Planning and Operation in Smart Grids - SMAGRINET

Planning and Operation in Smart Grids

Technische Universität Berlin

25th November 2020 – Train the Trainers Workshop

TUB: MODULE 3 – PLANNING AND OPERATION IN SMART GRIDS

Agenda

1. Module Overview

2. Lectures and Content

3. Intended Learning Outcomes

4. Learning and Teaching Methods

5. Assessment and Grading

6. Conclusion


Module Overview


Module OverviewMotivation

Old World: New World:

Source: Pixabay, https://pixabay.com/

„supply followsdemand“

„demand followssupply“

4


Module OverviewLecture Topics

Modern Power Systems

RenewableGeneration

Distribution Network

Demand Characteristics

Power Quality

State Estimation

EnergyStorage

Technologies

ElectricVehicle

Virtual Power Plant

Forecasting

5


Lectures and Content


Lectures and ContentTechnische Universität Berlin

7


RenewableGeneration



Power Quality

State Estimation

EnergyStorage

Technologies

ElectricVehicle

Virtual Power Plant

Forecasting


Lectures and ContentTechnische Universität Berlin

8


RenewableGeneration



Power Quality

State Estimation

EnergyStorage

Technologies

ElectricVehicle

Virtual Power Plant

Forecasting


Lectures and ContentRenewable Generation + Distribution Network I

Content:

• Renewable Energies

• Photovoltaics: Generation, Planning, and Measurement

• Wind: Generation, Planning, and Measurement

• Network Structure Basics

• Properties of Electric Energy Networks

• Network Operation

9


Wind: Dimensioning and PlanningWind Statistics – Weibull Function

For describing !(#), the Weibull probability density function !! # is used:

!! # = &' ⋅ #

'"#$

⋅ )#%&

!

0

0.05

0.1

0.15

0.2

0.25

0 5 10 15 20 25

k=2 (c=8)

k=3 (c=8)

c=6 (k=2)

c=4 (k=2)

shapeparameter

scaleparameter

&

'EXAMPLE

10


Network Operation: N-1 Criterion

• The N-1 criterion requires that a network be operated in such a way that, in the case of any failure, no other device is overloaded

• The N-1 criterion shall now

be discussed with a short example

NeighbouringNetwork

Neighbouring Network

EXAMPLE

11



Learning Outcomes:

Students should be able to...

• ...know the generation principles of renewable energiessuch as wind and photovoltaics.

• ...have the statistical knowledge to plan wind and PV generation units.

• ...evaluate grid structures and network operation.

12



Teaching Methods:

Lecture Lab Work

Topic: Rayleigh statistics

Paper and pen exercise

+

13


Lectures and ContentDistribution Network II

Content:

• Motivation for Power Flow Calculations

• Per-Phase Formulation for Balanced Systems

• Gauss Method

• Three-Phase Power Flow Formulation

• Three-Phase Load and Line Model

• Three-Phase Distribution Power Flow

14


Power Flow CalculationsPer-Phase Formulation for Balanced Systems

15å

å

=

-=

--

---

-=

-=

--=-=-=

n

itttsi

n

nttts

nnnns

nnnn

nnsn

IZVV

IZV

IZIZVIZVVIZVV

!1

11

111

:Voltages Node

å

å

=

=

-=

-=

-+=

-=

t

m m

mmt

m m

mm

VjQPI

VjQPVjQPII

VjQPI

1*

2

1*

*2

2212

*1

111

:Currents Line

!

Node Voltages: Line currents:

EXAMPLE


Power Flow CalculationsThree-Phase Distribution Power Flow

• Then the iteration scheme for the three-phase power flow is given in detail as follows:

with ! = 1,2,… , '16

!"#(%) = !'# −$

()"

*% (+ $

,)+

( &,# − '(,#!,# %-+ ∗ + % (. $

,)+

( &,/ − '(,/!,/ %-+ ∗ + % (0 $

,)+

( &,1 − '(,1!,1 %-+

∗

!"/(%) = !'/ −$

()"

*% (. $

,)+

( &,# − '(,#!,# %-+ ∗ + % (2 $

,)+

( &,/ − '(,/!,/ %-+ ∗ + % (3 $

,)+

( &,1 − '(,1!,1 %-+

∗

!"1(%) = !'1 −$

()"

*% (0 $

,)+

( &,# − '(,#!,# %-+ ∗ + % (3 $

,)+

( &,/ − '(,/!,/ %-+ ∗ + % (4 $

,)+

( &,1 − '(,1!,1 %-+

∗

EXAMPLE



Learning Outcomes:


• ...explain the importance of three-phase power flowcalculations in the distribution network.

• ...understand the per-phase formulation of three-phase power flow equations.

• ...use the Gauss method.

• ...perform a three-phase power flow.

17



Teaching Methods:

Lecture Lab Work

Topic: 3-phase power flow

MATLAB exercise

+

18


Lectures and ContentDemand Characteristics

Content:

• From Consumer to Prosumer

• Household Energy Demand and Production

• Business Prosumer Characteristics

• Industry Prosumers

• Energy Passport

19


Household Energy Demand and ProductionEnergy Consumption in EU Households

20

EXAMPLE


Business Prosumer CharacteristicsDuring winter and early spring

21

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21

Monday Tuesday Wednesday Thursday Friday Saturday Sunday

[MWh/h]

March Prosumer Characteristics

Wind Generator Consumption PV panel CHP

EXAMPLE



Learning Outcomes:


• ...know the difference between consumer and prosumer andwhy the focus is shifting.

• ...understand different demand characteristics, as observedin households, businesses and industry.

• ...evaluate the European energy passport approach.

22



Teaching Methods:

Lecture Lab Work

Topic: prosumer

Spreadsheet program exercise

+

23


Lectures and ContentEnergy Storage Technologies

Content:

• Energy Systems in Transition

• Energy Storage in Smart Grids

• Multi-energy Smart Grids

• Modeling Framework for Planning and Control of Multi-energySystems

• Modeling of Selected Resources

• Introduction to Optimization

• Research Project „Energy Network Berlin Adlershof“24


EXAMPLE

25


3. Multi-energy Smart GridsOverview

• Multi-energy systems (MES) can provide increased flexibility for integration of volatile renewable power generators.

• Electric part of power system benefits from availableflexibility in networks of otherenergy carriers.

• Those networks are connected to the electric power system by power conversion units.

EXAMPLE

26



Learning Outcomes:


• ...explain the importance of energy storage in smart grids.

• ...describe the concepts of multi-level storage and multi-energy smart grids.

• ...set up a model for multi-energy smart grids.

• ...formulate optimization problems including objectivefunction, constraints and bounds.

27



Teaching Methods:

Lecture Lab Work

Topic: linear optimization

Paper and pen exercise

+

28


Lectures and ContentVirtual Power Plant

Content:

• Necessity for Virtual Power Plants

• Structure of Virtual Power Plant

• Modelling and Market Integration of Resources

• Provision of System Services: Congestion Management in Distribution Networks

29


Bidirectional communication with single resourcesBidirectional communication at higher aggregation level

EXAMPLE

30


Successful congestion relief

2

3

4

5

6

7

8

9

10

11

12110/20 kV 110/20 kV

13

14

S2

S3

S1

LoadTransformerBus

1

Switch/CBPV

Wind park

Electrical storage

CHP unit

CHP CHP Th

Thermal storage

Th

CHP

Th

DSO

Congestion relieved without curtailment of renewables

EXAMPLE

31



Learning Outcomes:


• ...understand the role of a virtual power plant for the marketintegration of small distributed energy resources.

• ...comprehend the operation of a virtual power plant andaccount for uncertainties in the renewable power generationforecasts in different time frames, in particular the day-ahead and intraday operation.

• ...understand the role of a virtual power plant for theprovision of a congestion relief service to the distributionsystem operator. 32



Teaching Methods:

Lecture

33


Intended Learning Outcomes


Intended Learning OutcomesTechnische Universität Berlin



RenewableGeneration



Power Quality

State Estimation

EnergyStorage

Technologies

ElectricVehicle

Virtual Power Plant

Forecasting




• …know the fundamentals about generation and planning ofrenewable energies.


RenewableGeneration



Power Quality

State Estimation

EnergyStorage

Technologies

ElectricVehicle

Virtual Power Plant

Forecasting




• …describe the distribution network structure and performthree-phase-power-flow analysis.


RenewableGeneration



Power Quality

State Estimation

EnergyStorage

Technologies

ElectricVehicle

Virtual Power Plant

Forecasting




• …know different demand characteristics and define the termsconsumer and prosumer.


RenewableGeneration



Power Quality

State Estimation

EnergyStorage

Technologies

ElectricVehicle

Virtual Power Plant

Forecasting




• …evaluate the impact of energy storage possibilities andmulti-energy smart grids.


RenewableGeneration



Power Quality

State Estimation

EnergyStorage

Technologies

ElectricVehicle

Virtual Power Plant

Forecasting




• …evaluate the impact of virtual power plants, in particularthe day-ahead and intraday operation.


RenewableGeneration



Power Quality

State Estimation

EnergyStorage

Technologies

ElectricVehicle

Virtual Power Plant

Forecasting


Learning and Teaching Methods


Learning and Teaching MethodsBerlin January 2020

One Week Intense Course

Lectures

Lab work

Field trip

≈ 60 %*

≈ 30 %*

≈ 10 %*Lab demo andSmart.Grid app

* only TUB content, without guest lecturers42


Assessment and Grading


Assessment and GradingTechnische Universität Berlin

45

Final Grade

Writtentest

Project work

Writtentest

• 50 % of total grade

• 80 minutes written test

• Focus on paper and pen and spreadsheet exercises, as wellas general knowledge questions



46

Final Grade

Writtentest

Project work

Project Outcome:

• Independent student work, 2-3 students per project group

• Given task to be solved with research, MATLAB simulation, and result analysis



47

Final Grade

Writtentest

Project work

Preliminary Presentation:


• 10 minutes presentation

Written Report:


• 15-40 pages



48

Lectures andexercises

40-60 hours

6 ECTS = 150-180 hours

Class presence

Preparationtime

Project work

Includingpresentationand report

65-75 hours

Lectures andwritten test

45 hours


Conclusion


ConclusionState of the Art Topics on Smart Grid

50


Power Quality

EnergyStorage

Technologies

Forecasting


ConclusionFurther Benefits

• Modern media: computers, app, video conferences, CAD

• International compatibility: ECTS points, language English

• Integrated structure: lectures, assignments in class and at

home, projects

• Soft skill training: team orientation and cooperation,

presentation in oral and in writing, project-orientation

with scheduling

51


ConclusionFurther ReadingsRenewable Energies:

G. M. Masters: Renewable and Efficient Electric Power Systems, 2. Auflage, John Wiley & Sons inc., 2013, Hoboken, New Jersey, USA.

Multi-energy Smart Grids:

S. Bschorer, M. Kuschke and K. Strunz, "Object-oriented modeling for planning andcontrol of multi-energy systems," in CSEE Journal of Power and Energy Systems, vol. 5, no. 3, pp. 355-364, Sept. 2019, doi: 10.17775/CSEEJPES.2019.00650.

K. Strunz and H. Louie, “Cache Energy Control for Storage: Power System Integration and Education Based on Analogies Derived From Computer Engineering,” in IEEE Transactions on Power Systems, vol. 24, no. 1, pp. 12-19, Feb. 2009.

Virtual Power Plant:

D. Koraki and K. Strunz, "Wind and Solar Power Integration in Electricity Markets andDistribution Networks Through Service-Centric Virtual Power Plants," in IEEE Transactions on Power Systems, vol. 33, no. 1, pp. 473-485, Jan. 2018, doi: 10.1109/TPWRS.2017.2710481. 52

TUB: MODULE 3 – CONNECTION PLANNING IN SMART GRIDS

Thank you for your attention!

Christine Michalek, [email protected]

Prof. Dr.-Ing. Kai [email protected]

mailto:[email protected]

mailto:[email protected]

Planning and Operation in Smart Grids - SMAGRINET

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