shift that can be achieved with over cooling a residential building during the hours of 1PM – 5PM utilizing excess PV generation to reduce the cooling demand during typical evening home occupation hours. Note, utility power can also be utilized to in place of an onsite disturbed generation source.



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Figure 7. Overcooling Model

In the modeled plot above, the overcooled home utilizes the excess PV generation and also results in a cooling demand that is less than a base case where overcooling is not applied when solar generation has ceased for the day. This scenario utilizes a PV system that is sized to adequately perform the demand shift during one of the harshest cooling months such as July. During less intense cooling months, overcooling can be combined with other self-consumption practices to maximize the potential excess PV that is generated.

5.1.3 Electric Vehicles and Charging Stations

There has been a significant growth in the electric vehicle (EV) market over the past 8 years. Since 2012, the number of EVs on the road has reached over 1 million, with the next 1 million EVs expected to be sold in the next 3 years. It is estimated that 18.7 million EVs will be on the road by 2030, making up 7% of the expected 259 million vehicles (cars and light trucks) that will be on U.S. roads that year [4]. The average Nevada driver will drive about 12,869 miles/year that equates to approximately 35 miles/day. Assuming drivers maintain a 20% deviation from the average, an average EV owner could potentially have about 5kWh-80kWh of capacity that can be utilized for a demand response program to help flatten the duck curve. Commercial buildings that maintain a fleet of electrical vehicles (such as government agencies) or house customer vehicles for a prolonged period of time can also be utilized to assist in the mitigation of low net loads and peak loads at desired intervals. The potential load shifting capability for EVs are shown in the table below.



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Non-Commercial Electrical Vehicle Load Shifting Capability

Year Conservative Estimated Number of EVs on the

Street

Avg. Load Shifting Capability (kWh) per

Day After Daily Commute

(~25 kWh/vehicle)

Equivalent Number ofTesla Powerwall 2

Units (~13 kWh/unit)

2019 3,549 88,725 kWh 6,825 Units

2025 6,975 174,375 kWh 13,413 Units

2030 9,830 245,750 kWh 18,903 Units

2040 15,540 388,500 kWh 29,884 Units High Estimate

2040 50,000 1,250,000 kWh 96,153 Units

The overgeneration risk may be complicated since the solution would be to increase the overall load, which typically is the opposite of what DR and EE programs are trying to achieve, but EV charging stations would be able provide this flexibility needed to increase the load. The charging stations can be scheduled to charge the already plugged-in vehicles to output at maximum power when there is excess solar generation. Large EV stations where customers typically park their cars for long durations would prove highly valuable in these situations as they provide flexibility on when the charger would pull power from the grid.

On the other hand, vehicle-to-grid (V2G) technology via a bi-directional inverter can help in mitigating the steep ramps and regulate energy on the grid. The EV’s would practically act as battery storage that can be charged, or discharged in this case, to balance the energy consumption towards the hours when the sun is setting and the ramp is steep. EV charging stations would be valuable in adding to the demand response program for the mitigation of both overgeneration and steep ramping.



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6. Appendices Appendix A: Renewable Future Detailed Data Report



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Appendix B: Clean Energy Programs July 2020 Update



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Demand Response Strategies for Extreme Climate

Prepared For:

Conditions

Final Report

NV Energy Demand Side Management Services

January 2021

6226 W. Sahara Avenue Las Vegas, NV 89146

Prepared By:

6445 W Sunset Rd. Suite 166

Las Vegas, NV 89118 702-412-6014

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Techinical Appendix DSM-18Advanced Demand Response Strategies for Extreme Climate Conditions – Final Report



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Disclaimer

This report was prepared with reasonable care and in accordance with professional standards. However, neither Company nor any entity performing the work pursuant to Company’s authority make any warranty or representation, expressed or implied, regarding this report, the merchantability or fitness for a particular purpose of the results of the work, or any analyses, or conclusions contained in this report. The results reflected in the work are generally representative of operating conditions; however, the results in any other situation may vary depending upon particular operating conditions.



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Table of Contents

Contents

Table of Contents.................................................................................................................................................................... 4

List of Figures .......................................................................................................................................................................... 4

Abbreviations and Acronyms.................................................................................................................................................. 5

1. Executive Summary ......................................................................................................................................................... 6

1.1 Overview .................................................................................................................................................................... 6

1.2 Summary of Major Findings ....................................................................................................................................... 6

2. Introduction .................................................................................................................................................................... 7

2.1 Background ................................................................................................................................................................ 7

2.2 Project Objectives ...................................................................................................................................................... 7

2.3 Approach.................................................................................................................................................................... 7

3. Weather Trend Study ...................................................................................................................................................... 8

4. Building Load and Ambient Temperature Trends......................................................................................................... 12

5. Demand Response Strategies ....................................................................................................................................... 15

6. Appendices .................................................................................................................................................................... 20

Appendix A – Extreme Climate Detailed Data Report ...................................................................................................... 20

Appendix B – Current DR Strategy Effectiveness.............................................................................................................. 21

List of Figures Figure 1. Ambient Temperature vs. Avg kW Factor................................................................................................................ 6 Figure 2. Highest Temperatures Recorded in Las Vegas......................................................................................................... 8 Figure 3. Number of Hours with >110°F ................................................................................................................................. 9 Figure 4. Wholesale Electricity Price Hub Locations............................................................................................................... 9 Figure 5. 2020 Max Temperature and Wtd. Price/MWh ...................................................................................................... 10 Figure 6. Thermostat Setback in Typical High vs. Extreme Temperatures ........................................................................... 10 Figure 7. Cooling Power vs. Temperature for Typical High vs. Extreme Temperatures .......................................................11 Figure 8. Residential Sample Daily Demand vs. Temperature.............................................................................................. 13 Figure 9. Hotel Sample Daily Demand vs. Temperature....................................................................................................... 14 Figure 10. School Sample Daily Demand vs. Temperature ................................................................................................... 15 Figure 11. Overcooling Model............................................................................................................................................... 17



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Abbreviations and Acronyms

AC Air Conditioning CEMP Community Environmental Monitoring Program DRI Desert Research Institute kW Kilowatt kWh Kilowatt - hour NAN Not A Number STC Standard Test Condition(s) CdTe Cadmium telluride - Photovoltaic technology EV Electrical Vehicle TCL Thermostatically Control Load TES Thermal Energy Storage CWS Chilled Water Storage COP Coefficient Of Performance DR Demand Response DRS Demand Response Strategy



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1. Executive Summary 1.1 Overview

In recent years, the effects of extreme climate conditions to the electric grid system have become more apparent. While the NV Energy northern and southern territories do not encounter natural disasters such as tornados, hurricanes, or ice storms, the territories experiences climate threats relating to increased frequency, intensity, and duration of heat waves, especially in Southern Nevada. With the utility peak demand typically occurring during the hottest hours of the day, driven by the cooling load of residential and commercial buildings, it is important to increase the utility’s flexibility in shedding or shifting its peak using advanced demand response strategies and technologies. In this report, the weather trends in the southern territory will be analyzed, observe how extreme climate affects a building load, and recommend DR strategies suited for extreme heat waves.

1.2 Summary of Major Findings Sampled weather data between in the last five to ten years have shown a general upward trend in the high temperature hours. While high temperature peaks do not show consistent trends, prolonged hot periods have been increasing throughout the years. With the prolonged and high temperatures, the effectiveness of the current thermostat program of a 4°F setback was observed and modeled with extreme temperature bins over 115°F through 130°F. As shown in the Figure 1 below, there is a point when the effectiveness of the thermostat setback will not provide as much kW delta as it is able to offset currently if the ambient temperature is hot enough, setting off the HVAC system regardless even with a 4°F lower setpoint.

Figure 1. Ambient Temperature vs. Avg kW Factor

The diminishing returns from the current thermostat DR program calls to action to develop more advanced and more flexible DR strategies that would help offset load from the grid. Some strategies outlined are building overcooling strategies, thermal storage, battery storage with electric vehicles, as well as incorporating smart appliances.



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2. Introduction 2.1 Background

Over the past few years, nearly two-thirds of the country have experienced extreme heat waves, and the electric grid demand has soared. High temperatures can affect the electrical grid infrastructure and cause outages. Unlike the heating load, basically all the cooling load relies on the electrical grid alone. With higher temperatures, the demand increases, and to meet the demands for peak electricity, additional generation and distribution will be needed, or at a relatively lower cost, advanced demand strategies will have to be implemented.

2.2 Project Objectives The following were identified as objectives through desk study, modeling, and analysis of existing loads:

Weather trends and effects of extreme heat conditions Building load and ambient temperature trends Demand response strategies to mitigate increased load

2.3 Approach To support the desk study, an analysis of the weather data using the parameters below was conducted to identify any extreme climate trends. The wholesale electricity pricing during the hottest days and other utilities were also studied to explore different DR strategies available.

Ambient Temperature in Las Vegas, NV o Desert Research Institute (DRI) CEMP Weather Station o TMY3 - Typical Meteorological Year 3

NV Energy Interval Meter Data Extract o Building location o Building demand meter measurements o Building calculated meter energy

ATTOM Residential & Commercial Real Estate Data Extract o Building location o Building Area o Conditioned Area o Total Area o Building HVAC Equipment Listings o Building Purpose/Type



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3. Weather Trend Study

Figure 2. Highest Temperatures Recorded in Las Vegas

In the maximum temperatures recorded in Las Vegas shown in Figure 2 above, there is no clear pattern of the high temperatures. However, in recent years, the maximum has been reached more often and a general upward trend appears to be forming. In addition, the unpredictability of the temperatures makes it important for utilities to be prepared for extreme cases.

3.1.1 High Temperature Trends

Temperature data hours across 10 years in Las Vegas, Nevada, were analyzed to identify high temperature trends from year to year. These trends were identified by sampling measured high temperature points from 90°F up to and beyond 110°F at increments of 5°F bins. It has been observed that the territory has experienced an upsurge in high ambient temperature run hours between 2009 -2019. For temperatures above 110°F, from 2015 and 2018, there was a 107% increase in run hours from 27 hrs. to 56 hrs. From 2015 and 2017, there was a 233% increase in run hours at 110°F from 27 hrs. to 90 hrs. It is observed that the number of hours for prolonged high temperatures is generally in an upward trend. Additional temperature ranges are in Appendix A.

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Figure 3. Number of Hours with >110°F

3.1.2 Wholesale Electricity Pricing during Peak Temperatures

Figure 4. Wholesale Electricity Price Hub Locations

Along with the stress on the utility grid high temperatures would cause, the wholesale electricity price is also affected, especially with prolonged days of hot temperatures. As observed earlier this summer, the month of August consistently had extremely high temperature for more than 7 consecutive days, shown in the heat map below. Following these high temperatures, the average wholesale electricity price spiked significantly in the following days. Part of the utility’s strategy was to encourage customers to generally



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Figure 5. 2020 Max Temperature and Wtd. Price/MWh

highest consumers during the summer season. It is able to delay or trickle down the load from customers' HVAC systems with the higher setpoints.

To observe the effectiveness of this method in extreme high temperatures of greater than 120°F ambient temperature, a home was modeled to participate in the current thermostat setback program with temperatures higher than 120°F to show an extreme case. The graphs below show the lower temperature bins on the left side that is typically seen and the extreme high temperatures on the right side. Immediately observed in the plots, the kW delta is not as great with the extreme temperatures since the HVAC is working hard to keep up with the temperature outside. In the beginning of the event, it may delay the turning on the HVAC system, but the system cannot keep up with the outdoor heat gain.

Advanced Demand Response Strategies for Extreme Climate Conditions – Final Report

use less electricity during those days, further emphasizing the importance of having various and flexible advanced demand response strategies during these high peaks.

3.1.3 Current DR Program in Extreme High Temperatures

The existing NV Energy program currently calls for 4°F thermostat setbacks during high temperature days between the hours of the system peak load. This method is quite effective as HVAC loads are the

Figure 6. Thermostat Setback in Typical High vs. Extreme Temperatures



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Figure 7. Cooling Power vs. Temperature for Typical High vs. Extreme Temperatures

3.1.4 Impact of High Ambient Temperatures on Renewable Energy Systems

The utility grid is increasingly evolving with more renewable systems integrated, and it is valuable to understand the effects of extreme temperatures on these solar PV, inverters, and battery storage systems. Photovoltaic (PV) modules are factory rated at standard test condition (STC). These STCs usually consist of parameters such as, an irradiance of 1000 W/m2, temperature at 25°C and solar spectrum of Air Mass 1.5G. The actual output from the PV module in the field varies from its rated output due to changes in ambient environmental conditions from the STC. The reduction in output due to temperature is determined by a temperature coefficient which can vary based upon different types of solar module technologies. In a study performed for Int. Journal of Engineering Research and Applications, the results showed that the average temperature coefficient of power for mono-crystalline, multi-crystalline and CdTe based photovoltaic modules are -0.446 %/°C, -0.387 %/°C and -0.172 %/°C respectively. Based upon these findings the average temperature coefficient of power for mono-crystalline, multi-crystalline and CdTe based photovoltaic modules would be -0.446 %/ 1.8°F, -0.387 %/1.8°F and -0.172 %/ 1.8°F respectively for every 1.8°F increase above 77°F. Based upon results from the high temperature weather data analysis, the following table depicts the minimum degradation in performance based upon the specified high ambient temperature.



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Change in Performance %

Temperature (°F) Mono-Crystalline Multi-Crystalline CdTe

(-0.446%/1.8°F) (-0.387%/1.8°F) (-0.172%/1.8°F)

90 -3.22 -2.80 -1.24

95 -4.46 -3.87 -1.72

100 -5.70 -4.95 -2.20

105 -6.94 -6.02 -2.68

110 -8.18 -7.10 -3.15

115 -9.42 -8.17 -3.63

120 -10.65 -9.25 -4.11

125 -11.89 -10.32 -4.59

130 -13.13 -11.40 -5.06

Photovoltaic inverters on the other hand deal with larger issues than degradation of performance at high ambient temperatures. Photovoltaic inverters that are exposed to extreme (high) ambient weather conditions have increased odds of operational failure over time. According to a study by the National Renewable Energy Laboratories (NREL), high ambient temperatures combined with inverter heat sink design, specifications, and other weather conditions such as wind can be used to identify possible points of failure with specific field deployed inverters.

Similar to energy production, energy storage is also subject to degradation in overall performance as a result of increased ambient temperature conditions. In a study performed for the Journal of Power Sources, laboratory-size lithium-ion pouch cells were cycled over 100% depth of discharge (DOD) at room temperature 25°C (77°F) and 60°C (140°F) in order to investigate high-temperature degradation mechanisms of capacity fading for individual battery cell components. The high-temperature cell lost 65% of its initial capacity after 140 cycles at 60°C (140°F) compared to only a 4% loss for the cell cycled at room temperature.

4. Building Load and Ambient Temperature Trends A random sample of residential and commercial buildings having the highest summer electric consumption were analyzed. The graphs described below show the apparent trend that demand generally increase with higher temperatures. Additional sites and time ranges were observed and can be found in the Appendix A for further details. The Daily Demand and Ambient Temperature Graphs depict a single



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Figure 8. Residential Sample Daily Demand vs. Temperature

Commercial, Customer Sample – Hotel

One of the biggest consumers of electricity in the NV Energy territory are the hotels. Compared to the residential load shape, the upward trend of peak demand as a function of peak ambient temperature can be seen more clearly in a commercial building. With larger buildings typically having greater demand as well, the participation of commercial customers in demand response programs is essential as they may offer more reduction in demand during events.


building’s recorded hourly peak demand and peak isolated HVAC demand alongside the recorded ambient temperature for each hour of a specified day of the year.

Residential Customer Sample

Typically, the hottest month of the year, July is shown below with the building demand shown as a function of peak ambient temperature. While the pattern is not steep, it can be observed that the hotter and higher the temperature is, the more power the building load needs. This was generally seen in many other residential homes.



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Figure 9. Hotel Sample Daily Demand vs. Temperature

Commercial, Sample Customer – Public School

Other types of commercial customers are also big consumers of electricity, and it is important to note that some load profiles, such as the public school below, do not always heavily depend on ambient temperature for their system load. When choosing demand response participants, looking at the building load shape could help determine how much reduction the building may be able to contribute during extreme climate events.



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Figure 10. School Sample Daily Demand vs. Temperature

5. Demand Response Strategies Extreme heat wave conditions will inevitably result in significant increase in load over long periods of time. The prolonged stress on the utility grid may cause outages and equipment damage, therefore, addressing the increase in load by implementing strategies to reduce the peak demand is vital. A few demand reduction strategies and technologies are described below.

5.1 Thermostat Controlled Loads

Thermostatically controlled loads (TCLs), devices such as water heaters and air conditioners, can be controlled to provide ancillary services by assisting in balancing generation and load. By adding simple imbedded instructions and a small amount of memory to temperature controllers of TCLs, it is possible to design open-loop control algorithms capable of creating short-term pulses for demand response strategies. Meanwhile, avoiding temporary synchronization of the TCLs following the completion of a demand response activity. Such demand response activities could include but are not limited to, delayed thermostat call for cooling, over cooling activities as well as adjusted cooling set point parameters. The end goal of these cooling demand response conditions is to reduce the on cycle of a building’s AC compressor. However, AC compressors mitigating response strategies that are implemented at the same time can result in a synchronized-on cycle between multiple compressors within a single neighborhood. This can result in oscillations in demand that arise before and/or after a peak demand schedule depending



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on weather a pre- or post-peak demand activity occurred. A pre activity being over cooling while a post activity being some form of a thermostat set back which occurs during peak load hours.

Peak demand load management within a neighborhood and/or substation level can be properly managed in order to avoid pre/post demand response activity synchronization of loads. These oscillations in demand can lead to further peak demand occurrences as building compressors synchronize across neighborhoods and substations. The decoupling and desynchronization of building peak loads can be attained through a decentralized ensemble of communicating smart TCLs. The TCLs can work together in order to identify the necessary delays that will result in the most ideal peak demand shift/shave while avoiding load synchronization as a byproduct.

5.2 Building Overcooling Strategies

Thermal storage in the form of a tight and/or adequately sealed building can offer the ability to pre-condition a space in anticipation for an expected cooling/heating condition. A pre-conditioned (over cooled/heated) space can offer peak shifting and/or peak shaving benefits by using excess power from distributed generation and/or available utility power to better manage an incoming cooling/heating cycle.

An example can be seen below. A plot depicts the possible off solar demand shift that can be achieved with over cooling a residential building during the hours of 1PM – 5PM utilizing excess PV generation in order to reduce the cooling demand during typical evening home occupation hours. Note, utility power can also be utilized to in place of an onsite disturbed generation source. Based on the plot below, the over cooled home on excess PV generation results in a cooling demand that is less than a base case where over cooling is not applied, and solar generation has seized for the day.

The current scenario utilizes a PV system that is sized to adequately perform the demand shift during one of the harshest cooling months (July). During less intense cooling months, over cooling can be combined with other self-consumption practices to maximize the potential excess PV that is generated.

The building profile is: 2,160 sq-ft property with a SEER 16 Central AC & a 4kW PV system.



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Figure 11. Overcooling Model

5.3 Thermal Storage Demand Response Strategies

The principal idea in using thermal energy storage (TES) is shifting the electricity peak load associated with buildings' cooling from peak time to off-peak periods. In general, TES is considered by utilities as demand management strategy, which is suitable for specific applications. The concept of thermal energy storage has been employed long ago for solar energy applications; recently with the vast increase of A/C energy demand, cold TES technology appeared to provide a feasible solution for solving peak load problems. In hot climate areas where reliance on air conditioning increases, the maximum cooling loads of buildings occur during midday period. At the same time the performance of generating units, especially gas turbine plants, drops because of the high inlet air temperature to the compressor. Though use of cold storage seems to be a promising technology, its implementation depends on the variation of the daily cooling load. The latter depends on the features of the building and occupants’ activities. Thermal storage systems for cooling application are generally categorized in three types, which are chilled water, ice storage and eutectic salt TES systems. Between these techniques, the Chilled Water Storage (CWS) and the Ice Thermal Storage (ITS) systems are the most promising ones in case of the normal applications. Table 1 shows some of the main differences between these three cool storage systems.

Specific heat

Latent heat of fusion

kJ/kg K

kJ/kg

Chilled water

4.19

Ice storage

2.04

334

Eutectic salt

80-250

Chiller cost $/kW 57-85 57-142 57-85



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Tank Volume cu. m/kWh 0.089-0.17 0.019-0.02 0.048

Storage installed cost

Charging temperature

Charging efficiency

$/kWh

deg. F

COP

8.5-28

39-43

4.0-6.0

14-20

21-27

2.7-4.0

28-43

39-43

5.0-6.0

Discharge temperature deg. F 34-39 34-37 48-50

Based upon the desired BTU shift that would need to be conducted on a specified residential or commercial building, an appropriately sized thermal storage system could assist in shaving, and or completely shifting otherwise expensive peak leads to off peak hours in order to reduce the overall stress on the grid during peak cooling hours of an extreme climate condition day.

5.4 Electric Vehicle Bi-Directional Charging

There has been a significant growth in the electric vehicle (EV) market over the past 8 years. Since 2012, the number of EVs on the road has reached over 1 million, with the next 1 million EVs expected to be sold in the next 3 years. Furthermore, it is estimated that 18.7 million EVs will be on the road by 2030, making up 7% of the expected 259 million vehicles (cars and light trucks) that will be on U.S. roads that year.

Electrical vehicle battery capacities can range between 17.6 kWh in compact, low range (58 mile) vehicles to 100 kWh sports cars and SUVS with increased range (up to 300 mile) and performance capabilities. The average Nevada driver will drive about 12,869 miles/year that equates to approximately 35 miles/day. Assuming drivers maintain a 20% deviation from the average, an average EV owner could potentially have about 5kWh – 80kWh of capacity that can be utilized to respond to extreme weather condition induced peak demands in the afternoon using a bi-directional inverter.

Alternatively, commercial buildings that maintain a fleet of electrical vehicles (such as government agencies) or house customer vehicles for a prolonged period of time can utilize the parked electrical vehicles to assist in the mitigation of peak loads at desired intervals and then continuing to charge their vehicles once the peak shifting routine is complete. Vehicle-to-grid (V2G) technology via a bi-directional inverter could also help bridge the gap between time of power supply and time of demand while helping regulate energy on the grid.

Given the significant growth of electrical vehicles in United Sates as well the globe, a similar trend is to be expected of second-hand electrical vehicles and their parts. The average electrical vehicle’s life span is estimated to be between 15-17 years at ~12,000 miles/year. Due to the rapid rise of EVs in recent years and even faster expected growth over the next ten years in some scenarios, the second-life-battery supply for stationary applications could exceed 200 gigawatt-hours per year by 2030. This volume will



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exceed the demand for lithium-ion utility-scale storage for low- and high-cycle applications combined, which by 2030 will constitute a market with global value north of $30 billion.




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6. Appendices Appendix A – Extreme Climate Detailed Data Report



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Appendix B – Current DR Strategy Effectiveness



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......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

39

NPC

6/11

/201

9 –

7/19

/201

9 ....

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

.... 3

9

NPC

7/22

/201

9 –

8/15

/201

9 ....

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

.... 4

0

NPC

8/20

/201

9 –

9/4/

2019

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

.... 4

1

Appe

ndix

D –

SPPC

201

9 DR

par

ticip

atio

n su

mm

ary .

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

.... 4

2

SPPC

6/1

1/20

19 –

7/1

8/20

19 ..

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

.... 4

2

SPPC

7/1

9/20

19 –

8/1

4/20

19 ..

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

.... 4

3

SPPC

8/1

5/20

19 –

9/3

/201

9 ...

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

.....

44

Appe

ndix

E –

eco+

Pop

ulat

ion

Data

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

. 45

NPC

2020

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

.. 45

SPPC

202

0 ...

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

... 4

7

Appe

ndix

F –

Occ

upan

cy S

ensin

g Ca

pabi

lity

Data

.....

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

.... 4

8 1

of 342

Techinical Appendix DSM-18Ap

pend

ix G

– S

mar

t Met

er D

ata

Anal

ysis

Sum

mar

y ....

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

.. 49

NPC

2020

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

.. 49

NPC

2019

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

.. 50

SPPC

202

0 ...

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

... 5

1

SPPC

201

9 ...

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

......

... 5

2 2


of 342

Appe

ndix

A –

NPC

202

0 DR

par

ticip

atio

n su

mm

ary

NPC

6/3

/202

0 –

7/10

/202

0

3-Ju

n 11

-Jun

22-Ju

n 23

-Jun

24-Ju

n 26

-Jun

6-Ju

l 7-

Jul

8-Ju

l 10

-Jul

Tota

l 10

065

1006

5 10

065

1006

5 10

065

1006

5 10

065

1006

5 10

065

1006

5 En

rolle

d 95

35

9535

95

35

9535

95

35

9535

95

35

9535

95

35

9535

10

0% p

artic

ipat

ion,

no

eco+

17

42

1799

17

35

1734

17

01

1749

16

10

1642

15

77

1596

no

eco

+ to

tal

2649

26

49

2649

26

49

2649

26

49

2509

25

09

2509

25

09

no e

co+

n/a

data

, to

exclu

de

245

245

247

246

247

247

236

236

236

236

no e

co+,

miss

ing

row

>19

, to

exlu

cde

394

387

400

395

394

397

393

397

393

396

no e

co+

full

part

icipa

tion

perc

enta

ge

87%

89

%

87%

86

%

85%

87

%

86%

88

%

84%

85

%

no e

co+,

no

part

icipa

tion

69

18

26

27

92

27

14

20

72

14

no e

co+,

ove

rrid

e to

tal

199

200

241

247

216

229

256

215

231

267

no e

co+,

ove

rrid

e at

0~3

0 m

ins

47

49

64

65

56

64

76

55

52

91

no e

co+,

ove

rrid

e at

31-

60 m

ins

61

60

49

60

58

62

72

52

75

70

no e

co+,

ove

rrid

e at

61-

90 m

ins

45

48

68

64

50

55

60

53

43

59

no e

co+,

ove

rrid

e at

91-

119

min

s 46

43

60

58

52

48

48

55

61

47

ec

o+1

- 100

% p

artic

ipat

ion

224

254

262

263

248

260

285

298

290

293

eco+

1 - t

otal

31

3 31

3 31

3 31

3 31

3 31

3 36

4 36

4 36

4 36

4 ec

o+1

- n/a

dat

a, to

exl

uce

6 6

8 8

8 8

20

20

20

20

eco+

1, m

issin

g ro

w >

19, t

o ex

lucd

e 10

8

5 6

8 9

9 7

7 7

eco+

1 fu

ll pa

rtici

patio

n pe

rcen

tage

75

%

85%

87

%

88%

84

%

88%

85

%

88%

86

%

87%

ec

o+1,

no

part

icipa

tion

20

17

8 7

14

3 6

10

18

16

eco+

1, o

verr

ide

tota

l 53

28

30

29

35

33

44

29

29

28

ec

o+1,

ove

rrid

e at

0~3

0 m

ins

14

6 3

5 5

11

10

10

8 7

eco+

1, o

verr

ide

at 3

1-60

min

s 13

8

6 13

7

5 11

4

12

8 ec

o+1,

ove

rrid

e at

61-

90 m

ins

16

10

11

5 11

13

17

10

6

5 ec

o+1,

ove

rrid

e at

91-

119

min

s 10

4

10

6 12

4

6 5

3 8

eco+

2 - 1

00%

par

ticip

atio

n 89

10

4 98

10

3 10

0 94

13

9 13

7 12

5 13

5 ec

o+2

- tot

al

139

139

139

139

139

139

188

188

188

188

eco+

2 - n

/a d

ata,

to e

xluc

e 7

7 7

7 7

7 7

7 7

7 ec

o+2,

miss

ing

row

>19

, to

exlu

cde

3 1

2 1

2 2

6 3

4 3

3

of 342

Techinical Appendix DSM-18ec

o+2

full

part

icipa

tion

perc

enta

ge

69%

79

%

75%

79

%

77%

72

%

79%

77

%

71%

76

%

eco+

2, n

o pa

rtici

patio

n 6

6 3

3 7

10

3 11

18

6

eco+

2, o

verr

ide

tota

l 34

21

29

25

23

26

33

30

35

37

ec

o+2,

ove

rrid

e at

0~3

0 m

ins

8 4

14

7 4

10

13

9 11

6

eco+

2, o

verr

ide

at 3

1-60

min

s 12

7

5 10

7

5 9

8 12

12

ec

o+2,

ove

rrid

e at

61-

90 m

ins

9 5

6 6

8 4

7 9

6 6

eco+

2, o

verr

ide

at 9

1-11

9 m

ins

5 5

4 2

4 7

4 4

6 13

ec

o+3

- 100

% p

artic

ipat

ion

283

295

295

277

281

277

309

321

298

309

eco+

3 - t

otal

40

7 40

7 40

7 40

7 40

7 40

7 44

4 44

4 44

4 44

4 ec

o+3

- n/a

dat

a, to

exl

uce

33

33

33

33

33

33

34

34

34

34

eco+

3, m

issin

g ro

w >

19, t

o ex

lucd

e 8

2 2

3 2

1 3

3 4

4 ec

o+3

full

part

icipa

tion

perc

enta

ge

77%

79

%

79%

75

%

76%

74

%

76%

79

%

73%

76

%

eco+

3, n

o pa

rtici

patio

n 13

8

6 12

20

13

4

16

30

10

eco+

3, o

verr

ide

tota

l 70

69

71

82

71

83

94

70

78

87

ec

o+3,

ove

rrid

e at

0~3

0 m

ins

23

24

28

32

20

21

32

19

30

35

eco+

3, o

verr

ide

at 3

1-60

min

s 26

8

18

19

17

27

26

27

18

22

eco+

3, o

verr

ide

at 6

1-90

min

s 9

15

11

25

25

24

21

13

17

18

eco+

3, o

verr

ide

at 9

1-11

9 m

ins

12

22

14

6 9

11

15

11

13

12

eco+

4 - 1

00%

par

ticip

atio

n 37

05

3836

36

83

3622

36

22

3705

35

82

3590

34

99

3555

ec

o+4

- tot

al

5306

53

06

5306

53

06

5306

53

06

5289

52

89

5289

52

89

eco+

4 - n

/a d

ata,

to e

xluc

e 35

1 35

1 35

1 35

1 35

1 35

1 35

2 35

2 35

2 35

2 ec

o+4,

miss

ing

row

>19

, to

exlu

cde

75

67

88

88

91

92

110

115

113

123

eco+

4 fu

ll pa

rtici

patio

n pe

rcen

tage

76

%

78%

76

%

74%

74

%

76%

74

%

74%

73

%

74%

ec

o+4,

no

part

icipa

tion

132

64

67

91

232

85

61

97

264

95

eco+

4, o

verr

ide

tota

l 10

43

988

1117

11

54

1010

10

73

1184

11

35

1061

11

64

eco+

4, o

verr

ide

at 0

~30

min

s 24

4 27

4 32

0 36

6 31

3 32

0 34

0 34

5 32

8 38

3 ec

o+4,

ove

rrid

e at

31-

60 m

ins

310

308

342

310

296

281

369

331

302

344

eco+

4, o

verr

ide

at 6

1-90

min

s 30

1 22

4 24

6 28

5 22

7 25

9 27

2 26

5 22

9 22

8 ec

o+4,

ove

rrid

e at

91-

119

min

s 18

8 18

2 20

9 19

3 17

4 21

3 20

3 19

4 20

2 20

9 ec

o+5

- 100

% p

artic

ipat

ion

560

556

559

555

523

548

554

574

538

553

eco+

5 - t

otal

72

1 72

1 72

1 72

1 72

1 72

1 74

1 74

1 74

1 74

1 ec

o+5

- n/a

dat

a, to

exl

uce

44

44

44

44

44

44

44

44

44

44

eco+

5, m

issin

g ro

w >

19, t

o ex

lucd

e 6

7 7

6 5

9 7

6 5

7 4

of 342


o+5

full

part

icipa

tion

perc

enta

ge

83%

83

%

83%

83

%

78%

82

%

80%

83

%

78%

80

%

eco+

5, n

o pa

rtici

patio

n 18

8

14

15

29

8 12

13

37

6

eco+

5, o

verr

ide

tota

l 93

10

6 97

10

1 12

0 11

2 12

4 10

4 11

7 13

1 ec

o+5,

ove

rrid

e at

0~3

0 m

ins

26

18

16

39

33

35

25

34

15

40

eco+

5, o

verr

ide

at 3

1-60

min

s 25

29

24

27

33

31

45

29

41

41

ec

o+5,

ove

rrid

e at

61-

90 m

ins

17

27

34

18

32

34

30

22

24

30

eco+

5, o

verr

ide

at 9

1-11

9 m

ins

25

32

23

17

22

12

24

19

37

20

no e

co+,

no

part

icipa

tion,

% o

f all

no e

co+

with

da

ta

3%

1%

1%

1%

5%

1%

1%

1%

4%

1%

no e

co+,

ove

rrid

e at

0~3

0 m

ins,

% o

f all

over

ride

24%

25

%

27%

26

%

26%

28

%

30%

26

%

23%

34

%

no e

co+,

ove

rrid

e at

31-

60 m

ins,

% o

f all

over

ride

31%

30

%

20%

24

%

27%

27

%

28%

24

%

32%

26

%

no e

co+,

ove

rrid

e at

61-

90 m

ins,

% o

f all

over

ride

23%

24

%

28%

26

%

23%

24

%

23%

25

%

19%

22

%

no e

co+,

ove

rrid

e at

91-

119

min

s, %

of a

ll ov

errid

e 23

%

22%

25

%

23%

24

%

21%

19

%

26%

26

%

18%

eco+

1, n

o pa

rtici

patio

n, %

of a

ll ec

o+1

with

dat

a 7%

6%

3%

2%

5%

1%

2%

3%

5%

5%

ec

o+1,

ove

rrid

e at

0~3

0 m

ins,

% o

f all

over

ride

26%

21

%

10%

17

%

14%

33

%

23%

34

%

28%

25

%

eco+

1, o

verr

ide

at 3

1-60

min

s, %

of a

ll ov

errid

e 25

%

29%

20

%

45%

20

%

15%

25

%

14%

41

%

29%

ec

o+1,

ove

rrid

e at

61-

90 m

ins,

% o

f all

over

ride

30%

36

%

37%

17

%

31%

39

%

39%

34

%

21%

18

%

eco+

1, o

verr

ide

at 9

1-11

9 m

ins,

% o

f all

over

ride

19%

14

%

33%

21

%

34%

12

%

14%

17

%

10%

29

%

eco+

2, n

o pa

rtici

patio

n, %

of a

ll ec

o+2

with

dat

a 5%

5%

2%

2%

5%

8%

2%

6%

10

%

3%

eco+

2, o

verr

ide

at 0

~30

min

s, %

of a

ll ov

errid

e 24

%

19%

48

%

28%

17

%

38%

39

%

30%

31

%

16%

ec

o+2,

ove

rrid

e at

31-

60 m

ins,

% o

f all

over

ride

35%

33

%

17%

40

%

30%

19

%

27%

27

%

34%

32

%

eco+

2, o

verr

ide

at 6

1-90

min

s, %

of a

ll ov

errid

e 26

%

24%

21

%

24%

35

%

15%

21

%

30%

17

%

16%

ec

o+2,

ove

rrid

e at

91-

119

min

s, %

of a

ll ov

errid

e 15

%

24%

14

%

8%

17%

27

%

12%

13

%

17%

35

%

eco+

3, n

o pa

rtici

patio

n, %

of a

ll ec

o+3

with

dat

a 4%

2%

2%

3%

5%

3%

1%

4%

7%

2%

ec

o+3,

ove

rrid

e at

0~3

0 m

ins,

% o

f all

over

ride

33%

35

%

39%

39

%

28%

25

%

34%

27

%

38%

40

%

eco+

3, o

verr

ide

at 3

1-60

min

s, %

of a

ll ov

errid

e 37

%

12%

25

%

23%

24

%

33%

28

%

39%

23

%

25%

ec

o+3,

ove

rrid

e at

61-

90 m

ins,

% o

f all

over

ride

13%

22

%

15%

30

%

35%

29

%

22%

19

%

22%

21

%

eco+

3, o

verr

ide

at 9

1-11

9 m

ins,

% o

f all

over

ride

17%

32

%

20%

7%

13

%

13%

16

%

16%

17

%

14%

ec

o+4,

no

part

icipa

tion,

% o

f all

eco+

4 w

ith d

ata

3%

1%

1%

2%

5%

2%

1%

2%

5%

2%

eco+

4, o

verr

ide

at 0

~30

min

s, %

of a

ll ov

errid

e 23

%

28%

29

%

32%

31

%

30%

29

%

30%

31

%

33%

ec

o+4,

ove

rrid

e at

31-

60 m

ins,

% o

f all

over

ride

30%

31

%

31%

27

%

29%

26

%

31%

29

%

28%

30

%

eco+

4, o

verr

ide

at 6

1-90

min

s, %

of a

ll ov

errid

e 29

%

23%

22

%

25%

22

%

24%

23

%

23%

22

%

20%

5

of 342


o+4,

ove

rrid

e at

91-

119

min

s, %

of a

ll ov

errid

e 18

%

18%

19

%

17%

17

%

20%

17

%

17%

19

%

18%

ec

o+5,

no

part

icipa

tion,

% o

f all

eco+

5 w

ith d

ata

3%

1%

2%

2%

4%

1%

2%

2%

5%

1%

eco+

5, o

verr

ide

at 0

~30

min

s, %

of a

ll ov

errid

e 28

%

17%

16

%

39%

28

%

31%

20

%

33%

13

%

31%

ec

o+5,

ove

rrid

e at

31-

60 m

ins,

% o

f all

over

ride

27%

27

%

25%

27

%

28%

28

%

36%

28

%

35%

31

%

eco+

5, o

verr

ide

at 6

1-90

min

s, %

of a

ll ov

errid

e 18

%

25%

35

%

18%

27

%

30%

24

%

21%

21

%

23%

ec

o+5,

ove

rrid

e at

91-

119

min

s, %

of a

ll ov

errid

e 27

%

30%

24

%

17%

18

%

11%

19

%

18%

32

%

15%

6


of 342

NPC

7/1

3/20

20 –

7/2

9/20

20

13-Ju

l 15

-Jul

16-Ju

l 17

-Jul

20-Ju

l 21

-Jul

22-Ju

l 24

-Jul

27-Ju

l 29

-Jul

Tota

l 10

065

1006

5 10

065

1006

5 10

065

1006

5 10

065

1006

5 99

70

9970

En

rolle

d 95

35

9535

95

35

9535

95

35

9535

95

35

9535

95

35

9535

10

0% p

artic

ipat

ion,

no

eco+

13

99

1425

14

06

1376

14

46

1424

14

23

1417

13

96

1369

no

eco

+ to

tal

2287

22

87

2287

22

87

2287

22

87

2287

22

87

2287

22

87

no e

co+

n/a

data

, to

exclu

de

224

224

224

224

224

224

224

224

225

225

no e

co+,

miss

ing

row

>19

, to

exlu

cde

395

388

390

400

385

391

396

395

391

397

no e

co+

full

part

icipa

tion

perc

enta

ge

84%

85

%

84%

83

%

86%

85

%

85%

85

%

84%

82

%

no e

co+,

no

part

icipa

tion

11

17

14

18

16

18

17

13

17

17

no e

co+,

ove

rrid

e to

tal

258

233

253

270

216

230

227

238

258

279

no e

co+,

ove

rrid

e at

0~3

0 m

ins

75

70

89

82

70

77

94

68

79

90

no e

co+,

ove

rrid

e at

31-

60 m

ins

76

63

71

68

53

68

56

67

68

71

no e

co+,

ove

rrid

e at

61-

90 m

ins

48

50

52

46

48

44

38

61

60

59

no e

co+,

ove

rrid

e at

91-

119

min

s 59

50

41

74

45

41

39

42

51

59

ec

o+1

- 100

% p

artic

ipat

ion

318

303

307

300

312

320

314

326

324

316

eco+

1 - t

otal

39

9 39

9 39

9 39

9 39

9 39

9 39

9 39

9 39

9 39

9 ec

o+1

- n/a

dat

a, to

exl

uce

23

23

23

23

23

23

23

23

23

23

eco+

1, m

issin

g ro

w >

19, t

o ex

lucd

e 8

11

14

14

16

15

11

12

13

13

eco+

1 fu

ll pa

rtici

patio

n pe

rcen

tage

86

%

83%

85

%

83%

87

%

89%

86

%

90%

89

%

87%

ec

o+1,

no

part

icipa

tion

22

23

24

24

20

18

16

14

9 12

ec

o+1,

ove

rrid

e to

tal

28

39

31

38

28

23

35

24

30

35

eco+

1, o

verr

ide

at 0

~30

min

s 8

15

6 11

11

5

14

12

9 10

ec

o+1,

ove

rrid

e at

31-

60 m

ins

11

2 9

10

5 8

4 3

6 10

ec

o+1,

ove

rrid

e at

61-

90 m

ins

4 9

8 5

3 3

8 1

7 7

eco+

1, o

verr

ide

at 9

1-11

9 m

ins

5 13

8

12

9 7

9 8

8 8

eco+

2 - 1

00%

par

ticip

atio

n 15

0 15

2 14

7 15

0 15

5 15

5 15

6 16

0 16

8 16

0 ec

o+2

- tot

al

225

225

225

225

225

225

225

225

225

225

eco+

2 - n

/a d

ata,

to e

xluc

e 11

11

11

11

11

11

11

11

11

11

ec

o+2,

miss

ing

row

>19

, to

exlu

cde

7 7

7 7

5 5

5 5

6 4

eco+

2 fu

ll pa

rtici

patio

n pe

rcen

tage

72

%

73%

71

%

72%

74

%

74%

75

%

77%

81

%

76%

ec

o+2,

no

part

icipa

tion

10

23

13

14

11

13

10

9 3

5

7

of 342


o+2,

ove

rrid

e to

tal

47

32

47

43

43

41

43

40

37

45

eco+

2, o

verr

ide

at 0

~30

min

s 15

12

14

15

17

13

16

16

17

17

ec

o+2,

ove

rrid

e at

31-

60 m

ins

16

13

9 6

16

7 15

10

10

9

eco+

2, o

verr

ide

at 6

1-90

min

s 8

4 13

18

3

11

3 11

6

12

eco+

2, o

verr

ide

at 9

1-11

9 m

ins

8 3

11

4 7

10

9 3

4 7

eco+

3 - 1

00%

par

ticip

atio

n 33

6 34

7 34

3 32

6 34

2 32

8 33

4 33

9 33

7 33

8 ec

o+3

- tot

al

492

492

492

492

492

492

492

492

492

492

eco+

3 - n

/a d

ata,

to e

xluc

e 37

37

37

37

37

37

37

37

37

37

ec

o+3,

miss

ing

row

>19

, to

exlu

cde

6 4

8 8

8 7

8 7

6 6

eco+

3 fu

ll pa

rtici

patio

n pe

rcen

tage

75

%

77%

77

%

73%

77

%

73%

75

%

76%

75

%

75%

ec

o+3,

no

part

icipa

tion

13

10

17

14

16

24

13

12

12

16

eco+

3, o

verr

ide

tota

l 10

0 94

87

10

7 89

96

10

0 97

10

0 95

ec

o+3,

ove

rrid

e at

0~3

0 m

ins

49

37

38

39

38

36

36

34

40

37

eco+

3, o

verr

ide

at 3

1-60

min

s 18

36

19

26

23

25

25

34

28

27

ec

o+3,

ove

rrid

e at

61-

90 m

ins

14

11

18

18

18

17

25

17

18

23

eco+

3, o

verr

ide

at 9

1-11

9 m

ins

19

10

12

24

10

18

14

12

14

8 ec

o+4

- 100

% p

artic

ipat

ion

3584

35

96

3560

35

97

3684

36

41

3609

36

67

3597

34

65

eco+

4 - t

otal

53

62

5362

53

62

5362

53

62

5362

53

62

5362

53

62

5362

ec

o+4

- n/a

dat

a, to

exl

uce

351

351

351

351

351

351

351

353

354

354

eco+

4, m

issin

g ro

w >

19, t

o ex

lucd

e 12

6 15

1 14

1 13

0 14

0 14

8 15

9 15

5 15

8 14

4 ec

o+4

full

part

icipa

tion

perc

enta

ge

73%

74

%

73%

74

%

76%

75

%

74%

76

%

74%

71

%

eco+

4, n

o pa

rtici

patio

n 71

99

13

1 12

0 91

12

1 12

6 95

90

12

2 ec

o+4,

ove

rrid

e to

tal

1230

11

66

1180

11

64

1098

11

01

1117

10

92

1163

12

77

eco+

4, o

verr

ide

at 0

~30

min

s 41

6 37

7 40

0 40

9 33

5 35

9 40

4 34

4 40

2 47

5 ec

o+4,

ove

rrid

e at

31-

60 m

ins

332

364

332

319

295

331

301

343

352

354

eco+

4, o

verr

ide

at 6

1-90

min

s 24

5 25

6 25

0 23

8 28

6 22

1 23

5 25

3 22

4 25

2 ec

o+4,

ove

rrid

e at

91-

119

min

s 23

7 16

9 19

8 19

8 18

2 19

0 17

7 15

2 18

5 19

6 ec

o+5

- 100

% p

artic

ipat

ion

562

553

571

559

557

577

577

552

561

547

eco+

5 - t

otal

77

0 77

0 77

0 77

0 77

0 77

0 77

0 77

0 77

0 77

0 ec

o+5

- n/a

dat

a, to

exl

uce

47

47

47

47

47

47

47

48

48

48

eco+

5, m

issin

g ro

w >

19, t

o ex

lucd

e 10

9

11

9 9

7 9

11

12

12

eco+

5 fu

ll pa

rtici

patio

n pe

rcen

tage

79

%

77%

80

%

78%

78

%

81%

81

%

78%

79

%

77%

ec

o+5,

no

part

icipa

tion

9 15

15

20

10

14

7

11

10

11

8

of 342


o+5,

ove

rrid

e to

tal

142

146

127

135

147

125

130

148

139

152

eco+

5, o

verr

ide

at 0

~30

min

s 49

34

38

30

34

32

47

45

45

52

ec

o+5,

ove

rrid

e at

31-

60 m

ins

32

43

43

43

46

40

31

39

36

40

eco+

5, o

verr

ide

at 6

1-90

min

s 28

33

24

26

34

29

25

40

37

33

ec

o+5,

ove

rrid

e at

91-

119

min

s 33

36

22

36

33

24

27

24

21

27

no

eco

+, n

o pa

rtici

patio

n, %

of a

ll no

eco

+ w

ith

data

1%

1%

1%

1%

1%

1%

1%

1%

1%

1%

no e

co+,

ove

rrid

e at

0~3

0 m

ins,

% o

f all

over

ride

29%

30

%

35%

30

%

32%

33

%

41%

29

%

31%

32

%

no e

co+,

ove

rrid

e at

31-

60 m

ins,

% o

f all

over

ride

29%

27

%

28%

25

%

25%

30

%

25%

28

%

26%

25

%

no e

co+,

ove

rrid

e at

61-

90 m

ins,

% o

f all

over

ride

19%

21

%

21%

17

%

22%

19

%

17%

26

%

23%

21

%

no e

co+,

ove

rrid

e at

91-

119

min

s, %

of a

ll ov

errid

e 23

%

21%

16

%

27%

21

%

18%

17

%

18%

20

%

21%

eco+

1, n

o pa

rtici

patio

n, %

of a

ll ec

o+1

with

dat

a 6%

6%

7%

7%

6%

5%

4%

4%

2%

3%

ec

o+1,

ove

rrid

e at

0~3

0 m

ins,

% o

f all

over

ride

29%

38

%

19%

29

%

39%

22

%

40%

50

%

30%

29

%

eco+

1, o

verr

ide

at 3

1-60

min

s, %

of a

ll ov

errid

e 39

%

5%

29%

26

%

18%

35

%

11%

13

%

20%

29

%

eco+

1, o

verr

ide

at 6

1-90

min

s, %

of a

ll ov

errid

e 14

%

23%

26

%

13%

11

%

13%

23

%

4%

23%

20

%

eco+

1, o

verr

ide

at 9

1-11

9 m

ins,

% o

f all

over

ride

18%

33

%

26%

32

%

32%

30

%

26%

33

%

27%

23

%

eco+

2, n

o pa

rtici

patio

n, %

of a

ll ec

o+2

with

dat

a 5%

11

%

6%

7%

5%

6%

5%

4%

1%

2%

eco+

2, o

verr

ide

at 0

~30

min

s, %

of a

ll ov

errid

e 32

%

38%

30

%

35%

40

%

32%

37

%

40%

46

%

38%

ec

o+2,

ove

rrid

e at

31-

60 m

ins,

% o

f all

over

ride

34%

41

%

19%

14

%

37%

17

%

35%

25

%

27%

20

%

eco+

2, o

verr

ide

at 6

1-90

min

s, %

of a

ll ov

errid

e 17

%

13%

28

%

42%

7%

27

%

7%

28%

16

%

27%

ec

o+2,

ove

rrid

e at

91-

119

min

s, %

of a

ll ov

errid

e 17

%

9%

23%

9%

16

%

24%

21

%

8%

11%

16

%

eco+

3, n

o pa

rtici

patio

n, %

of a

ll ec

o+3

with

dat

a 3%

2%

4%

3%

4%

5%

3%

3%

3%

4%

ec

o+3,

ove

rrid

e at

0~3

0 m

ins,

% o

f all

over

ride

49%

39

%

44%

36

%

43%

38

%

36%

35

%

40%

39

%

eco+

3, o

verr

ide

at 3

1-60

min

s, %

of a

ll ov

errid

e 18

%

38%

22

%

24%

26

%

26%

25

%

35%

28

%

28%

ec

o+3,

ove

rrid

e at

61-

90 m

ins,

% o

f all

over

ride

14%

12

%

21%

17

%

20%

18

%

25%

18

%

18%

24

%

eco+

3, o

verr

ide

at 9

1-11

9 m

ins,

% o

f all

over

ride

19%

11

%

14%

22

%

11%

19

%

14%

12

%

14%

8%

ec

o+4,

no

part

icipa

tion,

% o

f all

eco+

4 w

ith d

ata

1%

2%

3%

2%

2%

2%

3%

2%

2%

3%

eco+

4, o

verr

ide

at 0

~30

min

s, %

of a

ll ov

errid

e 34

%

32%

34

%

35%

31

%

33%

36

%

32%

35

%

37%

ec

o+4,

ove

rrid

e at

31-

60 m

ins,

% o

f all

over

ride

27%

31

%

28%

27

%

27%

30

%

27%

31

%

30%

28

%

eco+

4, o

verr

ide

at 6

1-90

min

s, %

of a

ll ov

errid

e 20

%

22%

21

%

20%

26

%

20%

21

%

23%

19

%

20%

ec

o+4,

ove

rrid

e at

91-

119

min

s, %

of a

ll ov

errid

e 19

%

14%

17

%

17%

17

%

17%

16

%

14%

16

%

15%

ec

o+5,

no

part

icipa

tion,

% o

f all

eco+

5 w

ith d

ata

1%

2%

2%

3%

1%

2%

1%

2%

1%

2%

9

of 342


o+5,

ove

rrid

e at

0~3

0 m

ins,

% o

f all

over

ride

35%

23

%

30%

22

%

23%

26

%

36%

30

%

32%

34

%

eco+

5, o

verr

ide

at 3

1-60

min

s, %

of a

ll ov

errid

e 23

%

29%

34

%

32%

31

%

32%

24

%

26%

26

%

26%

ec

o+5,

ove

rrid

e at

61-

90 m

ins,

% o

f all

over

ride

20%

23

%

19%

19

%

23%

23

%

19%

27

%

27%

22

%

eco+

5, o

verr

ide

at 9

1-11

9 m

ins,

% o

f all

over

ride

23%

25

%

17%

27

%

22%

19

%

21%

16

%

15%

18

%

10


of 342

NPC

7/3

0/20

20 –

8/1

6/20

20

30-Ju

l 31

-Jul

3-Au

g 4-

Aug

5-Au

g 10

-Aug

12

-Aug

13

-Aug

14

-Aug

16

-Aug

To

tal

9970

99

70

9970

99

70

9970

99

70

9970

99

70

9970

99

70

Enro

lled

9535

95

35

9535

95

35

9535

95

35

9535

95

35

9535

95

35

100%

par

ticip

atio

n, n

o ec

o+

1373

14

05

1212

11

99

1140

11

65

1206

11

85

1161

11

64

no e

co+

tota

l 22

87

2287

20

21

2021

20

21

2021

20

21

2021

20

21

2021

no

eco

+ n/

a da

ta, t

o ex

clude

22

5 22

5 21

1 21

2 21

1 20

9 20

8 20

8 20

8 20

9 no

eco

+, m

issin

g ro

w >

19, t

o ex

lucd

e 39

7 40

2 39

2 39

6 40

4 41

1 40

7 41

2 41

5 41

5 no

eco

+ fu

ll pa

rtici

patio

n pe

rcen

tage

82

%

85%

85

%

85%

81

%

83%

86

%

85%

83

%

83%

no

eco

+, n

o pa

rtici

patio

n 21

15

13

16

88

10

15

18

16

11

no

eco

+, o

verr

ide

tota

l 27

1 24

0 19

3 19

8 17

8 22

6 18

5 19

8 22

1 22

2 no

eco

+, o

verr

ide

at 0

~30

min

s 85

78

47

65

57

63

60

66

69

82

no

eco

+, o

verr

ide

at 3

1-60

min

s 77

62

57

54

48

48

47

46

74

62

no

eco

+, o

verr

ide

at 6

1-90

min

s 70

54

49

55

43

45

39

54

41

42

no

eco

+, o

verr

ide

at 9

1-11

9 m

ins

39

46

40

24

30

70

39

32

37

36

eco+

1 - 1

00%

par

ticip

atio

n 31

4 30

5 37

0 38

4 36

5 39

8 39

7 39

1 39

2 40

8 ec

o+1

- tot

al

399

399

510

510

510

510

510

510

510

510

eco+

1 - n

/a d

ata,

to e

xluc

e 23

23

34

34

34

34

34

34

34

34

ec

o+1,

miss

ing

row

>19

, to

exlu

cde

13

14

15

14

15

14

14

14

16

16

eco+

1 fu

ll pa

rtici

patio

n pe

rcen

tage

87

%

84%

80

%

83%

79

%

86%

86

%

85%

85

%

89%

ec

o+1,

no

part

icipa

tion

18

21

36

32

55

17

23

26

24

19

eco+

1, o

verr

ide

tota

l 31

36

55

46

41

47

42

45

44

33

ec

o+1,

ove

rrid

e at

0~3

0 m

ins

8 9

14

15

14

13

12

13

13

10

eco+

1, o

verr

ide

at 3

1-60

min

s 10

8

15

14

10

12

9 9

9 9

eco+

1, o

verr

ide

at 6

1-90

min

s 9

11

16

4 11

13

11

12

8

8 ec

o+1,

ove

rrid

e at

91-

119

min

s 4

8 10

13

6

9 10

11

14

6

eco+

2 - 1

00%

par

ticip

atio

n 15

7 16

6 19

6 19

5 18

3 18

5 19

8 19

6 19

9 18

2 ec

o+2

- tot

al

225

225

280

280

280

280

280

280

280

280

eco+

2 - n

/a d

ata,

to e

xluc

e 11

11

13

13

13

13

13

13

13

13

ec

o+2,

miss

ing

row

>19

, to

exlu

cde

4 5

7 6

6 8

9 9

9 7

eco+

2 fu

ll pa

rtici

patio

n pe

rcen

tage

75

%

79%

75

%

75%

70

%

71%

77

%

76%

77

%

70%

ec

o+2,

no

part

icipa

tion

13

9 10

9

25

9 9

12

13

14

11

of 342


o+2,

ove

rrid

e to

tal

40

34

54

57

53

65

51

50

46

64

eco+

2, o

verr

ide

at 0

~30

min

s 11

15

12

25

14

23

18

18

18

24

ec

o+2,

ove

rrid

e at

31-

60 m

ins

10

10

19

16

17

11

16

14

17

15

eco+

2, o

verr

ide

at 6

1-90

min

s 10

5

12

8 15

19

10

11

7

11

eco+

2, o

verr

ide

at 9

1-11

9 m

ins

9 4

11

8 7

12

7 7

4 14

ec

o+3

- 100

% p

artic

ipat

ion

315

332

355

360

343

377

355

358

359

348

eco+

3 - t

otal

49

2 49

2 54

4 54

4 54

4 54

4 54

4 54

4 54

4 54

4 ec

o+3

- n/a

dat

a, to

exl

uce

37

37

44

44

44

44

44

44

44

44

eco+

3, m

issin

g ro

w >

19, t

o ex

lucd

e 6

5 9

11

12

14

16

13

15

18

eco+

3 fu

ll pa

rtici

patio

n pe

rcen

tage

70

%

74%

72

%

74%

70

%

78%

73

%

74%

74

%

72%

ec

o+3,

no

part

icipa

tion

25

24

25

24

44

19

18

23

19

16

eco+

3, o

verr

ide

tota

l 10

9 94

11

1 10

5 10

1 90

11

2 10

6 10

7 11

8 ec

o+3,

ove

rrid

e at

0~3

0 m

ins

43

41

38

36

30

39

46

34

38

49

eco+

3, o

verr

ide

at 3

1-60

min

s 31

17

35

35

29

20

20

25

33

31

ec

o+3,

ove

rrid

e at

61-

90 m

ins

22

17

25

22

23

21

29

29

26

18

eco+

3, o

verr

ide

at 9

1-11

9 m

ins

13

19

13

12

19

10

17

18

10

20

eco+

4 - 1

00%

par

ticip

atio

n 34

98

3488

35

50

3575

34

10

3578

36

10

3583

35

17

3404

ec

o+4

- tot

al

5362

53

62

5356

53

56

5356

53

56

5356

53

56

5356

53

56

eco+

4 - n

/a d

ata,

to e

xluc

e 35

4 35

4 34

0 34

0 34

0 34

1 34

1 34

1 34

1 34

1 ec

o+4,

miss

ing

row

>19

, to

exlu

cde

142

148

151

164

165

169

172

178

181

191

eco+

4 fu

ll pa

rtici

patio

n pe

rcen

tage

72

%

72%

73

%

74%

70

%

74%

75

%

74%

73

%

71%

ec

o+4,

no

part

icipa

tion

175

181

98

137

357

66

86

128

132

93

eco+

4, o

verr

ide

tota

l 11

93

1191

12

17

1140

10

84

1202

11

47

1126

11

86

1327

ec

o+4,

ove

rrid

e at

0~3

0 m

ins

428

478

405

434

374

421

416

422

466

509

eco+

4, o

verr

ide

at 3

1-60

min

s 35

2 34

8 37

4 32

7 33

4 33

6 33

1 31

3 32

9 34

5 ec

o+4,

ove

rrid

e at

61-

90 m

ins

234

221

267

218

196

271

232

215

233

297

eco+

4, o

verr

ide

at 9

1-11

9 m

ins

179

144

171

161

180

174

168

176

158

176

eco+

5 - 1

00%

par

ticip

atio

n 53

0 54

8 58

3 59

2 56

9 59

8 61

1 58

8 57

7 59

5 ec

o+5

- tot

al

770

770

824

824

824

824

824

824

824

824

eco+

5 - n

/a d

ata,

to e

xluc

e 47

47

56

56

56

56

56

57

57

56

ec

o+5,

miss

ing

row

>19

, to

exlu

cde

13

15

17

18

16

21

23

25

21

21

eco+

5 fu

ll pa

rtici

patio

n pe

rcen

tage

75

%

77%

78

%

79%

76

%

80%

82

%

79%

77

%

80%

ec

o+5,

no

part

icipa

tion

23

24

12

17

57

14

8 14

24

17

12

of 342


o+5,

ove

rrid

e to

tal

157

136

156

141

126

135

126

140

145

135

eco+

5, o

verr

ide

at 0

~30

min

s 51

52

61

49

39

38

23

44

42

47

ec

o+5,

ove

rrid

e at

31-

60 m

ins

34

34

38

45

40

42

49

33

44

32

eco+

5, o

verr

ide

at 6

1-90

min

s 39

33

36

26

23

29

23

34

32

27

ec

o+5,

ove

rrid

e at

91-

119

min

s 33

17

21

21

24

26

31

29

27

29

no

eco

+, n

o pa

rtici

patio

n, %

of a

ll no

eco

+ w

ith

data

1%

1%

1%

1%

6%

1%

1%

1%

1%

1%

no e

co+,

ove

rrid

e at

0~3

0 m

ins,

% o

f all

over

ride

31%

33

%

24%

33

%

32%

28

%

32%

33

%

31%

37

%

no e

co+,

ove

rrid

e at

31-

60 m

ins,

% o

f all

over

ride

28%

26

%

30%

27

%

27%

21

%

25%

23

%

33%

28

%

no e

co+,

ove

rrid

e at

61-

90 m

ins,

% o

f all

over

ride

26%

23

%

25%

28

%

24%

20

%

21%

27

%

19%

19

%

no e

co+,

ove

rrid

e at

91-

119

min

s, %

of a

ll ov

errid

e 14

%

19%

21

%

12%

17

%

31%

21

%

16%

17

%

16%

eco+

1, n

o pa

rtici

patio

n, %

of a

ll ec

o+1

with

dat

a 5%

6%

8%

7%

12

%

4%

5%

6%

5%

4%

eco+

1, o

verr

ide

at 0

~30

min

s, %

of a

ll ov

errid

e 26

%

25%

25

%

33%

34

%

28%

29

%

29%

30

%

30%

ec

o+1,

ove

rrid

e at

31-

60 m

ins,

% o

f all

over

ride

32%

22

%

27%

30

%

24%

26

%

21%

20

%

20%

27

%

eco+

1, o

verr

ide

at 6

1-90

min

s, %

of a

ll ov

errid

e 29

%

31%

29

%

9%

27%

28

%

26%

27

%

18%

24

%

eco+

1, o

verr

ide

at 9

1-11

9 m

ins,

% o

f all

over

ride

13%

22

%

18%

28

%

15%

19

%

24%

24

%

32%

18

%

eco+

2, n

o pa

rtici

patio

n, %

of a

ll ec

o+2

with

dat

a 6%

4%

4%

3%

10

%

3%

3%

5%

5%

5%

eco+

2, o

verr

ide

at 0

~30

min

s, %

of a

ll ov

errid

e 28

%

44%

22

%

44%

26

%

35%

35

%

36%

39

%

38%

ec

o+2,

ove

rrid

e at

31-

60 m

ins,

% o

f all

over

ride

25%

29

%

35%

28

%

32%

17

%

31%

28

%

37%

23

%

eco+

2, o

verr

ide

at 6

1-90

min

s, %

of a

ll ov

errid

e 25

%

15%

22

%

14%

28

%

29%

20

%

22%

15

%

17%

ec

o+2,

ove

rrid

e at

91-

119

min

s, %

of a

ll ov

errid

e 23

%

12%

20

%

14%

13

%

18%

14

%

14%

9%

22

%

eco+

3, n

o pa

rtici

patio

n, %

of a

ll ec

o+3

with

dat

a 6%

5%

5%

5%

9%

4%

4%

5%

4%

3%

ec

o+3,

ove

rrid

e at

0~3

0 m

ins,

% o

f all

over

ride

39%

44

%

34%

34

%

30%

43

%

41%

32

%

36%

42

%

eco+

3, o

verr

ide

at 3

1-60

min

s, %

of a

ll ov

errid

e 28

%

18%

32

%

33%

29

%

22%

18

%

24%

31

%

26%

ec

o+3,

ove

rrid

e at

61-

90 m

ins,

% o

f all

over

ride

20%

18

%

23%

21

%

23%

23

%

26%

27

%

24%

15

%

eco+

3, o

verr

ide

at 9

1-11

9 m

ins,

% o

f all

over

ride

12%

20

%

12%

11

%

19%

11

%

15%

17

%

9%

17%

ec

o+4,

no

part

icipa

tion,

% o

f all

eco+

4 w

ith d

ata

4%

4%

2%

3%

7%

1%

2%

3%

3%

2%

eco+

4, o

verr

ide

at 0

~30

min

s, %

of a

ll ov

errid

e 36

%

40%

33

%

38%

35

%

35%

36

%

37%

39

%

38%

ec

o+4,

ove

rrid

e at

31-

60 m

ins,

% o

f all

over

ride

30%

29

%

31%

29

%

31%

28

%

29%

28

%

28%

26

%

eco+

4, o

verr

ide

at 6

1-90

min

s, %

of a

ll ov

errid

e 20

%

19%

22

%

19%

18

%

23%

20

%

19%

20

%

22%

ec

o+4,

ove

rrid

e at

91-

119

min

s, %

of a

ll ov

errid

e 15

%

12%

14

%

14%

17

%

14%

15

%

16%

13

%

13%

ec

o+5,

no

part

icipa

tion,

% o

f all

eco+

5 w

ith d

ata

3%

3%

2%

2%

8%

2%

1%

2%

3%

2%

13

of 342


o+5,

ove

rrid

e at

0~3

0 m

ins,

% o

f all

over

ride

32%

38

%

39%

35

%

31%

28

%

18%

31

%

29%

35

%

eco+

5, o

verr

ide

at 3

1-60

min

s, %

of a

ll ov

errid

e 22

%

25%

24

%

32%

32

%

31%

39

%

24%

30

%

24%

ec

o+5,

ove

rrid

e at

61-

90 m

ins,

% o

f all

over

ride

25%

24

%

23%

18

%

18%

21

%

18%

24

%

22%

20

%

eco+

5, o

verr

ide

at 9

1-11

9 m

ins,

% o

f all

over

ride

21%

13

%

13%

15

%

19%

19

%

25%

21

%

19%

21

%

14


of 342

NPC

8/1

7/20

20 –

9/3

/202

0 17

-Aug

20

-Aug

21

-Aug

24

-Aug

25

-Aug

26

-Aug

28

-Aug

31

-Aug

2-

Sep

3-Se

p To

tal

9970

99

70

9970

99

70

9970

99

70

9970

99

70

9970

99

70

Enro

lled

9535

95

35

9535

95

35

9535

95

35

9535

95

35

9488

94

88

100%

par

ticip

atio

n, n

o ec

o+

1152

11

86

1186

11

72

1144

11

65

1164

11

71

1091

10

81

no e

co+

tota

l 20

21

2021

20

21

2021

20

21

2021

20

21

2021

19

40

1940

no

eco

+ n/

a da

ta, t

o ex

clude

20

9 20

8 20

8 20

9 20

9 20

9 20

9 21

0 20

8 20

8 no

eco

+, m

issin

g ro

w >

19, t

o ex

lucd

e 41

6 41

1 41

1 42

5 42

6 42

7 42

7 42

5 43

1 43

3 no

eco

+ fu

ll pa

rtici

patio

n pe

rcen

tage

83

%

85%

85

%

84%

83

%

84%

84

%

84%

84

%

83%

no

eco

+, n

o pa

rtici

patio

n 21

11

11

13

14

16

11

12

27

18

no

eco

+, o

verr

ide

tota

l 22

3 20

7 20

7 20

2 22

8 20

5 21

0 20

3 18

3 20

2 no

eco

+, o

verr

ide

at 0

~30

min

s 72

76

76

67

87

59

62

48

59

58

no

eco

+, o

verr

ide

at 3

1-60

min

s 66

58

58

54

56

63

54

58

56

52

no

eco

+, o

verr

ide

at 6

1-90

min

s 45

39

39

51

47

54

49

48

44

59

no

eco

+, o

verr

ide

at 9

1-11

9 m

ins

40

34

34

30

38

29

45

49

24

33

eco+

1 - 1

00%

par

ticip

atio

n 38

8 39

0 39

0 38

7 38

2 38

2 39

2 37

7 44

1 42

4 ec

o+1

- tot

al

510

510

510

510

510

510

510

510

557

557

eco+

1 - n

/a d

ata,

to e

xluc

e 34

34

34

33

34

34

34

36

44

44

ec

o+1,

miss

ing

row

>19

, to

exlu

cde

22

23

23

24

26

22

22

25

22

22

eco+

1 fu

ll pa

rtici

patio

n pe

rcen

tage

85

%

86%

86

%

85%

85

%

84%

86

%

84%

90

%

86%

ec

o+1,

no

part

icipa

tion

23

18

18

28

20

20

31

19

21

27

eco+

1, o

verr

ide

tota

l 43

45

45

39

48

52

31

53

29

40

ec

o+1,

ove

rrid

e at

0~3

0 m

ins

13

13

13

10

17

7 5

12

12

14

eco+

1, o

verr

ide

at 3

1-60

min

s 8

12

12

10

6 15

5

9 3

5 ec

o+1,

ove

rrid

e at

61-

90 m

ins

9 10

10

8

14

12

11

11

8 12

ec

o+1,

ove

rrid

e at

91-

119

min

s 13

10

10

11

11

18

10

21

6

9 ec

o+2

- 100

% p

artic

ipat

ion

193

194

194

195

202

198

199

180

222

211

eco+

2 - t

otal

28

0 28

0 28

0 28

0 28

0 28

0 28

0 28

0 30

8 30

8 ec

o+2

- n/a

dat

a, to

exl

uce

13

13

13

13

13

13

13

13

13

13

eco+

2, m

issin

g ro

w >

19, t

o ex

lucd

e 8

8 8

10

8 8

9 8

8 9

eco+

2 fu

ll pa

rtici

patio

n pe

rcen

tage

75

%

75%

75

%

76%

78

%

76%

77

%

69%

77

%

74%

ec

o+2,

no

part

icipa

tion

18

11

11

8 8

12

12

16

16

11

15

of 342


o+2,

ove

rrid

e to

tal

48

54

54

54

49

49

47

63

49

64

eco+

2, o

verr

ide

at 0

~30

min

s 23

19

19

24

21

23

10

17

22

27

ec

o+2,

ove

rrid

e at

31-

60 m

ins

11

14

14

18

13

11

19

21

11

18

eco+

2, o

verr

ide

at 6

1-90

min

s 9

10

10

7 9

8 10

10

9

12

eco+

2, o

verr

ide

at 9

1-11

9 m

ins

5 11

11

5

6 7

8 15

7

7 ec

o+3

- 100

% p

artic

ipat

ion

349

370

370

359

363

348

341

347

378

375

eco+

3 - t

otal

54

4 54

4 54

4 54

4 54

4 54

4 54

4 54

4 57

4 57

4 ec

o+3

- n/a

dat

a, to

exl

uce

44

44

44

44

44

44

44

44

49

51

eco+

3, m

issin

g ro

w >

19, t

o ex

lucd

e 19

14

14

14

16

17

15

15

13

13

ec

o+3

full

part

icipa

tion

perc

enta

ge

73%

76

%

76%

74

%

75%

72

%

70%

72

%

74%

74

%

eco+

3, n

o pa

rtici

patio

n 27

20

20

21

17

24

20

18

18

18

ec

o+3,

ove

rrid

e to

tal

105

96

96

106

104

112

124

120

116

117

eco+

3, o

verr

ide

at 0

~30

min

s 44

35

35

37

42

37

40

38

37

38

ec

o+3,

ove

rrid

e at

31-

60 m

ins

28

27

27

30

24

34

42

34

40

33

eco+

3, o

verr

ide

at 6

1-90

min

s 18

19

19

23

21

22

18

25

25

32

ec

o+3,

ove

rrid

e at

91-

119

min

s 15

15

15

16

17

19

24

23

14

14

ec

o+4

- 100

% p

artic

ipat

ion

3433

35

55

3555

35

49

3462

34

22

3401

34

23

3560

34

88

eco+

4 - t

otal

53

56

5356

53

56

5356

53

56

5356

53

56

5356

52

76

5276

ec

o+4

- n/a

dat

a, to

exl

uce

342

342

342

341

341

341

343

341

327

327

eco+

4, m

issin

g ro

w >

19, t

o ex

lucd

e 20

0 20

4 20

4 20

6 22

2 21

9 22

9 22

9 20

0 20

8 ec

o+4

full

part

icipa

tion

perc

enta

ge

71%

74

%

74%

74

%

72%

71

%

71%

72

%

75%

74

%

eco+

4, n

o pa

rtici

patio

n 14

7 12

2 12

2 96

13

0 15

7 14

7 11

0 85

11

9 ec

o+4,

ove

rrid

e to

tal

1234

11

34

1134

11

65

1202

12

17

1236

12

54

1104

11

34

eco+

4, o

verr

ide

at 0

~30

min

s 54

8 43

9 43

9 42

6 46

4 48

3 43

6 37

6 37

6 43

0 ec

o+4,

ove

rrid

e at

31-

60 m

ins

314

333

333

325

357

291

350

339

297

311

eco+

4, o

verr

ide

at 6

1-90

min

s 20

3 21

7 21

7 23

1 21

7 25

3 27

9 24

8 25

3 21

6 ec

o+4,

ove

rrid

e at

91-

119

min

s 16

9 14

5 14

5 18

3 16

4 19

0 17

1 29

1 17

8 17

7 ec

o+5

- 100

% p

artic

ipat

ion

580

597

597

603

583

581

574

591

617

617

eco+

5 - t

otal

82

4 82

4 82

4 82

4 82

4 82

4 82

4 82

4 83

3 83

3 ec

o+5

- n/a

dat

a, to

exl

uce

56

56

56

56

56

56

56

56

53

53

eco+

5, m

issin

g ro

w >

19, t

o ex

lucd

e 22

21

21

22

21

20

20

23

22

18

ec

o+5

full

part

icipa

tion

perc

enta

ge

78%

80

%

80%

81

%

78%

78

%

77%

79

%

81%

81

%

eco+

5, n

o pa

rtici

patio

n 17

13

13

10

16

22

23

7

8 16

16

of 342


o+5,

ove

rrid

e to

tal

149

137

137

133

149

145

151

147

133

129

eco+

5, o

verr

ide

at 0

~30

min

s 62

38

38

38

51

51

40

32

31

39

ec

o+5,

ove

rrid

e at

31-

60 m

ins

36

41

41

43

48

43

44

25

32

33

eco+

5, o

verr

ide

at 6

1-90

min

s 20

30

30

33

32

24

32

40

37

32

ec

o+5,

ove

rrid

e at

91-

119

min

s 31

28

28

19

18

27

35

50

33

25

no

eco

+, n

o pa

rtici

patio

n, %

of a

ll no

eco

+ w

ith

data

2%

1%

1%

1%

1%

1%

1%

1%

2%

1%

no e

co+,

ove

rrid

e at

0~3

0 m

ins,

% o

f all

over

ride

32%

37

%

37%

33

%

38%

29

%

30%

24

%

32%

29

%

no e

co+,

ove

rrid

e at

31-

60 m

ins,

% o

f all

over

ride

30%

28

%

28%

27

%

25%

31

%

26%

29

%

31%

26

%

no e

co+,

ove

rrid

e at

61-

90 m

ins,

% o

f all

over

ride

20%

19

%

19%

25

%

21%

26

%

23%

24

%

24%

29

%

no e

co+,

ove

rrid

e at

91-

119

min

s, %

of a

ll ov

errid

e 18

%

16%

16

%

15%

17

%

14%

21

%

24%

13

%

16%

eco+

1, n

o pa

rtici

patio

n, %

of a

ll ec

o+1

with

dat

a 5%

4%

4%

6%

4%

4%

7%

4%

4%

5%

ec

o+1,

ove

rrid

e at

0~3

0 m

ins,

% o

f all

over

ride

30%

29

%

29%

26

%

35%

13

%

16%

23

%

41%

35

%

eco+

1, o

verr

ide

at 3

1-60

min

s, %

of a

ll ov

errid

e 19

%

27%

27

%

26%

13

%

29%

16

%

17%

10

%

13%

ec

o+1,

ove

rrid

e at

61-

90 m

ins,

% o

f all

over

ride

21%

22

%

22%

21

%

29%

23

%

35%

21

%

28%

30

%

eco+

1, o

verr

ide

at 9

1-11

9 m

ins,

% o

f all

over

ride

30%

22

%

22%

28

%

23%

35

%

32%

40

%

21%

23

%

eco+

2, n

o pa

rtici

patio

n, %

of a

ll ec

o+2

with

dat

a 7%

4%

4%

3%

3%

5%

5%

6%

6%

4%

ec

o+2,

ove

rrid

e at

0~3

0 m

ins,

% o

f all

over

ride

48%

35

%

35%

44

%

43%

47

%

21%

27

%

45%

42

%

eco+

2, o

verr

ide

at 3

1-60

min

s, %

of a

ll ov

errid

e 23

%

26%

26

%

33%

27

%

22%

40

%

33%

22

%

28%

ec

o+2,

ove

rrid

e at

61-

90 m

ins,

% o

f all

over

ride

19%

19

%

19%

13

%

18%

16

%

21%

16

%

18%

19

%

eco+

2, o

verr

ide

at 9

1-11

9 m

ins,

% o

f all

over

ride

10%

20

%

20%

9%

12

%

14%

17

%

24%

14

%

11%

ec

o+3,

no

part

icipa

tion,

% o

f all

eco+

3 w

ith d

ata

6%

4%

4%

4%

4%

5%

4%

4%

4%

4%

eco+

3, o

verr

ide

at 0

~30

min

s, %

of a

ll ov

errid

e 42

%

36%

36

%

35%

40

%

33%

32

%

32%

32

%

32%

ec

o+3,

ove

rrid

e at

31-

60 m

ins,

% o

f all

over

ride

27%

28

%

28%

28

%

23%

30

%

34%

28

%

34%

28

%

eco+

3, o

verr

ide

at 6

1-90

min

s, %

of a

ll ov

errid

e 17

%

20%

20

%

22%

20

%

20%

15

%

21%

22

%

27%

ec

o+3,

ove

rrid

e at

91-

119

min

s, %

of a

ll ov

errid

e 14

%

16%

16

%

15%

16

%

17%

19

%

19%

12

%

12%

ec

o+4,

no

part

icipa

tion,

% o

f all

eco+

4 w

ith d

ata

3%

3%

3%

2%

3%

3%

3%

2%

2%

3%

eco+

4, o

verr

ide

at 0

~30

min

s, %

of a

ll ov

errid

e 44

%

39%

39

%

37%

39

%

40%

35

%

30%

34

%

38%

ec

o+4,

ove

rrid

e at

31-

60 m

ins,

% o

f all

over

ride

25%

29

%

29%

28

%

30%

24

%

28%

27

%

27%

27

%

eco+

4, o

verr

ide

at 6

1-90

min

s, %

of a

ll ov

errid

e 16

%

19%

19

%

20%

18

%

21%

23

%

20%

23

%

19%

ec

o+4,

ove

rrid

e at

91-

119

min

s, %

of a

ll ov

errid

e 14

%

13%

13

%

16%

14

%

16%

14

%

23%

16

%

16%

ec

o+5,

no

part

icipa

tion,

% o

f all

eco+

5 w

ith d

ata

2%

2%

2%

1%

2%

3%

3%

1%

1%

2%

17

of 342


o+5,

ove

rrid

e at

0~3

0 m

ins,

% o

f all

over

ride

42%

28

%

28%

29

%

34%

35

%

26%

22

%

23%

30

%

eco+

5, o

verr

ide

at 3

1-60

min

s, %

of a

ll ov

errid

e 24

%

30%

30

%

32%

32

%

30%

29

%

17%

24

%

26%

ec

o+5,

ove

rrid

e at

61-

90 m

ins,

% o

f all

over

ride

13%

22

%

22%

25

%

21%

17

%

21%

27

%

28%

25

%

eco+

5, o

verr

ide

at 9

1-11

9 m

ins,

% o

f all

over

ride

21%

20

%

20%

14

%

12%

19

%

23%

34

%

25%

19

%

18


of 342

NPC

9/4

/202

0 –

9/7/

2020

4-

Sep

5-Se

p 6-

Sep

7-Se

p To

tal

9970

99

70

9970

99

70

Enro

lled

9488

94

88

9488

94

88

100%

par

ticip

atio

n, n

o ec

o+

1100

10

93

1096

11

11

no e

co+

tota

l 19

40

1940

19

40

1940

no

eco

+ n/

a da

ta, t

o ex

clude

20

8 20

8 20

8 20

8 no

eco

+, m

issin

g ro

w >

19, t

o ex

lucd

e 42

9 43

2 42

9 43

0 no

eco

+ fu

ll pa

rtici

patio

n pe

rcen

tage

84

%

84%

84

%

85%

no

eco

+, n

o pa

rtici

patio

n 18

24

25

16

no

eco

+, o

verr

ide

tota

l 18

5 18

3 18

2 17

5 no

eco

+, o

verr

ide

at 0

~30

min

s 76

63

73

56

no

eco

+, o

verr

ide

at 3

1-60

min

s 45

39

50

43

no

eco

+, o

verr

ide

at 6

1-90

min

s 37

43

40

45

no

eco

+, o

verr

ide

at 9

1-11

9 m

ins

27

38

19

31

eco+

1 - 1

00%

par

ticip

atio

n 43

5 40

8 40

6 38

9 ec

o+1

- tot

al

557

557

557

557

eco+

1 - n

/a d

ata,

to e

xluc

e 44

44

44

44

ec

o+1,

miss

ing

row

>19

, to

exlu

cde

23

24

22

24

eco+

1 fu

ll pa

rtici

patio

n pe

rcen

tage

89

%

83%

83

%

80%

ec

o+1,

no

part

icipa

tion

21

18

27

27

eco+

1, o

verr

ide

tota

l 34

63

58

73

ec

o+1,

ove

rrid

e at

0~3

0 m

ins

14

20

20

32

eco+

1, o

verr

ide

at 3

1-60

min

s 6

22

13

20

eco+

1, o

verr

ide

at 6

1-90

min

s 8

10

13

15

eco+

1, o

verr

ide

at 9

1-11

9 m

ins

6 11

12

6

eco+

2 - 1

00%

par

ticip

atio

n 21

6 20

9 21

6 21

7 ec

o+2

- tot

al

308

308

308

308

eco+

2 - n

/a d

ata,

to e

xluc

e 13

13

13

13

ec

o+2,

miss

ing

row

>19

, to

exlu

cde

12

10

9 10

ec

o+2

full

part

icipa

tion

perc

enta

ge

76%

73

%

76%

76

%

eco+

2, n

o pa

rtici

patio

n 11

16

12

10

19

of 342


o+2,

ove

rrid

e to

tal

56

60

58

58

eco+

2, o

verr

ide

at 0

~30

min

s 23

28

24

20

ec

o+2,

ove

rrid

e at

31-

60 m

ins

19

15

13

17

eco+

2, o

verr

ide

at 6

1-90

min

s 9

10

14

16

eco+

2, o

verr

ide

at 9

1-11

9 m

ins

5 7

7 5

eco+

3 - 1

00%

par

ticip

atio

n 38

4 37

5 38

1 38

1 ec

o+3

- tot

al

574

574

574

574

eco+

3 - n

/a d

ata,

to e

xluc

e 50

50

50

50

ec

o+3,

miss

ing

row

>19

, to

exlu

cde

12

12

12

12

eco+

3 fu

ll pa

rtici

patio

n pe

rcen

tage

75

%

73%

74

%

74%

ec

o+3,

no

part

icipa

tion

29

22

26

23

eco+

3, o

verr

ide

tota

l 99

11

5 10

5 10

8 ec

o+3,

ove

rrid

e at

0~3

0 m

ins

37

52

49

40

eco+

3, o

verr

ide

at 3

1-60

min

s 30

29

29

21

ec

o+3,

ove

rrid

e at

61-

90 m

ins

19

17

14

28

eco+

3, o

verr

ide

at 9

1-11

9 m

ins

13

17

13

19

eco+

4 - 1

00%

par

ticip

atio

n 34

95

3572

35

34

3574

ec

o+4

- tot

al

5276

52

76

5276

52

76

eco+

4 - n

/a d

ata,

to e

xluc

e 32

7 32

8 32

7 32

7 ec

o+4,

miss

ing

row

>19

, to

exlu

cde

233

213

216

212

eco+

4 fu

ll pa

rtici

patio

n pe

rcen

tage

74

%

75%

75

%

75%

ec

o+4,

no

part

icipa

tion

97

109

131

137

eco+

4, o

verr

ide

tota

l 11

24

1054

10

68

1026

ec

o+4,

ove

rrid

e at

0~3

0 m

ins

441

400

447

338

eco+

4, o

verr

ide

at 3

1-60

min

s 28

2 30

6 28

9 29

1 ec

o+4,

ove

rrid

e at

61-

90 m

ins

235

211

191

221

eco+

4, o

verr

ide

at 9

1-11

9 m

ins

166

137

141

176

eco+

5 - 1

00%

par

ticip

atio

n 60

7 60

0 60

9 60

7 ec

o+5

- tot

al

833

833

833

833

eco+

5 - n

/a d

ata,

to e

xluc

e 53

53

53

53

ec

o+5,

miss

ing

row

>19

, to

exlu

cde

24

24

22

24

eco+

5 fu

ll pa

rtici

patio

n pe

rcen

tage

80

%

79%

80

%

80%

ec

o+5,

no

part

icipa

tion

20

17

19

23

20

of 342


o+5,

ove

rrid

e to

tal

129

139

130

126

eco+

5, o

verr

ide

at 0

~30

min

s 41

40

52

29

ec

o+5,

ove

rrid

e at

31-

60 m

ins

50

45

34

37

eco+

5, o

verr

ide

at 6

1-90

min

s 18

25

31

26

ec

o+5,

ove

rrid

e at

91-

119

min

s 20

29

13

34

no

eco

+, n

o pa

rtici

patio

n, %

of a

ll no

eco

+ w

ith

data

1%

2%

2%

1%

no e

co+,

ove

rrid

e at

0~3

0 m

ins,

% o

f all

over

ride

41%

34

%

40%

32

%

no e

co+,

ove

rrid

e at

31-

60 m

ins,

% o

f all

over

ride

24%

21

%

27%

25

%

no e

co+,

ove

rrid

e at

61-

90 m

ins,

% o

f all

over

ride

20%

23

%

22%

26

%

no e

co+,

ove

rrid

e at

91-

119

min

s, %

of a

ll ov

errid

e 15

%

21%

10

%

18%

eco+

1, n

o pa

rtici

patio

n, %

of a

ll ec

o+1

with

dat

a 4%

4%

5%

6%

ec

o+1,

ove

rrid

e at

0~3

0 m

ins,

% o

f all

over

ride

41%

32

%

34%

44

%

eco+

1, o

verr

ide

at 3

1-60

min

s, %

of a

ll ov

errid

e 18

%

35%

22

%

27%

ec

o+1,

ove

rrid

e at

61-

90 m

ins,

% o

f all

over

ride

24%

16

%

22%

21

%

eco+

1, o

verr

ide

at 9

1-11

9 m

ins,

% o

f all

over

ride

18%

17

%

21%

8%

ec

o+2,

no

part

icipa

tion,

% o

f all

eco+

2 w

ith d

ata

4%

6%

4%

4%

eco+

2, o

verr

ide

at 0

~30

min

s, %

of a

ll ov

errid

e 41

%

47%

41

%

34%

ec

o+2,

ove

rrid

e at

31-

60 m

ins,

% o

f all

over

ride

34%

25

%

22%

29

%

eco+

2, o

verr

ide

at 6

1-90

min

s, %

of a

ll ov

errid

e 16

%

17%

24

%

28%

ec

o+2,

ove

rrid

e at

91-

119

min

s, %

of a

ll ov

errid

e 9%

12

%

12%

9%

ec

o+3,

no

part

icipa

tion,

% o

f all

eco+

3 w

ith d

ata

6%

4%

5%

4%

eco+

3, o

verr

ide

at 0

~30

min

s, %

of a

ll ov

errid

e 37

%

45%

47

%

37%

ec

o+3,

ove

rrid

e at

31-

60 m

ins,

% o

f all

over

ride

30%

25

%

28%

19

%

eco+

3, o

verr

ide

at 6

1-90

min

s, %

of a

ll ov

errid

e 19

%

15%

13

%

26%

ec

o+3,

ove

rrid

e at

91-

119

min

s, %

of a

ll ov

errid

e 13

%

15%

12

%

18%

ec

o+4,

no

part

icipa

tion,

% o

f all

eco+

4 w

ith d

ata

2%

2%

3%

3%

eco+

4, o

verr

ide

at 0

~30

min

s, %

of a

ll ov

errid

e 39

%

38%

42

%

33%

ec

o+4,

ove

rrid

e at

31-

60 m

ins,

% o

f all

over

ride

25%

29

%

27%

28

%

eco+

4, o

verr

ide

at 6

1-90

min

s, %

of a

ll ov

errid

e 21

%

20%

18

%

22%

ec

o+4,

ove

rrid

e at

91-

119

min

s, %

of a

ll ov

errid

e 15

%

13%

13

%

17%

ec

o+5,

no

part

icipa

tion,

% o

f all

eco+

5 w

ith d

ata

3%

2%

3%

3%

21

of 342


o+5,

ove

rrid

e at

0~3

0 m

ins,

% o

f all

over

ride

32%

29

%

40%

23

%

eco+

5, o

verr

ide

at 3

1-60

min

s, %

of a

ll ov

errid

e 39

%

32%

26

%

29%

ec

o+5,

ove

rrid

e at

61-

90 m

ins,

% o

f all

over

ride

14%

18

%

24%

21

%

eco+

5, o

verr

ide

at 9

1-11

9 m

ins,

% o

f all

over

ride

16%

21

%

10%

27

%

22


of 342

Appe

ndix

B –

SPP

C 20

20 D

R pa

rtic

ipat

ion

sum

mar

y

SPPC

6/3

/202

0 –

7/15

/202

0 3-

Jun

22-Ju

n 23

-Jun

24-Ju

n 26

-Jun

6-Ju

l 9-

Jul

10-Ju

l 13

-Jul

15-Ju

l To

tal

2243

22

43

2243

22

43

2243

22

43

2243

22

43

2243

22

43

Enro

lled

2165

21

65

2165

21

65

2165

21

65

2165

21

65

2165

21

65

100%

par

ticip

atio

n, n

o ec

o+

586

582

555

547

541

496

497

489

452

420

no e

co+

tota

l 78

5 78

5 78

5 78

5 78

5 68

0 68

0 68

0 65

6 65

6 no

eco

+ n/

a da

ta, t

o ex

clude

49

49

49

49

49

46

46

46

46

46

no

eco

+, m

issin

g ro

w >

19, t

o ex

lucd

e 89

92

93

91

92

90

93

96

92

95

no

eco

+ fu

ll pa

rtici

patio

n pe

rcen

tage

91

%

90%

86

%

85%

84

%

91%

92

%

91%

87

%

82%

no

eco

+, n

o pa

rtici

patio

n 15

2

7 24

4

2 3

1 2

5 no

eco

+, o

verr

ide

tota

l 46

61

81

74

99

46

41

49

64

90

no

eco

+, o

verr

ide

at 0

~30

min

s 11

12

21

17

27

9

9 10

22

22

no

eco

+, o

verr

ide

at 3

1-60

min

s 6

17

23

17

22

12

9 13

13

28

no

eco

+, o

verr

ide

at 6

1-90

min

s 13

13

16

20

31

8

14

12

16

19

no e

co+,

ove

rrid

e at

91-

119

min

s 16

19

21

20

19

17

9

14

13

21

eco+

1 - 1

00%

par

ticip

atio

n 42

50

47

45

44

64

64

63

68

65

ec

o+1

- tot

al

57

57

57

57

57

80

80

80

84

84

eco+

1 - n

/a d

ata,

to e

xluc

e 4

4 4

4 4

7 7

7 7

7 ec

o+1,

miss

ing

row

>19

, to

exlu

cde

2 3

2 4

5 6

6 6

6 5

eco+

1 fu

ll pa

rtici

patio

n pe

rcen

tage

82

%

100%

92

%

92%

92

%

96%

96

%

94%

96

%

90%

ec

o+1,

no

part

icipa

tion

3 0

1 3

0 1

1 1

2 2

eco+

1, o

verr

ide

tota

l 6

0 3

1 4

2 2

3 1

5 ec

o+1,

ove

rrid

e at

0~3

0 m

ins

2 0

0 0

2 0

0 1

1 0

eco+

1, o

verr

ide

at 3

1-60

min

s 1

0 0

0 0

0 0

0 0

3 ec

o+1,

ove

rrid

e at

61-

90 m

ins

2 0

2 1

1 0

1 2

0 0

eco+

1, o

verr

ide

at 9

1-11

9 m

ins

1 0

1 0

1 2

1 0

0 2

eco+

2 - 1

00%

par

ticip

atio

n 28

25

24

25

22

31

33

30

38

35

ec

o+2

- tot

al

30

30

30

30

30

37

37

37

42

42

eco+

2 - n

/a d

ata,

to e

xluc

e 0

0 0

0 0

0 0

0 0

0 ec

o+2,

miss

ing

row

>19

, to

exlu

cde

0 0

0 0

1 0

0 0

0 0

23

of 342


o+2

full

part

icipa

tion

perc

enta

ge

93%

83

%

80%

83

%

76%

84

%

89%

81

%

90%

83

%

eco+

2, n

o pa

rtici

patio

n 0

1 1

3 3

1 1

0 0

1 ec

o+2,

ove

rrid

e to

tal

2 4

5 2

4 5

3 7

4 6

eco+

2, o

verr

ide

at 0

~30

min

s 1

0 4

0 1

2 0

1 0

2 ec

o+2,

ove

rrid

e at

31-

60 m

ins

0 1

0 1

1 3

2 1

2 2

eco+

2, o

verr

ide

at 6

1-90

min

s 0

3 0

1 0

0 0

2 0

1 ec

o+2,

ove

rrid

e at

91-

119

min

s 1

0 1

0 2

0 1

3 2

1 ec

o+3

- 100

% p

artic

ipat

ion

98

97

89

93

93

111

114

108

105

98

eco+

3 - t

otal

11

3 11

3 11

3 11

3 11

3 13

4 13

4 13

4 13

7 13

7 ec

o+3

- n/a

dat

a, to

exl

uce

1 1

1 1

1 2

2 2

2 2

eco+

3, m

issin

g ro

w >

19, t

o ex

lucd

e 0

1 3

4 2

4 4

4 4

5 ec

o+3

full

part

icipa

tion

perc

enta

ge

88%

87

%

82%

86

%

85%

87

%

89%

84

%

80%

75

%

eco+

3, n

o pa

rtici

patio

n 1

0 1

2 0

0 0

0 2

2 ec

o+3,

ove

rrid

e to

tal

13

14

20

14

17

17

14

20

24

30

eco+

3, o

verr

ide

at 0

~30

min

s 5

1 4

6 6

3 3

5 6

10

eco+

3, o

verr

ide

at 3

1-60

min

s 1

2 7

4 4

5 6

6 5

8 ec

o+3,

ove

rrid

e at

61-

90 m

ins

3 9

4 2

6 4

2 4

9 7

eco+

3, o

verr

ide

at 9

1-11

9 m

ins

4 2

5 2

1 5

3 5

4 5

eco+

4 - 1

00%

par

ticip

atio

n 87

6 83

8 83

4 81

2 76

5 91

7 92

4 89

0 88

9 82

1 ec

o+4

- tot

al

1041

10

41

1041

10

41

1041

10

86

1086

10

86

1095

10

95

eco+

4 - n

/a d

ata,

to e

xluc

e 25

25

25

25

25

24

24

24

24

24

ec

o+4,

miss

ing

row

>19

, to

exlu

cde

12

19

18

14

19

24

22

24

28

25

eco+

4 fu

ll pa

rtici

patio

n pe

rcen

tage

87

%

84%

84

%

81%

77

%

88%

89

%

86%

85

%

78%

ec

o+4,

no

part

icipa

tion

20

14

16

48

10

10

8 12

8

9 ec

o+4,

ove

rrid

e to

tal

109

145

148

142

222

111

108

137

146

216

eco+

4, o

verr

ide

at 0

~30

min

s 23

38

40

38

57

27

24

30

36

53

ec

o+4,

ove

rrid

e at

31-

60 m

ins

29

32

48

29

69

38

26

38

47

67

eco+

4, o

verr

ide

at 6

1-90

min

s 27

43

34

39

53

22

33

36

30

50

ec

o+4,

ove

rrid

e at

91-

119

min

s 30

32

26

36

43

24

25

33

33

46

ec

o+5

- 100

% p

artic

ipat

ion

117

122

108

106

106

133

132

123

129

119

eco+

5 - t

otal

13

9 13

9 13

9 13

9 13

9 14

8 14

8 14

8 15

1 15

1 ec

o+5

- n/a

dat

a, to

exl

uce

2 2

2 2

2 2

2 2

2 2

eco+

5, m

issin

g ro

w >

19, t

o ex

lucd

e 3

2 4

2 2

2 3

2 3

4 24

of 342


o+5

full

part

icipa

tion

perc

enta

ge

87%

90

%

81%

79

%

79%

92

%

92%

85

%

88%

82

%

eco+

5, n

o pa

rtici

patio

n 5

1 2

5 5

2 1

4 1

3 ec

o+5,

ove

rrid

e to

tal

12

12

23

24

24

9 10

17

16

23

ec

o+5,

ove

rrid

e at

0~3

0 m

ins

2 5

6 11

5

4 4

7 1

5 ec

o+5,

ove

rrid

e at

31-

60 m

ins

4 3

3 4

4 3

0 5

8 11

ec

o+5,

ove

rrid

e at

61-

90 m

ins

1 3

11

3 10

1

5 2

3 6

eco+

5, o

verr

ide

at 9

1-11

9 m

ins

5 1

3 6

5 1

1 3

4 1

no e

co+,

no

part

icipa

tion,

% o

f all

no e

co+

with

da

ta

2%

0%

1%

4%

1%

0%

1%

0%

0%

1%

no e

co+,

ove

rrid

e at

0~3

0 m

ins,

% o

f all

over

ride

24%

20

%

26%

23

%

27%

20

%

22%

20

%

34%

24

%

no e

co+,

ove

rrid

e at

31-

60 m

ins,

% o

f all

over

ride

13%

28

%

28%

23

%

22%

26

%

22%

27

%

20%

31

%

no e

co+,

ove

rrid

e at

61-

90 m

ins,

% o

f all

over

ride

28%

21

%

20%

27

%

31%

17

%

34%

24

%

25%

21

%

no e

co+,

ove

rrid

e at

91-

119

min

s, %

of a

ll ov

errid

e 35

%

31%

26

%

27%

19

%

37%

22

%

29%

20

%

23%

eco+

1, n

o pa

rtici

patio

n, %

of a

ll ec

o+1

with

dat

a 6%

0%

2%

6%

0%

1%

1%

1%

3%

3%

ec

o+1,

ove

rrid

e at

0~3

0 m

ins,

% o

f all

over

ride

33%

0%

0%

0%

50

%

0%

0%

33%

10

0%

0%

eco+

1, o

verr

ide

at 3

1-60

min

s, %

of a

ll ov

errid

e 17

%

0%

0%

0%

0%

0%

0%

0%

0%

60%

ec

o+1,

ove

rrid

e at

61-

90 m

ins,

% o

f all

over

ride

33%

0%

67

%

100%

25

%

0%

50%

67

%

0%

0%

eco+

1, o

verr

ide

at 9

1-11

9 m

ins,

% o

f all

over

ride

17%

0%

33

%

0%

25%

10

0%

50%

0%

0%

40

%

eco+

2, n

o pa

rtici

patio

n, %

of a

ll ec

o+2

with

dat

a 0%

3%

3%

10

%

10%

3%

3%

0%

0%

2%

ec

o+2,

ove

rrid

e at

0~3

0 m

ins,

% o

f all

over

ride

50%

0%

80

%

0%

25%

40

%

0%

14%

0%

33

%

eco+

2, o

verr

ide

at 3

1-60

min

s, %

of a

ll ov

errid

e 0%

25

%

0%

50%

25

%

60%

67

%

14%

50

%

33%

ec

o+2,

ove

rrid

e at

61-

90 m

ins,

% o

f all

over

ride

0%

75%

0%

50

%

0%

0%

0%

29%

0%

17

%

eco+

2, o

verr

ide

at 9

1-11

9 m

ins,

% o

f all

over

ride

50%

0%

20

%

0%

50%

0%

33

%

43%

50

%

17%

ec

o+3,

no

part

icipa

tion,

% o

f all

eco+

3 w

ith d

ata

1%

0%

1%

2%

0%

0%

0%

0%

2%

2%

eco+

3, o

verr

ide

at 0

~30

min

s, %

of a

ll ov

errid

e 38

%

7%

20%

43

%

35%

18

%

21%

25

%

25%

33

%

eco+

3, o

verr

ide

at 3

1-60

min

s, %

of a

ll ov

errid

e 8%

14

%

35%

29

%

24%

29

%

43%

30

%

21%

27

%

eco+

3, o

verr

ide

at 6

1-90

min

s, %

of a

ll ov

errid

e 23

%

64%

20

%

14%

35

%

24%

14

%

20%

38

%

23%

ec

o+3,

ove

rrid

e at

91-

119

min

s, %

of a

ll ov

errid

e 31

%

14%

25

%

14%

6%

29

%

21%

25

%

17%

17

%

eco+

4, n

o pa

rtici

patio

n, %

of a

ll ec

o+4

with

dat

a 2%

1%

2%

5%

1%

1%

1%

1%

1%

1%

ec

o+4,

ove

rrid

e at

0~3

0 m

ins,

% o

f all

over

ride

21%

26

%

27%

27

%

26%

24

%

22%

22

%

25%

25

%

eco+

4, o

verr

ide

at 3

1-60

min

s, %

of a

ll ov

errid

e 27

%

22%

32

%

20%

31

%

34%

24

%

28%

32

%

31%

ec

o+4,

ove

rrid

e at

61-

90 m

ins,

% o

f all

over

ride

25%

30

%

23%

27

%

24%

20

%

31%

26

%

21%

23

%

25

of 342


o+4,

ove

rrid

e at

91-

119

min

s, %

of a

ll ov

errid

e 28

%

22%

18

%

25%

19

%

22%

23

%

24%

23

%

21%

ec

o+5,

no

part

icipa

tion,

% o

f all

eco+

5 w

ith d

ata

4%

1%

2%

4%

4%

1%

1%

3%

1%

2%

eco+

5, o

verr

ide

at 0

~30

min

s, %

of a

ll ov

errid

e 17

%

42%

26

%

46%

21

%

44%

40

%

41%

6%

22

%

eco+

5, o

verr

ide

at 3

1-60

min

s, %

of a

ll ov

errid

e 33

%

25%

13

%

17%

17

%

33%

0%

29

%

50%

48

%

eco+

5, o

verr

ide

at 6

1-90

min

s, %

of a

ll ov

errid

e 8%

25

%

48%

13

%

42%

11

%

50%

12

%

19%

26

%

eco+

5, o

verr

ide

at 9

1-11

9 m

ins,

% o

f all

over

ride

42%

8%

13

%

25%

21

%

11%

10

%

18%

25

%

4%

26


of 342

SPPC

7/1

6/20

20 –

8/4

/202

0

16-Ju

l 17

-Jul

20-Ju

l 21

-Jul

27-Ju

l 29

-Jul

30-Ju

l 31

-Jul

3-Au

g 4-

Aug

Tota

l 22

43

2243

22

43

2243

24

09

2409

24

09

2409

24

09

2409

En

rolle

d 21

65

2165

21

65

2165

21

65

2165

21

65

2165

21

65

2165

10

0% p

artic

ipat

ion,

no

eco+

44

0 41

5 40

9 41

4 43

8 42

1 41

5 41

0 40

8 41

7 no

eco

+ to

tal

656

656

634

634

634

634

634

634

634

634

no e

co+

n/a

data

, to

exclu

de

46

46

46

46

46

46

46

46

46

48

no e

co+,

miss

ing

row

>19

, to

exlu

cde

96

95

94

95

96

95

97

95

100

95

no e

co+

full

part

icipa

tion

perc

enta

ge

86%

81

%

83%

84

%

89%

85

%

85%

83

%

84%

85

%

no e

co+,

no

part

icipa

tion

12

11

6 5

4 10

4

10

6 9

no e

co+,

ove

rrid

e to

tal

62

89

79

75

50

62

72

73

74

65

no e

co+,

ove

rrid

e at

0~3

0 m

ins

17

23

17

28

13

18

16

17

17

14

no e

co+,

ove

rrid

e at

31-

60 m

ins

18

27

24

17

14

11

18

21

35

22

no e

co+,

ove

rrid

e at

61-

90 m

ins

8 23

19

13

14

19

23

14

11

19

no

eco

+, o

verr

ide

at 9

1-11

9 m

ins

19

16

19

17

9 14

15

21

11

10

ec

o+1

- 100

% p

artic

ipat

ion

67

63

73

72

72

73

69

74

71

72

eco+

1 - t

otal

84

84

89

89

89

89

89

89

89

89

ec

o+1

- n/a

dat

a, to

exl

uce

7 7

8 8

8 8

8 8

8 8

eco+

1, m

issin

g ro

w >

19, t

o ex

lucd

e 5

5 5

5 5

6 5

5 5

5 ec

o+1

full

part

icipa

tion

perc

enta

ge

93%

88

%

96%

95

%

95%

97

%

91%

97

%

93%

95

%

eco+

1, n

o pa

rtici

patio

n 1

4 3

1 2

1 1

0 1

1 ec

o+1,

ove

rrid

e to

tal

4 5

0 3

2 1

6 2

4 3

eco+

1, o

verr

ide

at 0

~30

min

s 0

0 0

0 0

0 0

1 1

1 ec

o+1,

ove

rrid

e at

31-

60 m

ins

1 0

0 0

1 1

1 0

2 0

eco+

1, o

verr

ide

at 6

1-90

min

s 0

1 0

1 1

0 1

0 1

1 ec

o+1,

ove

rrid

e at

91-

119

min

s 3

4 0

2 0

0 4

1 0

1 ec

o+2

- 100

% p

artic

ipat

ion

38

31

32

33

39

41

40

32

39

39

eco+

2 - t

otal

42

42

45

45

45

45

45

45

45

45

ec

o+2

- n/a

dat

a, to

exl

uce

0 0

0 0

0 0

0 0

0 0

eco+

2, m

issin

g ro

w >

19, t

o ex

lucd

e 0

0 0

0 0

0 0

0 0

0 ec

o+2

full

part

icipa

tion

perc

enta

ge

90%

74

%

71%

73

%

87%

91

%

89%

71

%

87%

87

%

27

of 342


o+2,

no

part

icipa

tion

0 0

2 3

1 0

0 2

0 0

eco+

2, o

verr

ide

tota

l 4

11

11

9 5

4 5

11

6 6

eco+

2, o

verr

ide

at 0

~30

min

s 2

3 0

4 0

2 3

4 1

1 ec

o+2,

ove

rrid

e at

31-

60 m

ins

1 1

6 1

2 1

1 3

1 0

eco+

2, o

verr

ide

at 6

1-90

min

s 1

6 2

1 1

0 1

2 1

3 ec

o+2,

ove

rrid

e at

91-

119

min

s 0

1 3

3 2

1 0

2 3

2 ec

o+3

- 100

% p

artic

ipat

ion

106

108

114

111

116

105

108

103

108

114

eco+

3 - t

otal

13

7 13

7 14

4 14

4 14

4 14

4 14

4 14

4 14

4 14

4 ec

o+3

- n/a

dat

a, to

exl

uce

2 2

2 2

2 2

2 2

2 2

eco+

3, m

issin

g ro

w >

19, t

o ex

lucd

e 4

4 7

6 7

7 6

6 7

6 ec

o+3

full

part

icipa

tion

perc

enta

ge

81%

82

%

84%

82

%

86%

78

%

79%

76

%

80%

84

%

eco+

3, n

o pa

rtici

patio

n 2

2 2

3 0

1 3

5 2

5 ec

o+3,

ove

rrid

e to

tal

23

21

19

22

20

29

25

28

26

17

eco+

3, o

verr

ide

at 0

~30

min

s 3

3 6

5 4

9 6

9 3

5 ec

o+3,

ove

rrid

e at

31-

60 m

ins

6 9

6 4

9 14

7

5 13

5

eco+

3, o

verr

ide

at 6

1-90

min

s 8

6 5

5 4

4 4

9 6

6 ec

o+3,

ove

rrid

e at

91-

119

min

s 6

3 2

8 3

2 8

5 4

1 ec

o+4

- 100

% p

artic

ipat

ion

857

809

823

848

907

860

871

834

838

867

eco+

4 - t

otal

10

95

1095

11

01

1101

11

01

1101

11

01

1101

11

01

1101

ec

o+4

- n/a

dat

a, to

exl

uce

24

24

23

23

23

23

23

23

23

23

eco+

4, m

issin

g ro

w >

19, t

o ex

lucd

e 29

29

35

37

39

36

37

40

43

40

ec

o+4

full

part

icipa

tion

perc

enta

ge

82%

78

%

79%

81

%

87%

83

%

84%

80

%

81%

84

%

eco+

4, n

o pa

rtici

patio

n 18

19

21

13

14

12

21

23

21

20

ec

o+4,

ove

rrid

e to

tal

167

214

199

180

121

170

149

181

176

151

eco+

4, o

verr

ide

at 0

~30

min

s 54

65

69

47

29

42

42

42

51

41

ec

o+4,

ove

rrid

e at

31-

60 m

ins

37

59

58

42

32

42

36

48

52

42

eco+

4, o

verr

ide

at 6

1-90

min

s 39

47

44

52

33

56

40

41

36

37

ec

o+4,

ove

rrid

e at

91-

119

min

s 37

43

28

39

27

30

31

50

37

31

ec

o+5

- 100

% p

artic

ipat

ion

120

116

119

118

129

123

125

125

123

125

eco+

5 - t

otal

15

1 15

1 15

2 15

2 15

2 15

2 15

2 15

2 15

2 15

2 ec

o+5

- n/a

dat

a, to

exl

uce

2 2

2 2

2 2

2 2

3 3

eco+

5, m

issin

g ro

w >

19, t

o ex

lucd

e 4

5 7

5 5

1 1

3 5

3 ec

o+5

full

part

icipa

tion

perc

enta

ge

83%

81

%

83%

81

%

89%

83

%

84%

85

%

85%

86

%

28

of 342


o+5,

no

part

icipa

tion

3 3

3 1

2 7

1 4

4 4

eco+

5, o

verr

ide

tota

l 22

25

21

26

14

19

23

18

18

17

ec

o+5,

ove

rrid

e at

0~3

0 m

ins

4 4

11

10

5 5

5 5

10

5 ec

o+5,

ove

rrid

e at

31-

60 m

ins

3 8

4 1

4 10

6

4 4

6 ec

o+5,

ove

rrid

e at

61-

90 m

ins

5 9

6 8

1 2

5 5

3 2

eco+

5, o

verr

ide

at 9

1-11

9 m

ins

10

4 0

7 4

2 7

4 1

4 no

eco

+, n

o pa

rtici

patio

n, %

of a

ll no

eco

+ w

ith

data

2%

2%

1%

1%

1%

2%

1%

2%

1%

2%

no e

co+,

ove

rrid

e at

0~3

0 m

ins,

% o

f all

over

ride

27%

26

%

22%

37

%

26%

29

%

22%

23

%

23%

22

%

no e

co+,

ove

rrid

e at

31-

60 m

ins,

% o

f all

over

ride

29%

30

%

30%

23

%

28%

18

%

25%

29

%

47%

34

%

no e

co+,

ove

rrid

e at

61-

90 m

ins,

% o

f all

over

ride

13%

26

%

24%

17

%

28%

31

%

32%

19

%

15%

29

%

no e

co+,

ove

rrid

e at

91-

119

min

s, %

of a

ll ov

errid

e 31

%

18%

24

%

23%

18

%

23%

21

%

29%

15

%

15%

eco+

1, n

o pa

rtici

patio

n, %

of a

ll ec

o+1

with

dat

a 1%

6%

4%

1%

3%

1%

1%

0%

1%

1%

ec

o+1,

ove

rrid

e at

0~3

0 m

ins,

% o

f all

over

ride

0%

0%

0%

0%

0%

0%

0%

50%

25

%

33%

ec

o+1,

ove

rrid

e at

31-

60 m

ins,

% o

f all

over

ride

25%

0%

0%

0%

50

%

100%

17

%

0%

50%

0%

ec

o+1,

ove

rrid

e at

61-

90 m

ins,

% o

f all

over

ride

0%

20%

0%

33

%

50%

0%

17

%

0%

25%

33

%

eco+

1, o

verr

ide

at 9

1-11

9 m

ins,

% o

f all

over

ride

75%

80

%

0%

67%

0%

0%

67

%

50%

0%

33

%

eco+

2, n

o pa

rtici

patio

n, %

of a

ll ec

o+2

with

dat

a 0%

0%

4%

7%

2%

0%

0%

4%

0%

0%

ec

o+2,

ove

rrid

e at

0~3

0 m

ins,

% o

f all

over

ride

50%

27

%

0%

44%

0%

50

%

60%

36

%

17%

17

%

eco+

2, o

verr

ide

at 3

1-60

min

s, %

of a

ll ov

errid

e 25

%

9%

55%

11

%

40%

25

%

20%

27

%

17%

0%

ec

o+2,

ove

rrid

e at

61-

90 m

ins,

% o

f all

over

ride

25%

55

%

18%

11

%

20%

0%

20

%

18%

17

%

50%

ec

o+2,

ove

rrid

e at

91-

119

min

s, %

of a

ll ov

errid

e 0%

9%

27

%

33%

40

%

25%

0%

18

%

50%

33

%

eco+

3, n

o pa

rtici

patio

n, %

of a

ll ec

o+3

with

dat

a 2%

2%

1%

2%

0%

1%

2%

4%

1%

4%

ec

o+3,

ove

rrid

e at

0~3

0 m

ins,

% o

f all

over

ride

13%

14

%

32%

23

%

20%

31

%

24%

32

%

12%

29

%

eco+

3, o

verr

ide

at 3

1-60

min

s, %

of a

ll ov

errid

e 26

%

43%

32

%

18%

45

%

48%

28

%

18%

50

%

29%

ec

o+3,

ove

rrid

e at

61-

90 m

ins,

% o

f all

over

ride

35%

29

%

26%

23

%

20%

14

%

16%

32

%

23%

35

%

eco+

3, o

verr

ide

at 9

1-11

9 m

ins,

% o

f all

over

ride

26%

14

%

11%

36

%

15%

7%

32

%

18%

15

%

6%

eco+

4, n

o pa

rtici

patio

n, %

of a

ll ec

o+4

with

dat

a 2%

2%

2%

1%

1%

1%

2%

2%

2%

2%

ec

o+4,

ove

rrid

e at

0~3

0 m

ins,

% o

f all

over

ride

32%

30

%

35%

26

%

24%

25

%

28%

23

%

29%

27

%

eco+

4, o

verr

ide

at 3

1-60

min

s, %

of a

ll ov

errid

e 22

%

28%

29

%

23%

26

%

25%

24

%

27%

30

%

28%

ec

o+4,

ove

rrid

e at

61-

90 m

ins,

% o

f all

over

ride

23%

22

%

22%

29

%

27%

33

%

27%

23

%

20%

25

%

eco+

4, o

verr

ide

at 9

1-11

9 m

ins,

% o

f all

over

ride

22%

20

%

14%

22

%

22%

18

%

21%

28

%

21%

21

%

29

of 342


o+5,

no

part

icipa

tion,

% o

f all

eco+

5 w

ith d

ata

2%

2%

2%

1%

1%

5%

1%

3%

3%

3%

eco+

5, o

verr

ide

at 0

~30

min

s, %

of a

ll ov

errid

e 18

%

16%

52

%

38%

36

%

26%

22

%

28%

56

%

29%

ec

o+5,

ove

rrid

e at

31-

60 m

ins,

% o

f all

over

ride

14%

32

%

19%

4%

29

%

53%

26

%

22%

22

%

35%

ec

o+5,

ove

rrid

e at

61-

90 m

ins,

% o

f all

over

ride

23%

36

%

29%

31

%

7%

11%

22

%

28%

17

%

12%

ec

o+5,

ove

rrid

e at

91-

119

min

s, %

of a

ll ov

errid

e 45

%

16%

0%

27

%

29%

11

%

30%

22

%

6%

24%

30


of 342

SPPC

8/5

/202

0 –

9/2/

2020

5-Au

g 10

-Aug

13

-Aug

14

-Aug

16

-Aug

17

-Aug

20

-Aug

24

-Aug

28

-Aug

2-

Sep

Tota

l 24

09

2409

24

09

2409

24

09

2409

24

09

2409

24

09

2409

En

rolle

d 21

65

2165

21

65

2165

21

44

2144

21

44

2144

21

44

2128

10

0% p

artic

ipat

ion,

no

eco+

40

8 41

1 42

6 41

4 38

1 37

5 36

1 39

0 38

4 35

4 no

eco

+ to

tal

634

634

634

634

594

594

594

594

594

560

no e

co+

n/a

data

, to

exclu

de

48

46

46

46

45

44

44

44

44

44

no e

co+,

miss

ing

row

>19

, to

exlu

cde

95

101

99

105

96

95

96

98

97

85

no e

co+

full

part

icipa

tion

perc

enta

ge

83%

84

%

87%

86

%

84%

82

%

80%

86

%

85%

82

%

no e

co+,

no

part

icipa

tion

31

5 8

4 9

6 4

8 6

19

no e

co+,

ove

rrid

e to

tal

52

71

55

65

63

74

89

54

63

58

no e

co+,

ove

rrid

e at

0~3

0 m

ins

16

18

10

19

18

17

12

8 19

15

no

eco

+, o

verr

ide

at 3

1-60

min

s 13

19

16

14

15

27

16

15

14

19

no

eco

+, o

verr

ide

at 6

1-90

min

s 9

19

16

12

19

13

15

21

12

11

no e

co+,

ove

rrid

e at

91-

119

min

s 14

15

13

20

11

17

46

10

18

13

ec

o+1

- 100

% p

artic

ipat

ion

63

71

70

73

72

69

72

80

75

81

eco+

1 - t

otal

89

89

89

89

98

98

98

98

98

10

9 ec

o+1

- n/a

dat

a, to

exl

uce

8 8

8 8

8 8

8 8

8 8

eco+

1, m

issin

g ro

w >

19, t

o ex

lucd

e 5

6 6

6 7

8 8

8 7

6 ec

o+1

full

part

icipa

tion

perc

enta

ge

83%

95

%

93%

97

%

87%

84

%

88%

98

%

90%

85

%

eco+

1, n

o pa

rtici

patio

n 7

2 1

0 6

5 4

0 2

8 ec

o+1,

ove

rrid

e to

tal

6 2

4 2

5 8

6 3

6 6

eco+

1, o

verr

ide

at 0

~30

min

s 2

1 1

1 1

0 0

1 0

3 ec

o+1,

ove

rrid

e at

31-

60 m

ins

0 1

0 0

3 2

1 0

2 1

eco+

1, o

verr

ide

at 6

1-90

min

s 1

0 0

1 0

1 1

1 1

1 ec

o+1,

ove

rrid

e at

91-

119

min

s 3

0 3

0 1

5 4

1 3

1 ec

o+2

- 100

% p

artic

ipat

ion

36

35

36

35

38

43

45

44

42

46

eco+

2 - t

otal

45

45

45

45

54

54

54

54

54

57

ec

o+2

- n/a

dat

a, to

exl

uce

0 0

0 0

0 0

0 0

0 0

eco+

2, m

issin

g ro

w >

19, t

o ex

lucd

e 0

1 1

1 2

2 2

4 3

2 ec

o+2

full

part

icipa

tion

perc

enta

ge

80%

80

%

82%

80

%

73%

83

%

87%

88

%

82%

84

%

31

of 342


o+2,

no

part

icipa

tion

2 2

1 1

2 1

0 1

2 3

eco+

2, o

verr

ide

tota

l 7

7 7

8 12

8

7 6

7 6

eco+

2, o

verr

ide

at 0

~30

min

s 1

3 3

4 5

1 1

2 3

1 ec

o+2,

ove

rrid

e at

31-

60 m

ins

4 1

2 1

5 0

3 1

3 1

eco+

2, o

verr

ide

at 6

1-90

min

s 2

1 0

1 1

4 1

2 0

2 ec

o+2,

ove

rrid

e at

91-

119

min

s 0

2 2

2 1

3 2

1 1

2 ec

o+3

- 100

% p

artic

ipat

ion

107

110

117

100

117

125

116

120

118

121

eco+

3 - t

otal

14

4 14

4 14

4 14

4 15

7 15

7 15

7 15

7 15

7 15

9 ec

o+3

- n/a

dat

a, to

exl

uce

2 2

2 2

2 2

2 2

2 2

eco+

3, m

issin

g ro

w >

19, t

o ex

lucd

e 6

6 7

7 7

6 7

7 8

6 ec

o+3

full

part

icipa

tion

perc

enta

ge

79%

81

%

87%

74

%

79%

84

%

78%

81

%

80%

80

%

eco+

3, n

o pa

rtici

patio

n 8

2 1

3 4

1 4

0 2

10

eco+

3, o

verr

ide

tota

l 21

24

17

32

27

23

28

28

28

20

ec

o+3,

ove

rrid

e at

0~3

0 m

ins

4 8

1 9

10

4 4

8 6

3 ec

o+3,

ove

rrid

e at

31-

60 m

ins

6 8

6 8

8 9

8 7

4 9

eco+

3, o

verr

ide

at 6

1-90

min

s 5

4 2

9 5

2 6

7 10

3

eco+

3, o

verr

ide

at 9

1-11

9 m

ins

6 4

8 6

4 8

10

6 8

5 ec

o+4

- 100

% p

artic

ipat

ion

818

859

884

838

808

840

823

835

834

842

eco+

4 - t

otal

11

01

1101

11

01

1101

10

89

1089

10

89

1089

10

89

1085

ec

o+4

- n/a

dat

a, to

exl

uce

23

23

23

23

23

23

23

23

23

23

eco+

4, m

issin

g ro

w >

19, t

o ex

lucd

e 45

45

45

43

34

42

38

34

46

38

ec

o+4

full

part

icipa

tion

perc

enta

ge

79%

83

%

86%

81

%

78%

82

%

80%

81

%

82%

82

%

eco+

4, n

o pa

rtici

patio

n 69

11

7

18

15

18

18

25

14

33

eco+

4, o

verr

ide

tota

l 14

8 16

3 14

2 18

0 21

0 16

8 18

8 17

2 17

3 14

9 ec

o+4,

ove

rrid

e at

0~3

0 m

ins

31

44

37

40

62

46

41

49

36

30

eco+

4, o

verr

ide

at 3

1-60

min

s 38

39

31

51

51

41

35

46

43

46

ec

o+4,

ove

rrid

e at

61-

90 m

ins

39

42

40

44

54

42

40

39

56

45

eco+

4, o

verr

ide

at 9

1-11

9 m

ins

40

38

34

45

43

39

72

38

38

28

eco+

5 - 1

00%

par

ticip

atio

n 12

2 12

4 12

7 12

1 11

7 12

6 12

0 12

8 13

1 12

7 ec

o+5

- tot

al

152

152

152

152

152

152

152

152

152

158

eco+

5 - n

/a d

ata,

to e

xluc

e 3

3 3

3 2

2 2

2 2

2 ec

o+5,

miss

ing

row

>19

, to

exlu

cde

4 4

2 5

4 4

3 6

6 13

ec

o+5

full

part

icipa

tion

perc

enta

ge

84%

86

%

86%

84

%

80%

86

%

82%

89

%

91%

89

%

32

of 342


o+5,

no

part

icipa

tion

9 3

4 1

3 3

1 3

0 2

eco+

5, o

verr

ide

tota

l 14

19

16

23

26

17

26

13

14

14

ec

o+5,

ove

rrid

e at

0~3

0 m

ins

4 7

4 4

6 2

7 6

1 2

eco+

5, o

verr

ide

at 3

1-60

min

s 1

6 5

6 5

6 3

1 3

7 ec

o+5,

ove

rrid

e at

61-

90 m

ins

6 1

5 7

7 4

5 2

7 2

eco+

5, o

verr

ide

at 9

1-11

9 m

ins

3 5

2 6

8 5

11

4 3

3 no

eco

+, n

o pa

rtici

patio

n, %

of a

ll no

eco

+ w

ith

data

6%

1%

2%

1%

2%

1%

1%

2%

1%

4%

no e

co+,

ove

rrid

e at

0~3

0 m

ins,

% o

f all

over

ride

31%

25

%

18%

29

%

29%

23

%

13%

15

%

30%

26

%

no e

co+,

ove

rrid

e at

31-

60 m

ins,

% o

f all

over

ride

25%

27

%

29%

22

%

24%

36

%

18%

28

%

22%

33

%

no e

co+,

ove

rrid

e at

61-

90 m

ins,

% o

f all

over

ride

17%

27

%

29%

18

%

30%

18

%

17%

39

%

19%

19

%

no e

co+,

ove

rrid

e at

91-

119

min

s, %

of a

ll ov

errid

e 27

%

21%

24

%

31%

17

%

23%

52

%

19%

29

%

22%

eco+

1, n

o pa

rtici

patio

n, %

of a

ll ec

o+1

with

dat

a 9%

3%

1%

0%

7%

6%

5%

0%

2%

8%

ec

o+1,

ove

rrid

e at

0~3

0 m

ins,

% o

f all

over

ride

33%

50

%

25%

50

%

20%

0%

0%

33

%

0%

50%

ec

o+1,

ove

rrid

e at

31-

60 m

ins,

% o

f all

over

ride

0%

50%

0%

0%

60

%

25%

17

%

0%

33%

17

%

eco+

1, o

verr

ide

at 6

1-90

min

s, %

of a

ll ov

errid

e 17

%

0%

0%

50%

0%

13

%

17%

33

%

17%

17

%

eco+

1, o

verr

ide

at 9

1-11

9 m

ins,

% o

f all

over

ride

50%

0%

75

%

0%

20%

63

%

67%

33

%

50%

17

%

eco+

2, n

o pa

rtici

patio

n, %

of a

ll ec

o+2

with

dat

a 4%

5%

2%

2%

4%

2%

0%

2%

4%

5%

ec

o+2,

ove

rrid

e at

0~3

0 m

ins,

% o

f all

over

ride

14%

43

%

43%

50

%

42%

13

%

14%

33

%

43%

17

%

eco+

2, o

verr

ide

at 3

1-60

min

s, %

of a

ll ov

errid

e 57

%

14%

29

%

13%

42

%

0%

43%

17

%

43%

17

%

eco+

2, o

verr

ide

at 6

1-90

min

s, %

of a

ll ov

errid

e 29

%

14%

0%

13

%

8%

50%

14

%

33%

0%

33

%

eco+

2, o

verr

ide

at 9

1-11

9 m

ins,

% o

f all

over

ride

0%

29%

29

%

25%

8%

38

%

29%

17

%

14%

33

%

eco+

3, n

o pa

rtici

patio

n, %

of a

ll ec

o+3

with

dat

a 6%

1%

1%

2%

3%

1%

3%

0%

1%

7%

ec

o+3,

ove

rrid

e at

0~3

0 m

ins,

% o

f all

over

ride

19%

33

%

6%

28%

37

%

17%

14

%

29%

21

%

15%

ec

o+3,

ove

rrid

e at

31-

60 m

ins,

% o

f all

over

ride

29%

33

%

35%

25

%

30%

39

%

29%

25

%

14%

45

%

eco+

3, o

verr

ide

at 6

1-90

min

s, %

of a

ll ov

errid

e 24

%

17%

12

%

28%

19

%

9%

21%

25

%

36%

15

%

eco+

3, o

verr

ide

at 9

1-11

9 m

ins,

% o

f all

over

ride

29%

17

%

47%

19

%

15%

35

%

36%

21

%

29%

25

%

eco+

4, n

o pa

rtici

patio

n, %

of a

ll ec

o+4

with

dat

a 7%

1%

1%

2%

1%

2%

2%

2%

1%

3%

ec

o+4,

ove

rrid

e at

0~3

0 m

ins,

% o

f all

over

ride

21%

27

%

26%

22

%

30%

27

%

22%

28

%

21%

20

%

eco+

4, o

verr

ide

at 3

1-60

min

s, %

of a

ll ov

errid

e 26

%

24%

22

%

28%

24

%

24%

19

%

27%

25

%

31%

ec

o+4,

ove

rrid

e at

61-

90 m

ins,

% o

f all

over

ride

26%

26

%

28%

24

%

26%

25

%

21%

23

%

32%

30

%

eco+

4, o

verr

ide

at 9

1-11

9 m

ins,

% o

f all

over

ride

27%

23

%

24%

25

%

20%

23

%

38%

22

%

22%

19

%

33

of 342


o+5,

no

part

icipa

tion,

% o

f all

eco+

5 w

ith d

ata

6%

2%

3%

1%

2%

2%

1%

2%

0%

1%

eco+

5, o

verr

ide

at 0

~30

min

s, %

of a

ll ov

errid

e 29

%

37%

25

%

17%

23

%

12%

27

%

46%

7%

14

%

eco+

5, o

verr

ide

at 3

1-60

min

s, %

of a

ll ov

errid

e 7%

32

%

31%

26

%

19%

35

%

12%

8%

21

%

50%

ec

o+5,

ove

rrid

e at

61-

90 m

ins,

% o

f all

over

ride

43%

5%

31

%

30%

27

%

24%

19

%

15%

50

%

14%

ec

o+5,

ove

rrid

e at

91-

119

min

s, %

of a

ll ov

errid

e 21

%

26%

13

%

26%

31

%

29%

42

%

31%

21

%

21%

34


of 342

SPPC

9/3

/202

0 –

9/7/

2020

3-Se

p 4-

Sep

5-Se

p 6-

Sep

7-Se

p To

tal

2409

24

09

2409

24

09

2409

En

rolle

d 21

28

2128

21

28

2128

21

28

100%

par

ticip

atio

n, n

o ec

o+

369

354

361

365

361

no e

co+

tota

l 56

0 56

0 56

0 56

0 56

0 no

eco

+ n/

a da

ta, t

o ex

clude

44

44

44

44

44

no

eco

+, m

issin

g ro

w >

19, t

o ex

lucd

e 86

87

87

85

85

no

eco

+ fu

ll pa

rtici

patio

n pe

rcen

tage

86

%

83%

84

%

85%

84

%

no e

co+,

no

part

icipa

tion

6 5

4 7

7 no

eco

+, o

verr

ide

tota

l 55

70

64

59

64

no

eco

+, o

verr

ide

at 0

~30

min

s 14

18

21

16

13

no

eco

+, o

verr

ide

at 3

1-60

min

s 15

25

15

11

11

no

eco

+, o

verr

ide

at 6

1-90

min

s 6

14

14

17

20

no e

co+,

ove

rrid

e at

91-

119

min

s 20

13

14

15

20

ec

o+1

- 100

% p

artic

ipat

ion

81

84

76

80

80

eco+

1 - t

otal

10

9 10

9 10

9 10

9 10

9 ec

o+1

- n/a

dat

a, to

exl

uce

8 8

8 8

8 ec

o+1,

miss

ing

row

>19

, to

exlu

cde

6 6

6 6

6 ec

o+1

full

part

icipa

tion

perc

enta

ge

85%

88

%

80%

84

%

84%

ec

o+1,

no

part

icipa

tion

5 4

5 6

7 ec

o+1,

ove

rrid

e to

tal

9 7

14

9 8

eco+

1, o

verr

ide

at 0

~30

min

s 4

1 6

3 2

eco+

1, o

verr

ide

at 3

1-60

min

s 2

2 3

1 1

eco+

1, o

verr

ide

at 6

1-90

min

s 1

3 5

4 5

eco+

1, o

verr

ide

at 9

1-11

9 m

ins

2 1

0 1

0 ec

o+2

- 100

% p

artic

ipat

ion

44

46

46

43

46

eco+

2 - t

otal

57

57

57

57

57

ec

o+2

- n/a

dat

a, to

exl

uce

0 0

0 0

0 ec

o+2,

miss

ing

row

>19

, to

exlu

cde

2 2

2 2

2 ec

o+2

full

part

icipa

tion

perc

enta

ge

80%

84

%

84%

78

%

84%

35

of 342


o+2,

no

part

icipa

tion

1 2

2 1

0 ec

o+2,

ove

rrid

e to

tal

10

7 7

11

9 ec

o+2,

ove

rrid

e at

0~3

0 m

ins

0 4

3 5

3 ec

o+2,

ove

rrid

e at

31-

60 m

ins

4 0

1 3

4 ec

o+2,

ove

rrid

e at

61-

90 m

ins

3 1

2 3

1 ec

o+2,

ove

rrid

e at

91-

119

min

s 3

2 1

0 1

eco+

3 - 1

00%

par

ticip

atio

n 11

5 11

7 12

0 11

9 12

4 ec

o+3

- tot

al

159

159

159

159

159

eco+

3 - n

/a d

ata,

to e

xluc

e 2

2 2

2 2

eco+

3, m

issin

g ro

w >

19, t

o ex

lucd

e 6

6 6

6 6

eco+

3 fu

ll pa

rtici

patio

n pe

rcen

tage

76

%

77%

79

%

79%

82

%

eco+

3, n

o pa

rtici

patio

n 1

3 5

3 3

eco+

3, o

verr

ide

tota

l 35

31

26

29

24

ec

o+3,

ove

rrid

e at

0~3

0 m

ins

7 10

9

4 11

ec

o+3,

ove

rrid

e at

31-

60 m

ins

9 11

8

11

7 ec

o+3,

ove

rrid

e at

61-

90 m

ins

11

3 5

6 5

eco+

3, o

verr

ide

at 9

1-11

9 m

ins

8 7

4 8

1 ec

o+4

- 100

% p

artic

ipat

ion

840

818

823

841

828

eco+

4 - t

otal

10

85

1085

10

85

1085

10

85

eco+

4 - n

/a d

ata,

to e

xluc

e 23

23

23

23

23

ec

o+4,

miss

ing

row

>19

, to

exlu

cde

45

43

47

43

46

eco+

4 fu

ll pa

rtici

patio

n pe

rcen

tage

83

%

80%

81

%

83%

81

%

eco+

4, n

o pa

rtici

patio

n 14

12

9

14

13

eco+

4, o

verr

ide

tota

l 16

4 18

9 18

3 16

4 17

5 ec

o+4,

ove

rrid

e at

0~3

0 m

ins

48

56

50

44

55

eco+

4, o

verr

ide

at 3

1-60

min

s 38

43

63

43

58

ec

o+4,

ove

rrid

e at

61-

90 m

ins

50

48

44

44

33

eco+

4, o

verr

ide

at 9

1-11

9 m

ins

28

42

26

33

29

eco+

5 - 1

00%

par

ticip

atio

n 11

8 12

1 12

6 12

4 13

1 ec

o+5

- tot

al

158

158

158

158

158

eco+

5 - n

/a d

ata,

to e

xluc

e 2

2 2

2 2

eco+

5, m

issin

g ro

w >

19, t

o ex

lucd

e 11

10

9

10

11

eco+

5 fu

ll pa

rtici

patio

n pe

rcen

tage

81

%

83%

86

%

85%

90

%

36

of 342


o+5,

no

part

icipa

tion

3 3

1 3

0 ec

o+5,

ove

rrid

e to

tal

24

22

20

19

14

eco+

5, o

verr

ide

at 0

~30

min

s 6

8 8

2 3

eco+

5, o

verr

ide

at 3

1-60

min

s 6

6 5

8 4

eco+

5, o

verr

ide

at 6

1-90

min

s 5

3 2

5 2

eco+

5, o

verr

ide

at 9

1-11

9 m

ins

7 5

5 4

5 no

eco

+, n

o pa

rtici

patio

n, %

of a

ll no

eco

+ w

ith

data

1%

1%

1%

2%

2%

no e

co+,

ove

rrid

e at

0~3

0 m

ins,

% o

f all

over

ride

25%

26

%

33%

27

%

20%

no

eco

+, o

verr

ide

at 3

1-60

min

s, %

of a

ll ov

errid

e 27

%

36%

23

%

19%

17

%

no e

co+,

ove

rrid

e at

61-

90 m

ins,

% o

f all

over

ride

11%

20

%

22%

29

%

31%

no

eco

+, o

verr

ide

at 9

1-11

9 m

ins,

% o

f all

over

ride

36%

19

%

22%

25

%

31%

eco+

1, n

o pa

rtici

patio

n, %

of a

ll ec

o+1

with

dat

a 5%

4%

5%

6%

7%

ec

o+1,

ove

rrid

e at

0~3

0 m

ins,

% o

f all

over

ride

44%

14

%

43%

33

%

25%

ec

o+1,

ove

rrid

e at

31-

60 m

ins,

% o

f all

over

ride

22%

29

%

21%

11

%

13%

ec

o+1,

ove

rrid

e at

61-

90 m

ins,

% o

f all

over

ride

11%

43

%

36%

44

%

63%

ec

o+1,

ove

rrid

e at

91-

119

min

s, %

of a

ll ov

errid

e 22

%

14%

0%

11

%

0%

eco+

2, n

o pa

rtici

patio

n, %

of a

ll ec

o+2

with

dat

a 2%

4%

4%

2%

0%

ec

o+2,

ove

rrid

e at

0~3

0 m

ins,

% o

f all

over

ride

0%

57%

43

%

45%

33

%

eco+

2, o

verr

ide

at 3

1-60

min

s, %

of a

ll ov

errid

e 40

%

0%

14%

27

%

44%

ec

o+2,

ove

rrid

e at

61-

90 m

ins,

% o

f all

over

ride

30%

14

%

29%

27

%

11%

ec

o+2,

ove

rrid

e at

91-

119

min

s, %

of a

ll ov

errid

e 30

%

29%

14

%

0%

11%

ec

o+3,

no

part

icipa

tion,

% o

f all

eco+

3 w

ith d

ata

1%

2%

3%

2%

2%

eco+

3, o

verr

ide

at 0

~30

min

s, %

of a

ll ov

errid

e 20

%

32%

35

%

14%

46

%

eco+

3, o

verr

ide

at 3

1-60

min

s, %

of a

ll ov

errid

e 26

%

35%

31

%

38%

29

%

eco+

3, o

verr

ide

at 6

1-90

min

s, %

of a

ll ov

errid

e 31

%

10%

19

%

21%

21

%

eco+

3, o

verr

ide

at 9

1-11

9 m

ins,

% o

f all

over

ride

23%

23

%

15%

28

%

4%

eco+

4, n

o pa

rtici

patio

n, %

of a

ll ec

o+4

with

dat

a 1%

1%

1%

1%

1%

ec

o+4,

ove

rrid

e at

0~3

0 m

ins,

% o

f all

over

ride

29%

30

%

27%

27

%

31%

ec

o+4,

ove

rrid

e at

31-

60 m

ins,

% o

f all

over

ride

23%

23

%

34%

26

%

33%

ec

o+4,

ove

rrid

e at

61-

90 m

ins,

% o

f all

over

ride

30%

25

%

24%

27

%

19%

ec

o+4,

ove

rrid

e at

91-

119

min

s, %

of a

ll ov

errid

e 17

%

22%

14

%

20%

17

%

37

of 342


o+5,

no

part

icipa

tion,

% o

f all

eco+

5 w

ith d

ata

2%

2%

1%

2%

0%

eco+

5, o

verr

ide

at 0

~30

min

s, %

of a

ll ov

errid

e 25

%

36%

40

%

11%

21

%

eco+

5, o

verr

ide

at 3

1-60

min

s, %

of a

ll ov

errid

e 25

%

27%

25

%

42%

29

%

eco+

5, o

verr

ide

at 6

1-90

min

s, %

of a

ll ov

errid

e 21

%

14%

10

%

26%

14

%

eco+

5, o

verr

ide

at 9

1-11

9 m

ins,

% o

f all

over

ride

29%

23

%

25%

21

%

36%

38

of 342


Appe

ndix

C –

NPC

201

9 DR

par

ticip

atio

n su

mm

ary

NPC

6/1

1/20

19 –

7/1

9/20

19

DR D

ate

6/11

/201

9 6/

12/2

019

7/10

/201

9 7/

11/2

019

7/12

/201

9 7/

15/2

019

7/16

/201

9 7/

17/2

019

7/19

/201

9 To

tal

9970

99

70

9970

99

70

9970

99

70

9970

99

70

9970

n/

a 81

86

8186

63

48

6343

63

42

6342

63

40

6341

63

35

Enro

lled

1784

17

84

3622

36

27

3628

36

28

3630

36

29

3635

10

0% p

artic

ipat

ion

1450

14

24

2379

24

11

2463

24

03

2532

25

68

2668

m

issin

g ro

w >

19, t

o ex

lucd

e 79

78

73

3 68

7 64

9 57

8 53

9 48

7 41

1 fu

ll pa

rtici

patio

n pe

rcen

tage

85

%

83%

82

%

82%

83

%

79%

82

%

82%

83

%

no p

artic

ipat

ion

71

64

142

119

150

214

138

127

100

over

ride

tota

l 18

4 21

8 36

8 41

1 36

6 43

3 42

1 44

7 45

6 ov

errid

e at

0~3

0 m

ins

36

52

89

122

126

129

143

131

140

over

ride

at 3

1-60

min

s 58

71

97

90

98

12

0 94

14

1 12

0 ov

errid

e at

61-

90 m

ins

48

45

102

117

72

107

98

98

112

over

ride

at 9

1-11

9 m

ins

42

50

80

82

70

77

86

77

84

no p

artic

ipat

ion,

% o

f all

no w

ith d

ata

4%

4%

5%

4%

5%

7%

4%

4%

3%

over

ride

at 0

~30

min

s, %

of a

ll ov

errid

e 20

%

24%

24

%

30%

34

%

30%

34

%

29%

31

%

over

ride

at 3

1-60

min

s, %

of a

ll ov

errid

e 32

%

33%

26

%

22%

27

%

28%

22

%

32%

26

%

over

ride

at 6

1-90

min

s, %

of a

ll ov

errid

e 26

%

21%

28

%

28%

20

%

25%

23

%

22%

25

%

over

ride

at 9

1-11

9 m

ins,

% o

f all

over

ride

23%

23

%

22%

20

%

19%

18

%

20%

17

%

18%

39

of 342


NPC

7/2

2/20

19 –

8/1

5/20

19

DR D

ate

7/22

/201

9 7/

26/2

019

7/29

/201

9 7/

30/2

019

8/5/

2019

8/

6/20

19

8/13

/201

9 8/

14/2

019

8/15

/201

9 To

tal

9970

99

70

9970

99

70

9970

99

70

9970

99

70

9970

n/

a 63

33

6324

60

42

6042

59

19

5919

64

91

6491

64

91

Enro

lled

3637

36

46

3928

39

28

4051

40

51

3479

34

79

3479

10

0% p

artic

ipat

ion

2665

13

97

2795

29

06

3010

31

39

2584

26

35

2691

m

issin

g ro

w >

19, t

o ex

lucd

e 33

3 17

52

410

362

252

223

163

162

164

full

part

icipa

tion

perc

enta

ge

81%

74

%

79%

81

%

79%

82

%

78%

79

%

81%

no

par

ticip

atio

n 18

4 80

17

4 95

16

9 19

7 18

5 15

2 36

ov

errid

e to

tal

455

545

549

565

620

492

547

533

588

over

ride

at 0

~30

min

s 14

3 90

17

3 19

5 23

0 13

4 15

9 17

1 19

5 ov

errid

e at

31-

60 m

ins

121

113

157

130

180

128

158

154

194

over

ride

at 6

1-90

min

s 10

5 15

6 11

9 13

8 12

7 12

9 12

5 11

6 11

8 ov

errid

e at

91-

119

min

s 86

18

6 10

0 10

2 83

10

1 10

5 92

81

no

par

ticip

atio

n, %

of a

ll no

with

dat

a 6%

4%

5%

3%

4%

5%

6%

5%

1%

ov

errid

e at

0~3

0 m

ins,

% o

f all

over

ride

31%

17

%

32%

35

%

37%

27

%

29%

32

%

33%

over

ride

at 3

1-60

min

s, %

of a

ll ov

errid

e 27

%

21%

29

%

23%

29

%

26%

29

%

29%

33

%

over

ride

at 6

1-90

min

s, %

of a

ll ov

errid

e 23

%

29%

22

%

24%

20

%

26%

23

%

22%

20

%

over

ride

at 9

1-11

9 m

ins,

% o

f all

over

ride

19%

34

%

18%

18

%

13%

21

%

19%

17

%

14%

40

of 342


NPC

8/2

0/20

19 –

9/4

/201

9 DR

Dat

e 8/

20/2

01

9 8/

21/2

01

9 8/

22/2

01

9 8/

26/2

01

9 8/

27/2

01

9 8/

28/2

01

9 8/

29/2

01

9 8/

30/2

01

9 9/

3/20

1 9

9/4/

201

9 To

tal

9970

99

70

9970

99

70

9970

99

70

9970

99

70

9970

99

70

n/a

5697

56

98

5697

48

45

4843

48

42

4843

48

45

4549

45

45

Enro

lled

4273

42

72

4273

51

25

5127

51

28

5127

51

25

5421

54

25

100%

par

ticip

atio

n 33

59

3331

31

58

3289

32

90

3320

36

55

3656

37

30

3656

m

issin

g ro

w >

19, t

o ex

lucd

e 16

5 16

8 16

9 65

6 62

9 57

3 54

0 50

0 70

5 64

5 fu

ll pa

rtici

patio

n pe

rcen

tage

82

%

81%

77

%

74%

73

%

73%

80

%

79%

79

%

76%

no

par

ticip

atio

n 13

2 13

5 13

2 48

9 45

7 50

5 11

8 15

5 23

2 28

7 ov

errid

e to

tal

617

638

814

691

751

730

817

814

754

838

over

ride

at 0

~30

min

s 18

6 22

2 21

4 23

8 26

0 26

0 29

9 29

9 25

9 30

8 ov

errid

e at

31-

60 m

ins

186

169

172

207

193

205

226

203

204

223

over

ride

at 6

1-90

min

s 14

4 14

8 15

1 15

0 18

8 15

1 16

9 18

5 16

9 17

8 ov

errid

e at

91-

119

min

s 10

1 99

27

7 96

11

0 11

4 12

3 12

7 12

2 12

9 no

par

ticip

atio

n, %

of a

ll no

with

da

ta

over

ride

at 0

~30

min

s, %

of a

ll ov

errid

e ov

errid

e at

31-

60 m

ins,

% o

f all

over

ride

3%

30%

30%

3%

35%

26%

3%

26%

21%

11%

34%

30%

10%

35%

26%

11%

36%

28%

3%

37%

28%

3%

37%

25%

5%

34%

27%

6%

37%

27%

over

ride

at 6

1-90

min

s, %

of a

ll ov

errid

e 23

%

23%

19

%

22%

25

%

21%

21

%

23%

22

%

21%

over

ride

at 9

1-11

9 m

ins,

% o

f all

over

ride

16%

16

%

34%

14

%

15%

16

%

15%

16

%

16%

15

%

41

of 342


Appe

ndix

D –

SPP

C 20

19 D

R pa

rtic

ipat

ion

sum

mar

y

SPPC

6/1

1/20

19 –

7/1

8/20

19

DR D

ate

6/11

/201

9

6/12

/201

9

6/18

/201

9

6/19

/201

9

7/10

/201

9

7/11

/201

9

7/12

/201

9

7/15

/201

9

7/17

/201

9

7/18

/20

19

Tota

l 24

09

2409

24

09

2409

24

09

2409

24

09

2409

24

09

2409

n/

a 20

00

2000

19

38

1938

17

13

1713

17

13

1715

17

14

1714

En

rolle

d 40

9 40

9 47

1 47

1 69

6 69

6 69

6 69

4 69

5 69

5 10

0% p

artic

ipat

ion

316

329

316

325

458

473

447

470

489

498

miss

ing

row

>19

, to

exlu

cde

40

37

58

51

156

149

140

120

108

100

full

part

icipa

tion

perc

enta

ge

86%

88

%

77%

77

%

85%

86

%

80%

82

%

83%

84

%

no p

artic

ipat

ion

6 11

45

54

36

22

33

48

44

42

ov

errid

e to

tal

47

32

52

41

46

52

76

56

54

55

over

ride

at 0

~30

min

s 15

9

10

11

13

15

18

12

13

13

over

ride

at 3

1-60

min

s ov

errid

e at

61-

90 m

ins

over

ride

at 9

1-11

9 m

ins

no p

artic

ipat

ion,

% o

f all

no w

ith

data

ov

errid

e at

0~3

0 m

ins,

% o

f all

over

ride

11

15

6 2%

32%

7 6 10

3%

28%

12

13

17

11%

19%

13

9 8 13%

27%

7 17

9 7%

28%

15

15

7 4%

29%

20

23

15

6%

24%

9 13

22

8%

21%

12

16

13

7%

24%

14

16

12

7%

24%

over

ride

at 3

1-60

min

s, %

of a

ll ov

errid

e 23

%

22%

23

%

32%

15

%

29%

26

%

16%

22

%

25%

over

ride

at 6

1-90

min

s, %

of a

ll ov

errid

e 32

%

19%

25

%

22%

37

%

29%

30

%

23%

30

%

29%

over

ride

at 9

1-11

9 m

ins,

% o

f all

over

ride

13%

31

%

33%

20

%

20%

13

%

20%

39

%

24%

22

%

42

of 342


SPPC

7/1

9/20

19 –

8/1

4/20

19

DR D

ate

7/19

/201

9

7/22

/201

9

7/24

/201

9

7/25

/201

9

7/29

/201

9

7/30

/201

9

8/5/

2019

8/

6/20

19

8/13

/201

9

8/14

/201

9

Tota

l 24

09

2409

24

09

2409

24

09

2409

24

09

2409

24

09

2409

n/

a 17

14

1713

17

13

1713

16

77

1677

16

45

1644

15

81

1581

En

rolle

d 69

5 69

6 69

6 69

6 73

2 73

2 76

4 76

5 82

8 82

8 10

0% p

artic

ipat

ion

489

495

484

529

512

524

611

584

621

605

miss

ing

row

>19

, to

exlu

cde

89

76

61

62

105

96

59

53

69

68

full

part

icipa

tion

perc

enta

ge

81%

80

%

76%

83

%

82%

82

%

87%

82

%

82%

80

%

no p

artic

ipat

ion

51

51

36

29

51

59

36

45

60

56

over

ride

tota

l 66

74

11

5 80

64

53

58

84

78

99

ov

errid

e at

0~3

0 m

ins

20

17

26

13

20

17

16

16

20

22

over

ride

at 3

1-60

min

s 15

19

31

28

14

14

13

23

25

26

ov

errid

e at

61-

90 m

ins

16

15

26

24

17

15

14

27

19

28

over

ride

at 9

1-11

9 m

ins

15

23

32

15

13

7 15

18

14

23

no

par

ticip

atio

n, %

of a

ll no

with

da

ta

over

ride

at 0

~30

min

s, %

of a

ll ov

errid

e ov

errid

e at

31-

60 m

ins,

% o

f all

over

ride

8%

30%

23%

8%

23%

26%

6%

23%

27%

5%

16%

35%

8%

31%

22%

9%

32%

26%

5%

28%

22%

6%

19%

27%

8%

26%

32%

7%

22%

26%

over

ride

at 6

1-90

min

s, %

of a

ll ov

errid

e 24

%

20%

23

%

30%

27

%

28%

24

%

32%

24

%

28%

over

ride

at 9

1-11

9 m

ins,

% o

f all

over

ride

23%

31

%

28%

19

%

20%

13

%

26%

21

%

18%

23

%

43

of 342


SPPC

8/1

5/20

19 –

9/3

/201

9 DR

Dat

e 8/

15/2

019

8/20

/201

9

8/21

/201

9 8/

23/2

019

8/26

/201

9 8/

27/2

019

8/28

/201

9 8/

30/2

01

9 9/

3/20

19

Tota

l 24

09

2409

24

09

2409

24

09

2409

24

09

2409

24

09

n/a

1581

15

82

1582

15

82

1414

14

14

1414

14

14

1497

En

rolle

d 82

8 82

7 82

7 82

7 99

5 99

5 99

5 99

5 91

2 10

0% p

artic

ipat

ion

637

650

664

689

654

678

668

721

631

miss

ing

row

>19

, to

exlu

cde

57

41

40

42

150

133

122

105

135

full

part

icipa

tion

perc

enta

ge

83%

83

%

84%

88

%

77%

79

%

77%

81

%

81%

no

par

ticip

atio

n 35

57

52

20

10

4 99

10

9 43

54

ov

errid

e to

tal

99

80

71

76

91

86

96

126

93

over

ride

at 0

~30

min

s 34

13

20

20

23

21

24

33

25

ov

errid

e at

31-

60 m

ins

28

27

14

24

19

24

29

30

17

over

ride

at 6

1-90

min

s 21

20

19

16

23

23

24

23

22

ov

errid

e at

91-

119

min

s 16

20

18

16

26

18

19

40

29

no

par

ticip

atio

n, %

of a

ll no

with

dat

a ov

errid

e at

0~3

0 m

ins,

% o

f all

over

ride

over

ride

at 3

1-60

min

s, %

of a

ll ov

errid

e ov

errid

e at

61-

90 m

ins,

% o

f all

over

ride

5%

34%

28%

21%

7%

16%

34%

25%

7%

28%

20%

27%

3%

26%

32%

21%

12%

25

%

21%

25%

11%

24

%

28%

27%

12%

25

%

30%

25%

5%

26%

24%

18%

7%

27%

18%

24%

over

ride

at 9

1-11

9 m

ins,

% o

f all

over

ride

16%

25

%

25%

21

%

29%

21

%

20%

32

%

31%

44


of 342

Appe

ndix

E –

eco

+ Po

pula

tion

Data

N

PC 2

020

NPC

06/2

2 NP

C 06

/29

NPC

07/0

6 NP

C 07

/13

NPC

07/2

0 NP

C 8/

15

NPC

9/1

NPC

10/1

NP

C 11

/1

NPC

12/1

Po

pula

tion

Tota

ls EC

O+

DISA

BLED

26

85

2544

24

44

2320

22

57

2021

19

40

1955

19

16

1747

EC

O+

1 31

4 36

5 35

2 40

0 43

5 51

0 55

7 58

8 59

7 61

2 EC

O+

2 13

9 18

8 20

5 22

5 24

5 28

0 30

8 31

2 31

7 31

0 EC

O+

3 41

4 45

1 46

8 50

0 51

4 54

4 57

4 60

3 63

3 64

9 EC

O+

4 53

52

5334

53

94

5408

53

87

5356

52

76

5225

54

33

5438

EC

O+

5 72

7 74

9 76

8 77

8 79

3 82

4 83

3 83

9 93

6 99

1 TO

TAL E

NROL

LED

9631

96

31

9631

96

31

9631

95

35

9488

95

22

9832

97

47

NPC

06/2

2 NP

C 06

/29

NPC

07/0

6 NP

C 07

/13

NPC

07/2

0 NP

C 8/

15

NPC

9/1

NPC

10/1

NP

C 11

/1

NPC

12/1

O

vera

ll po

pula

tion

perc

enta

ge

ECO

+ DI

SABL

ED

28%

26

%

25%

24

%

23%

21

%

20%

21

%

19%

18

%

ECO

+ 1

ECO

+ 2

ECO

+ 3

ECO

+ 4

ECO

+ 5

3%

1%

4%

56%

8%

NP

C 06

/22

4%

2%

5%

55%

8%

NP

C 06

/29

4%

2%

5%

56%

8%

NP

C 07

/06

4%

2%

5%

56%

8%

NP

C 07

/13

5%

3%

5%

56%

8%

NP

C 07

/20

5%

3%

6%

56%

9%

NP

C 8/

15

6%

3%

6%

56%

9%

NP

C 9/

1

6%

3%

6%

55%

9%

NP

C 10

/1

6%

3%

6%

55%

10

%

NPC

11/1

6%

3%

7%

56%

10

%

NPC

12/1

EC

O+

only

pe

rcen

tage

EC

O+

1 5%

5%

5%

5%

6%

7%

7%

8%

8%

8%

EC

O+

2 2%

3%

3%

3%

3%

4%

4%

4%

4%

4%

EC

O+

3 6%

6%

7%

7%

7%

7%

8%

8%

8%

8%

EC

O+

4 77

%

75%

75

%

74%

73

%

71%

70

%

69%

69

%

68%

EC

O+

5 10

%

11%

11

%

11%

11

%

11%

11

%

11%

12

%

12%

NP

C 06

/22

NPC

06/2

9 NP

C 07

/06

NPC

07/1

3 NP

C 07

/20

NPC

8/15

NP

C 9/

1 NP

C 10

/1

NPC

11/1

NP

C 12

/1

22-Ju

n 29

-Jun

6-Ju

l 13

-Jul

20-Ju

l 15

-Au

g 1-

Sep

1-O

ct

1- Nov

1- Dec

Aver

age

setb

ack

3.80

3.

76

3.76

3.

74

3.72

3.

67

3.64

3.

62

3.63

3.

62

Scen

ario

1 -

All E

CO+

enab

led

3.72

3.

68

3.68

3.

65

3.63

3.

58

3.55

3.

53

3.54

3.

54 45

of 342

Techinical Appendix DSM-18Sc

enar

io 2

- EC

O+

1 =

1 DE

GREE

SE

TBAC

K 3.

83

3.80

3.

80

3.78

3.

76

3.72

3.

70

3.69

3.

69

3.68

Scen

ario

3 -

ECO

+ 1

= 1

DEGR

EE

SETB

ACK

+ AL

L ECO

+ en

able

d 3.

76

3.73

3.

73

3.71

3.

69

3.65

3.

62

3.60

3.

61

3.61

46


of 342

SPPC

202

0 SP

PC

06/2

2 SP

PC

06/2

9 SP

PC

07/0

6 SP

PC

07/1

3 SP

PC

07/2

0 SP

PC

8/15

SP

PC

9/1

SPPC

9/

15

SPPC

10

/1

SPPC

11

/1

SPPC

12

/1

Popu

latio

n To

tals

ECO

+ DI

SABL

ED

785

704

680

656

634

594

560

666

653

606

564

ECO

+ 1

57

73

80

84

89

98

109

112

112

117

123

ECO

+ 2

30

37

37

42

45

54

57

65

63

71

80

ECO

+ 3

113

133

134

137

144

157

159

170

171

181

182

ECO

+ 4

1041

10

70

1086

10

95

1101

10

89

1085

11

26

1127

11

87

1183

EC

O+

5 13

9 14

8 14

8 15

1 15

2 15

2 15

8 17

1 17

3 18

5 19

7 TO

TAL E

NROL

LED

2165

21

65

2165

21

65

2165

21

44

2128

23

10

2299

23

47

2329

SP

PC

06/2

2 SP

PC

06/2

9 SP

PC

07/0

6 SP

PC

07/1

3 SP

PC

07/2

0 SP

PC

8/15

SP

PC

9/1

SPPC

9/

15

SPPC

10

/1

SPPC

11

/1

SPPC

12

/1

Ove

rall

popu

latio

n pe

rcen

tage

ECO

+ on

ly

perc

enta

ge

ECO

+ DI

SABL

ED

36%

33

%

31%

30

%

29%

28

%

26%

29

%

28%

26

%

24%

EC

O+

1 3%

3%

4%

4%

4%

5%

5%

5%

5%

5%

5%

EC

O+

2 1%

2%

2%

2%

2%

3%

3%

3%

3%

3%

3%

EC

O+

3 EC

O+

4 EC

O+

5

ECO

+ 1

5%

48%

6%

SP

PC

06/2

2 4%

6%

49%

7%

SP

PC

06/2

9 5%

6%

50%

7%

SP

PC

07/0

6 5%

6%

51%

7%

SP

PC

07/1

3 6%

7%

51%

7%

SP

PC

07/2

0 6%

7%

51%

7%

SP

PC

8/15

6%

7%

51%

7%

SP

PC

9/1

7%

7%

49%

7%

SP

PC

9/15

7%

7%

49%

8%

SP

PC

10/1

7%

8%

51%

8%

SP

PC

11/1

7%

8%

51%

8%

SP

PC

12/1

7%

EC

O+

2 2%

3%

2%

3%

3%

3%

4%

4%

4%

4%

5%

EC

O+

3 8%

9%

9%

9%

9%

10

%

10%

10

%

10%

10

%

10%

EC

O+

4 75

%

73%

73

%

73%

72

%

70%

69

%

68%

68

%

68%

67

%

ECO

+ 5

10%

10

%

10%

10

%

10%

10

%

10%

10

%

11%

11

%

11%

SP

PC

06/2

2 SP

PC

06/2

9 SP

PC

07/0

6 SP

PC

07/1

3 SP

PC

07/2

0 SP

PC

8/15

SP

PC

9/1

SPPC

9/

15

SPPC

10

/1

SPPC

11

/1

SPPC

12

/1

22-

Jun

29-

Jun

6-Ju

l 13

-Jul

20-Ju

l 15

-Au

g 1-

Sep

15-

Sep

1-O

ct

SPPC

11

/1

SPPC

12

/1

Aver

age

setb

ack

3.81

3.

77

3.76

3.

74

3.73

3.

69

3.67

3.

68

3.68

3.

66

3.64

Sc

enar

io 1

-Al

l ECO

+ en

able

d 3.

71

3.66

3.

64

3.63

3.

61

3.58

3.

55

3.55

3.

55

3.55

3.

53

Scen

ario

2 -

ECO

+ 1

= 1

DEGR

EE S

ETBA

CK

3.84

3.

80

3.79

3.

78

3.77

3.

74

3.72

3.

72

3.72

3.

71

3.69

Sc

enar

io 3

- EC

O+

1 =

1 DE

GREE

SET

BACK

+

ALL E

CO+

enab

led

3.75

3.

71

3.70

3.

69

3.67

3.

64

3.62

3.

61

3.62

3.

61

3.60

47

of 342


Appe

ndix

F –

Occ

upan

cy S

ensin

g Ca

pabi

lity

Data

NP

C SP

PC

Enro

lled

eco+

en

able

d En

rolle

d ec

o+

enab

led

ecob

ee

3 16

3 11

3 39

25

ecob

ee

4 24

9 17

7 19

6 12

6

NPC

SPPC

O

ccup

ancy

en

able

d 29

0 15

1

Tota

l Pop

ulat

ion

9970

24

09

Perc

enta

ge

3%

6%

48


of 342

Appe

ndix

G –

Sm

art M

eter

Dat

a An

alys

is Su

mm

ary

NPC

202

0 Ph

ase

5 DR

Dat

es

DRCG

Bas

elin

e Dr

op

Pre

Even

t Ev

ent

Min

Po

st

Even

t 7/

29/2

020

2.71

4484

4.

903

2.64

6 5.

805

7/30

/202

0 2.

9203

5.

356

2.58

4 6.

209

7/31

/202

0 2.

7247

6.

016

2.90

1 6.

264

8/16

/202

0 3.

0957

16

5.48

8 2.

512

5.84

8/

17/2

020

3.20

4392

5.

626

2.52

7 6.

077

8/20

/202

0 2.

7776

16

5.30

6 2.

61

5.81

4 8/

21/2

020

2.80

844

5.28

4 2.

393

5.40

6 9/

5/20

20

2.86

9344

4.

96

2.34

5.

604

9/6/

2020

3.

0823

6 5.

4 2.

426

5.71

6 Av

erag

e 2.

9108

17

5.37

1 2.

5487

78

5.85

9444

Phas

e 6

DR D

ates

DR

CG B

asel

ine

Drop

Pr

e Ev

ent

Even

t M

in

Post

Ev

ent

7/29

/202

0 2.

7611

96

4.48

3 1.

898

5.95

1 7/

30/2

020

2.72

958

4.95

8 2.

121

6.18

5 7/

31/2

020

2.02

2388

4.

804

2.46

8 5.

283

8/16

/202

0 2.

5683

52

5.30

2 2.

481

5.87

6 8/

17/2

020

2.45

5036

5.

299

2.58

5 5.

985

8/20

/202

0 2.

6636

6 5.

146

2.14

8 6.

167

8/21

/202

0 2.

5986

96

4.89

1 2.

099

5.76

2 9/

5/20

20

2.50

0776

4.

963

2.11

5.

365

9/6/

2020

2.

7179

2 4.

929

2.10

6 5.

559

Aver

age

2.55

7512

4.

975

2.22

4 5.

7925

56

Phas

e 7

49

of 342

Techinical Appendix DSM-18DR

Dat

es

DRCG

Bas

elin

e Dr

op

Pre

Even

t Ev

ent

Min

Po

st

Even

t 7/

29/2

020

2.90

52

4.75

8 1.

949

5.67

1 7/

30/2

020

3.02

0468

5.

014

2.01

5.

635

7/31

/202

0 3.

2786

04

5.37

9 1.

955

5.96

5 8/

16/2

020

3.08

7068

5.

256

2.23

7 6.

106

8/17

/202

0 2.

7880

16

5.39

2.

484

6.03

5 8/

20/2

020

3.14

7188

5.

291

1.94

7 6.

052

8/21

/202

0 2.

9540

08

5.00

2 1.

963

5.81

3 9/

5/20

20

3.04

4384

5.

12

1.88

2 5.

489

9/6/

2020

3.

2830

12

4.92

5 1.

851

5.77

1 Av

erag

e 3.

0564

39

5.12

6111

2.

0308

89

5.83

7444

NPC

201

9 DR

Dat

es

Adju

sted

Dro

p Pr

e Ev

ent

Even

t M

in

Post

Ev

ent

7/11

/201

9 1.

7676

0098

3.

262

1.51

1 4.

254

7/15

/201

9 1.

7473

1551

8 3.

196

1.61

4.

188

7/22

/201

9 1.

6799

1826

8 3.

18

1.49

9 4.

187

7/29

/201

9 2.

1546

0761

5 3.

667

1.70

9 4.

781

8/5/

2019

2.

3586

5507

5 3.

898

1.66

1 4.

621

8/14

/201

9 1.

8275

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4 3.

46

1.72

1 4.

303

8/15

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9 1.

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4 3.

523

1.69

7 4.

327

8/21

/201

9 1.

8787

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8 3.

257

1.59

3 4.

518

8/26

/201

9 1.

6736

2795

9 3.

342

1.75

9 4.

264

8/28

/201

9 2.

0288

6115

5 3.

758

1.79

6 4.

476

Aver

age

1.90

5748

35

3.45

43

1.65

56

4.39

19

50

of 342


SPPC

202

0 Da

te

Stra

tegy

Ad

just

ed

Drop

Pr

e Ev

ent

Even

t M

in

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Ev

ent

6/23

/202

0 No

rth

1-Ph

ase

200P

M

1.15

99

2.24

0.

992

3.29

5

6/26

/202

0 No

rth

1-Ph

ase

400P

M

1.68

2.

462

1.21

2 3.

431

7/15

/202

0 No

rth

1-Ph

ase

400P

M

1.52

5 2.

6 1.

328

3.64

8

7/16

/202

0 No

rth

1-Ph

ase

400P

M

1.65

37

2.82

5 1.

193

3.02

7/17

/202

0 No

rth

1-Ph

ase

400P

M

1.83

7 2.

938

1.31

5 3.

824

7/29

/202

0 No

rth

1-Ph

ase

200P

M

1.60

3 2.

07

0.96

7 3.

701

8/16

/202

0 No

rth

1-Ph

ase

200P

M

1.36

8 2.

438

1.24

3.

438

9/4/

2020

No

rth

1-Ph

ase

200P

M

1.37

9 2.

294

1.25

2 3.

923

9/5/

2020

No

rth

1-Ph

ase

200P

M

1.57

1 2.

609

1.06

3.

758

Aver

age

1.53

0733

333

2.49

7333

1.

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22

3.55

9778

av

erag

e 2

pm

1.41

618

2.33

02

1.10

22

3.62

3 av

erag

e 4

pm

1.67

3925

2.

7062

5 1.

262

3.48

075

51

of 342


SPPC

201

9 Da

te

Stra

tegy

Ad

just

ed

Drop

Pr

e Ev

ent

Even

t M

in

Post

Ev

ent

6/18

/201

9 No

rth

1-Ph

ase

400P

M

1.09

9 1.

87

0.73

6 2.

94

7/12

/201

9 No

rth

1-Ph

ase

400P

M

1.45

08

2.79

1 1.

187

3.64

3

7/22

/201

9 No

rth

1-Ph

ase

200P

M

1.60

9 2.

202

0.92

2 3.

672

7/24

/201

9 No

rth

1-Ph

ase

400P

M

1.48

6 2.

324

0.98

3 3.

559

8/6/

2019

No

rth

1-Ph

ase

200P

M

1.06

1 1.

863

0.95

5 2.

889

8/14

/201

9 No

rth

1-Ph

ase

200P

M

1.18

92

1.61

5 0.

883

3.14

6

8/15

/201

9 No

rth

1-Ph

ase

200P

M

1.07

7 1.

897

0.79

9 3.

537

8/26

/201

9 No

rth

1-Ph

ase

200P

M

1.15

4 1.

824

0.83

6 3.

716

8/27

/201

9 No

rth

1-Ph

ase

200P

M

0.9

1.64

7 0.

849

3.40

6

8/28

/201

9 No

rth

1-Ph

ase

200P

M

1.41

1 2.

107

0.91

4 3.

73

9/3/

2019

No

rth

1-Ph

ase

200P

M

1.13

3 1.

957

0.79

5 3.

624

Aver

age

1.23

3636

364

2.00

8818

0.

8962

73

3.44

2 av

erag

e 2

pm

1.19

6426

263

1.90

2313

0.

8721

41

3.46

2444

av

erag

e 4

pm

1.34

5266

667

2.32

8333

0.

9686

67

3.38

0667

52

21-06 VOL9 - NV Energy

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Transcript of 21-06 VOL9 - NV Energy