Global Energy Decisions Integrated Fuel Analysis Best Practice (2007)

Global Energy uses an integrated fuel forecasting approach based upon a common data

model, best in class fundamental modeling solutions, and our Global Energy expertise to

provide the best available analysis of fuel and electricity supply and demand fundamentals,

expected prices and how they are formed. The results of this integrated fuel forecasting

strategy provide a consistent approach to fuel prices across all Global Energy advisory and

consulting services to enable our clients to turn their strategy analysis into credible results.

Global Energy uses a three-step process to achieve an integrated fuel forecast:

1. Fuel Demand for Power Generation from ENERPRISE Market Analytics™. Global

Energy uses a fundamental approach to forecast power demand in each region of North

America using its Market Analytics™ system—the standard of the industry for energy

data management and production simulation modeling. ENERPRISE Market Analytics™ is

a sophisticated, relational database that operates with PROSYM™, a state-of-the-art,

multi-area, chronological production simulation model.

2. GPCM and CQMM Fuels Models. Expected fuel demand for power generation serves as

an input in Global Energy’s Gas Pipeline Competition Model (GPCM) gas model and Coal

Quality Market Model (CQMM) coal model both developed by RBAC Inc. to develop a

fundamental analysis of fuel supply and demand and expected wholesale fuel prices.

Separately, we use Global Energy World Oil model to provide global oil supply and

demand inputs.

3. Expected Wholesale Electricity Prices. Expected fuel prices are then used as an input

to Market Analytics™ to produce wholesale expected prices and their implications

across each region in North America for Global Energy advisory services.

This Global Energy Integrated Fuels Forecast Best Practice provides an independent,

transparent, consistent approach to fuel analysis utilizing the best available data and

software models appropriate to the task integrated into a complete market advisory

service.

Integrated Fuels Analysis Global Energy Decisions Best Practice

Overview Global Energy is a market-leading provider of advanced market analysis

of natural gas, coal, and oil global fuel fundamentals, prices, and impacts

on power generation.

SOURCE: Global Energy.

Global Energy GasView version of GPCM. The fundamental model

used to prepare Global Energy’s natural gas forecast is a customized

architecture of RBAC’s GPCM™ natural gas model. In this specification,

Global Energy forecasts natural gas production, interstate and intrastate

transportation, storage, and consumption by sector. GPCM simulates

regional interactions between supply, transportation, storage, and

demand to determine market clearing price and reserve additions. Prices

and gas demand for electric generation from GPCM are integrated with

Global Energy’s North American Power Reference Case price forecast.1

1 The North American Reference Case is a 25-year price forecast of 76

competitive power markets across every North American Electric Reliability

Council region. These forecasts are updated twice per year.

Global Energy GasView

solution available as a

complete solution for clients

wishing to bring the tools in-

house:

• GPCM model

• Gas Reference Case

• Global Energy Gas Data

• Global Energy Consulting

Best Practice: Global Energy Integrated Fuels Forecasting

Global Energy Gas Reference Case is a fundamental analysis of supply and

demand, expected gas market prices, infrastructure, and market uncertainty for

36 competitive gas price zones. The Gas Reference Case uses the GPCM model

enhanced by Global Energy. Short-term price forecasts (48 months) are based on

NYMEX future strip combined with a mean reversion process during the latter

half of the forecast period based upon GPCM model results. The model reflects

Global Energy’s forecast of electricity demand and our forecast of gas demand for

power generation. Global Energy’s specification includes 189 supply nodes, 204

demand nodes, and over 450 transport nodes.

Gas Supply Sources Covered

Coal Sources Covered

Global Energy Coal Reference Case is a long-term fundamental analysis of

supply and demand, expected wholesale coal market prices, infrastructure, and

transportation constraints for every major North American coal basin. The Coal

A new Standard

for Natural Gas

Price Forecasting

Stay on top of

changes in coal

prices

Best Practice: Global Energy Integrated Fuel Price Forecasting

Reference Case focuses on the productive capacity of each basin and implications

for coal prices of deliverability and other market issues. Global Energy’s long-

term coal forecast is derived using RBAC’s CQMMTM (Coal Quality Market

Model) in conjunction with Global Energy’s short-term econometric model for

the best view of near-term and long-term factors affecting coal market prices.

CoalView Interactive is a web-based, interactive, short-term coal market

forecast service designed for top-level overview of the U.S. coal market providing

daily-updated, marked-to-market, two-year forward monthly FOB mine and

barge price forecasts for 16 common U.S. coals. CoalView lets you add your own

price inputs and see them instantly collected, summarized, and displayed for all

16 coals.

Global Energy Scenario Price Matrix Global Energy also produces several structural scenarios of future natural gas

market conditions. In addition, we have analyzed historical gas price volatility

over several time frames and used this data to construct structural confidence

bands of the natural gas market price centered on our Reference Case price

forecast. The range of future gas market outcomes and scenarios are organized in

the figure below along with the key input assumptions and forecast drivers.

Global Energy Scenario Price Matrix

Reference Case•Expected Market Conditions

Global Cartel Pricing

•LNG Price Maintenance

VolatilityConfidence Band

•Reference Case with historical volatility bands

LNG Displacement

•Excess Regasification•Lower EG Fuel Demand

Structural Scenarios

Statistical Volatility

Reference Case•Expected Market Conditions

Global Cartel Pricing

•LNG Price Maintenance

VolatilityConfidence Band

•Reference Case with historical volatility bands

LNG Displacement

•Excess Regasification•Lower EG Fuel Demand

Structural Scenarios

Statistical Volatility

GLOBAL ENERGY FUELS CONSULTING CREDENTIALS

Modeling Tools Used in Global Energy Fuels Consulting. Our work is

done using one or more of the following solutions:

• Global Energy EnerPrise Market Analytics

• Global Energy EnerPrise Planning & Risk

• Global Energy Strategic Planning, (formerly Midas Gold)

• Global Energy Velocity Suite database services

• Global Energy MAPS for geospatial mapping and graphics solutions

• RBAC’s GPCM model for natural gas modeling

• RBAC’s CQMM model for coal market modeling

Fuel Price Forecast Methodology

Fuel Oil Forecast. Since crude oil is the raw material used to produce distillate

oil, jet kerosene, and heavy fuel oil (e.g., various sulfur grades of #6 residual oil),

as well as gasoline, Global Energy derives fuel oil forecasts for generators from

our West Texas Intermediate (WTI) Reference Case Forecast.

Global Energy produces its WTI Reference Case based on NYMEX future prices

for WTI Oil and Fuel Oil #2, product price relationships between fuel oils and

long-term supply and demand analysis of the WTI, and global crude oil markets.

The WTI forecast is based on 43 months of NYMEX futures prices and on

subsequent supply/demand fundamentals for the remainder for the forecast

period. The WTI NYMEX prices are incorporated directly for the first 24 months

and for the following 24 months by mean regression analysis with the

supply/demand analysis. A similar approach is used to forecast near-term natural

gas prices. The NYMEX trading dates for the fall 2005 forecast were September

12-14, 2005. Therefore, from April 2009 to the balance of the forecast period,

prices are based on econometric analysis of supply/demand fundamentals

prepared by Global Energy.

Incorporating the NYMEX prices thus captures a snapshot of the then-current

trading value and volatility cost during the first 48 months of the forecast period

including the geopolitical risk premiums and other market trading costs. Beyond

the NYMEX-influenced forecast period, the WTI price range is approximately 35

to $40/Bbl (2005$) between 2010 and 2029. This is due to Global Energy’s

analysis of the full-cycle replacement cost for OPEC production—in particular for

Saudi Arabia—and our analysis of downstream-of-the-wellhead production

capacity, and the costs for infrastructural capacity for refining, storage, and

transportation are evaluated, as well as global oil demand.

Natural Gas. To forecast natural gas prices, Global Energy uses three

forecasting phases. These are outlined in the table below.

Table 1 Reference Case Gas Price Forecasting Phases

Forecast Phase Period Length Data Source Forecast Technique

Futures Driven First 24 Months NYMEX Henry Hub futures and market differentials

Calculated Henry Hub and liquid market center differentials

Mean Reversion Next 24 Months Global Energy Linear process to gradually equate near-term to long-term trend

Long-term Trend Remaining forecast period (to 2029)

Various Global Energy data sources

Fundamental supply and demand analysis using the GPCM forecasting model

To derive the burner-tip forecasts used, Global Energy first examined regional

prices and basis swaps at a number of trading hubs. Using this historical data, for

the first 24 months of the forecast, Global Energy developed a differential price

between the appropriate market center nearest to the power plant and the Henry

Hub. Gas prices used for the first 24 months were driven by Henry Hub futures

market prices plus a basis differential (if any).

Applying this approach permits Global Energy’s forecast to include the recent

shifts in natural gas prices. During the following 24 months of the forecast

period, Global Energy imposed a linear mean reversion process on the forecast.

This process worked to slowly align natural gas prices during the first 24 months

back to their long-term, historical level.

To forecast future burner-tip gas prices beyond the initial 48-month period,

Global Energy has integrated the GPCM and CQMM fuels forecasting models

from RBAC Consultants into our modeling methodology for medium- to long-

term analysis. The modal is a general equilibrium model of gas supply and

demand in a competitive environment for the North American natural gas

industry.

Another important component in Global Energy’s gas forecast is the seasonal or

monthly variation in price. In general, gas prices have been traditionally higher

during winter months due to greatly increased core heating demand. To

determine the seasonal variation in gas prices, data at individual pricing points

are utilized. The appropriate observed seasonal pattern is applied to annual gas

price forecasts to derive monthly price forecasts that are used in Global Energy’s

market simulations. These seasonal factors represent typical or normalized

variation in monthly spot gas prices within a region.

The estimated seasonal variation in gas prices is shown in Figure 1. This indicates

the deviation among monthly gas prices as recorded at the Henry Hub. A

polynomial curve was then fitted to the monthly average. The figure indicates

that prices are highest during periods of increased core heating demand, while

they decline during the spring and early summer months. On average, prices tend

to begin rising starting in June due to electricity demand increase, coupled with

the beginning of the traditional gas storage-filling season. A similar estimation

technique is used to forecast monthly fuel oil prices.

Figure 1 Gas Price Seasonal Variation

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

For electric generators, Global Energy models natural gas burner-tip prices as the

sum of commodity prices (the cost of gas at a particular liquid market center,

which may be a gas-producing basin, a recognized hub—for example, the Henry

Hub—or a city-gate) and all relevant transportation charges involved in

transporting natural gas from the market center to the generation plant. An

active aftermarket consisting of a “capacity release” market exists on most

interstate pipeline systems. This capacity allows a gas shipper to offer its own

reserved capacity, in excess of current needs, to others for a negotiated price.

Capacity release transportation often trades at rates well below firm

transportation rates, although this varies depending on prevailing market

conditions.

Generators within a particular region can generally receive gas from a number of

supply basins or hubs. The number of sources is dependent on the geography of

where the plant is located relative to the interstate pipeline system. Global Energy

assumes that commodity prices experienced by a generator have many short-,

medium-, and long-term supply options available, which are reflected in market

prices at liquid market centers. In this competitive marketplace, other supply

sources not directly connected to a region can also indirectly influence gas prices.

Figure 2 shows the key North American supply hubs that are used in the analysis,

as well as average bid week market prices for 2004.

Figure 2 Natural Gas Liquid Market Centers and 2004 Prices

WECCWECCWECCWECC

MAPPMAPPMAPPMAPP

SPPSPPSPPSPP

ERCOTERCOTERCOTERCOT

SERCSERCSERCSERC

FRCCFRCCFRCCFRCC

MAINMAINMAINMAIN

ECARECARECARECAR

MAACMAACMAACMAAC

NPCCNPCCNPCCNPCC

NG PG&E

NG NEPOOL

NG N Nevada

NG SCG

NG SDG&E

S Nevada

NG AZ/NM

NG PNW

Coastal

NG PNW

NG Sask

NG Manitoba

NG Minnesota

NG Ontario

NG Maritime

Nebraska

Colorado

NG North SPP

NG Central SPPNG West SPP

NG ASEC

NG Iowa

N MAIN

NG Florida

NG Southern

NG VACARNG TVA

NG ENTERGY

NG Kentucky NG East ECAR

Indiana

NG Ohio

NG South SPP

NG NBAJA

NG West MAPP

NG Alberta

NG S MAIN

Michigan

Liquid Market

Center

Lebanon

NG NY HIJK

Dracut

Dawn7.99

Chicago

Ventura

7.19Kingsgate7.45

Stanfield

Topock7.39

Florida

Waha7.37

NYCity9.39

NG MAAC

Iroquois

Blanco

BroadRun8.57

Kosciusko

NG ERCOT

NG MAAC

NG Topock

Niagara

Burner-tip gas price for each gas-fired generation plant in a region is developed by

taking the hub price and adding a regional transportation adder. This amount

depends on the plant’s location relative to the basins or hubs, and the economics of

transporting gas, including compressor fuel used and pipeline tariffs/discounts, to

the plant’s burner-tip. The commodity and transportation components of natural gas

burner-tip prices are forecast separately and then assembled to derive the prices paid

by generation plants appropriate to their geographic location.

To produce Global Energy’s gas price forecast for power generators, Global

Energy produces a supply and demand forecast for U.S. markets. In our supply

analysis, we show the growing importance of LNG in the U.S. supply mix. During

this period we assume seven additional LNG facilities will be built (six in the U.S.,

one in Canada and Mexico each). The new U.S. plants, along with the existing five

facilities, will produce on average 8 Bcf/d by 2010—nearly triple the current

production.

Global Energy Gas Forecasting Methodology. The fundamental model

used to prepare Global Energy’s forecast is a customized architecture of RBAC

Consultants GPCM™ model. In this specification, Global Energy forecasts natural

gas production, interstate and intrastate transportation, storage, and

consumption by sector. GPCM simulates regional interactions between supply,

transportation, storage, and demand to determine market clearing price and

reserve additions. Prices and gas demand for electric generation from GPCM are

integrated with Global Energy’s North American Reference Case price forecast.2

Our analysis of natural gas markets includes a fundamental market assessment

• Natural gas prices;

• Basis differentials;

• Natural gas transportation;

• Storage analysis;

• Basin level gas supply economics;

• Pipeline and liquefied natural gas imports; and

• Natural gas demand for the residential, commercial, industrial and electric

generation sectors.

Global Energy uses GPCM in an annual format for medium- and long-term price

discovery and supply additions assumptions.3 In this configuration, the model

calls on the latest estimates for proven, probable, and possible reserves to create

a market clearing price centered on expected demand growth and the models

endogenous supply and transportation capacity solutions.

Input data used in Global Energy’s GPCM specification is prepared using Global

Energy Intelligence’s Velocity Suite dataset along with Alto’s current gas

transportation network and supply assumptions. Global Energy’s specification

for the GPCM model includes 189 supply nodes, 204 demand nodes, and over

450 transportation nodes. Figure 3 provides a graphical framework for the

generalized equilibrium solution that is used to produce the Global Energy

Reference Case.

2 The North American Reference Case is a 25-year price forecast of 75 competitive power markets across every North American Electric Reliability Council region. These forecasts are updated twice per year. 3 Short-term (36 month) price estimates are based on recent NYMEX strips. Hub prices are produced by applying an appropriate shape for seasonality to the model projected annual basis values.

Figure 3 Generalized Equilibrium Solution Example

Demand

EmissionPrice

Forecast

Oil Model

Coal Model

GasModel

ReferenceCase

ForecastPrice Price

Demand Demand

PricePrice

Demand

EmissionPrice

Forecast

Oil Model

Coal Model

GasModel

ReferenceCase

ForecastPrice Price

Demand Demand

PricePrice

Gas Data Sources. Global Energy uses a comprehensive collection of data

sources, including important industry and inter-organizational relationships, to

create a robust dataset for modeling North America’s natural gas market. To fully

use the capabilities of the GPCM model, Global Energy enhances data licensed

from RBAC to create a GPCM-compliant dataset.

GPCM Input Data Provided • Gas Demand for Power Generation from Global Energy Power Reference Cases;

• State Gas Demand (Residential, Commercial, Industrial and Electric

Generators); and

• Supply Cost Curves by Basin by Type (connected, unconnected, conventional,

unconventional, CBM, etc.).

Global Energy Gas Reference Case Output Data Provided

• Supply by Basin;

• Demand by Census Division by Sector (Core, Industrial and Electric Generators);

• Corridor Gas Flows;

• LNG Imports;

• Net Canadian and Mexico Imports; and

• Supply and Demand Center Price Zones (over 30 markets).

Gas Supply Fundamentals. Global Energy uses supply data as reported by the

United States Geological Survey (USGS), Petro Data Source, the National

Petroleum Council (NPC), the Potential Gas Committee (PGC), and the National

Energy Board of Canada (NEB). These data sources are used as a starting point

for creating the Global Energy supply outlook. The Petro Data Source data that is

reported by basin and state in the Global Energy Velocity Suite is used as a cross-

reference for historical production. The most recent USGS, NPC, and PGC reports

are used as reference for resource potential out of the modeled supply nodes.

Generally, supply nodes are modeled as proven, probable, or possible in each

model-defined basin. Resource and O&M costs are derived from Global Energy

industry research.

Gas Pipeline and Storage. The data used to model natural gas infrastructure

is obtained, when available, from pipeline and storage company Websites. In

some instances Website data is supplemented with docket and Form-2 filings

submitted to FERC. Additionally, Global Energy cross-references electronic data

with system maps and other publicly available informational postings. Global

Energy reviews and updates the transportation data including capacity, tariffs,

embedded cost, discounting behavior, dates of entry of prospective new pipelines,

and the costs of these new pipelines.

Gas Demand. Global Energy forecasts natural gas demand using regression

analyses at the state level for each natural gas demand sector (industrial,

commercial, residential).4 Both linear and lognormal regressions are employed to

determine a best fit. When the results from lognormal and linear regression

analysis are inconclusive, alternative methods—such as time series analysis—are

used for forecasting demand and price elasticity.

Historical natural gas consumption data used in Global Energy’s demand forecast

is derived from EIA Form-176, EIA Form-906, EIA’s Natural Gas Monthly

publication and Statistics Canada. United States consumption data is sourced

from Global Energy’s VELOCITY SUITE. Canadian data is developed from data

sources that are available for purchase on the Statistics Canada Website. The

historical dataset includes monthly estimates of natural gas consumption by

sector for each U.S. state and Canadian province since 1997.5 To produce a

monthly demand forecast the historical consumption data is regressed against

independent variables such as:

• Price: Global Energy uses historical, regional natural gas spot prices as

reported by EIA, Natural Gas Intelligence. Daily spot market data is used to

derive a monthly average price.

4 Electric generation demand is derived from the Global Energy Reference Case through an iterative modeling cycle. 5 Historical consumption for Hawaii and the Canadian territories is not considered.

• Heating degree days (HDD): A measure of how cold a location is over a period of time

relative to a base temperature most commonly specified as 65 degrees Fahrenheit.

The measure is computed for each day by subtracting the average of the day’s

high and low temperatures from the base temperature (65 degrees), with

negative values set equal to zero. Each day’s heating degree days are summed

to create a heating degree day measure for a specified reference period.

Heating degree-days are used in energy analysis as an indicator of space

heating energy requirements or use. State level historical data is obtained

from the National Oceanic and Atmospheric Administration (NOAA).

• Cooling degree days (CDD): A measure of how warm a location is over a

period of time relative to a base temperature, most commonly specified as 65

degrees Fahrenheit. The measure is computed for each day by subtracting the

base temperature (65 degrees) from the average of the day’s high and low

temperatures, with negative values set equal to zero. Each day’s cooling

degree-days are summed to create a cooling degree day measure for a

specified reference period. Cooling degree days are used in energy analysis as

an indicator of air conditioning energy requirements or use. State level

historical data is obtained from the National Oceanic and Atmospheric

Administration (NOAA).

• Population: State level historical data and estimates are obtained from the

United States Census Bureau. Population data for Canada is sourced from

Statistics Canada.

• Income: State level historical data is obtained from the U.S. Department of

Commerce Bureau of Economic Analysis (BEA).

• Manufacturing Gross State Product (MGSP): State level historical data is

obtained from the U.S. Department of Commerce Bureau of Economic

Analysis (BEA).

Global Energy Coal Forecasting Methodology. Coal is the dominant input

fuel in regions that contain, or are nearby to the major coal producing areas. Coal

represents well above 50 percent of the input fuel for electricity generation in

ECAR, MAPP, SPP, MAIN, and SERC regions. Similarly, coal is the largest input

fuel for electricity generation (though with less than 50 percent of the market) in

MAAC and WECC. Coal generation is the largest electricity source—especially in

the interior parts of the region where large coal deposits are located and often

supply nearby power plants.

Understanding the role and importance of coal as a primary fuel is essential to

effectively modeling and assessing the market fundamentals of the electric power

industry. Global Energy Decision’s coal price forecasting is based on short-term

and long-term coal price forecasting models. Because coal prices respond to

model inputs differently depending over time, Global Energy has developed an

econometrically based short-term model and a supply-and-demand based long-

term model. In both coal price forecasting models, model output is intrinsically

linked to price forecasts for competitive fuel (i.e., natural gas), ensuring that the

competitive interplay between coal and natural gas prices remain intact.

Coal Quality Market Model (CQMM). Global Energy’s long-term coal

forecast is derived using RBAC’s CQMMTM (Coal Quality Market Model) in

conjunction with Global Energy’s short-term econometric model for the best view

of near-term and long-term factors affecting coal market prices.

Global Energy provides enhanced coal market data as an input to the CQMM

model to improve modeling performance and provide a consistent, high quality

starting point for analysis.

Oil and gas prices have shown a high correlation over time. Similarly, gas and

coal prices have also shown a relatively high correlation over time. NYMEX coal

prices, for example, have tracked to natural gas prices with a coefficient of

determination (R2) equal to 0.7. As the marginally priced fuel in many markets,

natural gas prices set the ceiling to which coal can go.

Within the spectrum of coal types, Powder River Basin coal competes with most

coal qualities because of its abundance, low cost, and desirable quality. Typical

PRB coal quality is 8,700 Btu/lb with an average of 0.70 lbsSO2/MMBtu. As a

comparison, coal that is considered to be in compliance with the Clean Air Act

amendments of 1990 has a sulfur content of 1.20 lbsSO2/MMBtu or less. With

SO2 allowance prices reaching into the $850/ton range and NYMEX coal trading

at $2.50/MMBtu, PRB coal at $0.50/MMBtu continues to be in strong demand.

Despite the strong demand for PRB coal, a key driver in keeping the price of PRB

coal down is the insufficient rail capacity out of the PRB. The Joint Line, operated

by Burlington Northern Santa Fe and Union Pacific, has the capacity to handle

about 350 million tons per year. However, the permitted capacity of coal mine

operations in the PRB exceeds 500 million tons per year. This discrepancy has

prevented PRB coal from reaching its full market potential as the mines are

unable to sell their full capacity of coal, resulting in depressed prices. In response

to this demand, additional rail capacity is being developed out of the PRB.

Currently, the Joint Line is being triple tracked; expansion of the DME rail line

from Belle Fourche to Gillette is expected to be completed by 2010; and the

Tongue River Railroad expansion from Birney to Miles City continues to move

toward regulatory approval. Therefore, for forecast periods of five years and less,

short-term coal prices are heavily dependent on marketplace fluctuations as:

• Weather;

• Coal stockpile volumes;

• Short-term natural gas forecasts;

• Short-term SO2 allowance price forecasts;

• Recently signed coal contract data;

• Known transportation constraints;

• Known supply constraints (e.g., significant mine outages);

• Known competitive fuel constraints (e.g., nuclear plant outages); and

• Inter-and intra-fuel competition.

In the short term, factors such as new plant development, technology advances,

and reserve conditions do not significantly influence prices because these factors

often require five years or more before their effects are felt. Temperature

differences will drive up or down coal prices, depending on the season. The size of

coal stockpiles will influence coal prices as utilities replenish or use their stocks.

Short-term forecasts of natural gas will typically set a ceiling on coal prices.

Emissions allowance price forecasts will influence coal-on-coal competition.

Recently signed contracts will also influence the short-term coal market as they

give an indication of how the market prices the future value of coal.

An advantage of short-term forecasts is that often many of the input parameters

are defined. Known transportation constraints impact short-term coal prices.

Diminished rail capacity can result in higher prices for alternative coal supplies

and a reduction in stockpiles at plants that burn PRB coal. Similarly, known

supply and competitive fuel constraints will also influence coal prices. Finally,

inter- and intra-fuel competition will play a major role in the ultimate pricing of

coal. It is critical that coal models incorporate competing fuel price forecasts to

ensure that prices do not unrealistically diverge from the competition.

Factors in Coal Modeling. Long-term coal price forecasting is dependent on:

• Reserve conditions;

• Reserve accessibility;

• Transportation trends;

• Development of enhanced coal technology (such as gasification and K-Fuel

coal quality enhancement);

• Future air emissions regulations;

• New emission control construction;

• Long-term SO2 allowance price forecasts;

• New plant development; and

• Inter- and intra-fuel competition.

Weather, coal contract data, stockpile fluctuations, and mine outages do not have

significant impact on long-term coal prices as these factors are assumed to take

on a “steady-state” value over time.

For PRB coal, the dominant long-term factors are reserve conditions, reserve

accessibility, transportation constraints, technology advances, and the price of

competing coal and gas. Reserve conditions can be used to determine extraction

costs of the coal. Reserve accessibility and transportation constraints will help

determine the amount of coal that can reach the market. Technology advances

will impact future extraction and transportation costs. Technology advances such

as gasification and K-Fuel coal upgrading may also open PRB coal to new

markets. The price of coal-on-coal and gas-on-coal competition will also

determine the extent to which PRB coal will penetrate new markets and retain its

hold on existing markets.

Beyond the dominant long-term factors, other factors in the long-term forecast

will also influence PRB prices. Future air emissions will impact SO2 allowance

price forecasts. Additionally, the Clean Air Mercury Rule will impact PRB coal.

With its low halogen content, the mercury in PRB coal is more likely to turn into

gaseous elemental mercury during combustion, a form that is not captured as a

co-benefit in SO2 scrubbers and NOX SCR devices. The Clean Air Interstate Rule

will also impact the combustion of coal in the eastern 28 states. The scheduled

construction of over 50,000 MW of emission control devices will decrease the

demand for low sulfur coal. With over 68,000 MW of new coal burning power

plants proposed to be constructed over the next decade, the impact on coal

demand will be significant. The location of these plants will place affect

transportation capacity and coal prices.

Putting It All Together Global Energy’s state-of-the-art approach to Integrated Fuel Forecasting enables

the best available look at long-term supply and demand fundamentals in each

fuel stream as well as for fuel demand for power generation all seamlessly

integrated through Global Energy’s common data model, iterative simulation

modeling methodology, and our transparent, consistent, independent market

advisory service to interpret the modeling results and implications for our clients.

For more information on Global Energy’s Advisory Services talk to your account executive, visit our

home page at www.globalenergy.com or call Global Energy at 720-240-5554.

Global Energy Decisions Integrated Fuel Analysis Best Practice (2007)

Documents

Transcript of Global Energy Decisions Integrated Fuel Analysis Best Practice (2007)

STANDARDS & BEST PRACTICES

GENDER AND IMPRISONMENT DECISIONS

Aircraft fuel system

applications and decisions - GOV.UK

4 Dividend Decisions Learning Objectives

Expect the Best

Advance decisions toolkit

Fuel - CORE

Influence of the Fuel Composition and the Fuel/Oxidizer Ratio ...

Best Practice Manual

BEST PRAC BEST PRACTICES - Sophia College, Ajmer

Fuel Flowrate Control for Aeroengine and Fuel Thermal ...

Untitled - Best Aire

CHAPTER 13 - Distribution decisions

From Waste to Traffic Fuel (W-fuel)

Best District Best Block Best PHC Bikaner Choti Sarwan ...

QS Best 200

DFSMShsm Best Practices

2021 - BEST - DiversityComm

Recent Decisions - NDLScholarship