grid connected electricity storage systems (2/2)

GROWDERS & PLATOS, session 2Optimal use of storage systems

Petra de Boer Project coordinator GROWDERSRoger Cremers Developer PLATOS toolGabriël Bloemhof Consultant decision making models

14 February 201116:00 – 17:3017 February 201116:00 – 17:30

2

Introduction Workshop Leaders

Petra de Boer Roger Cremers Gabriël Bloemhof

Energy storageElectric vehiclesSmart Grids

Power FactoryDeveloper PLATOS

Energy systemsGrid integrationOptimization

3

Agenda

14 February 2011, 16:00 – 17:30– Introduction of GROWDERS project– Benefits of grid connected storage– 4 field tests in Europe using storage– Decision making model in PLATOS

17 February 2011, 16:00 – 17:30 – Introduction of PLATOS – Possible applications– Demonstration of PLATOS

4

Introduction of GROWDERS

EC funded under 6th Framework Programme Coordinated by KEMA

Goal: To demonstrate the technical and economical possibilities of existing electricity storage technologies.

– Realization of Transportable Flexible storage systems

– Realization of an Assessment tool for optimal distribution network management

– Description of conceptual directions for EU regulatory framework

Grid Reliability and Operability with Distributed Generation using Transportable Storage

5

Applications of Electricity Storage

T&D:

Asset management

Voltage control

Power quality

Grid stability

Trading/Generation:

Control / load following

Energy management

Peak generation

Load levelling

System operators:

Frequency control

Spinning reserve

Balancing

End user (industry) :

UPS / Ride Through / Shut down

Peak shaving

optimization of energy purchase by load shifting

(Reactive power)

Renewable:

Decoupling demand and source availability

Control and integration

6

New development

Generate alternative solutions

Technical evaluation per alternative solution (check constraints)

Per alternative solution:Define optimal investment phasesEvaluate expected objectives (costs, reliability, image, …)

Decide (with uncertainty)

Inventory / problem definition

Scenarios + probabilities

Re-evaluate

Physical implementation of first step

Summary decision making process

7

INTRODUCTION OF PLATOS

8

What is PLATOS?

Tool that assists network planners to optimise the location, size and types of energy storage systems in electrical power systems

Developed within GROWDERS project– Demonstration of technical and economical possibilities of

existing electricity storage technologies

9

Need for new tool

Utilities are faced with increasing number of distributed energy sources. Storage devices can facilitate the implementation of these sources in the power system

The implementation of storage devices in power systems faces the utilities with a lot a of questions that need to be answered

A tool can assist to address the relevant issues involved with storage applications

10

Typical questions

Can a storage system alleviate the problems in my distribution network?

I have a limited amount of money to buy storage systems. What systems should I buy?

I need a certain amount of storage capacity in my power system. Should I buy only one storage device or multiple smaller devices?

Can storage based solutions compete with classical solutions?

11

Main question

WHAT IS THE

OPTIMAL STORAGE BASED SOLUTION

FOR MY POWER SYSTEM?

12

Requirements for the new tool

Automatic generation of storage based solutions Automatic technical and economical assessment of

individual solutions Comparison of storage based solutions with classical

solutions Clear presentation of results

13

Main modules of PLATOS

Alternativesolutionsmodule

Optimisationmodule

Systemanalysismodule

14

System analysis module

Needed for standard system analyses– Load flow calculations– Short circuit calculations

Alternativesolutionsmodule

Optimisationmodule

Systemanalysismodule

15

Optimisation module

Needed for automatic generation of solutions Needed for automatic assessment of solution

performance

Alternativesolutionsmodule

Optimisationmodule

Systemanalysismodule

16

Alternative solutions module

Needed for comparison of storage based solutions with classical solutions to network problems

Alternativesolutionsmodule

Optimisationmodule

Systemanalysismodule

17

Realisation of the tool

All PLATOS modules are realised within the Digsilent PowerFactory simulation package (a well known and widely used tool for power system analysis)

Advantages– All standard power system analysis tools are readily available– Graphical user interface already present– Easy access to library of power system components– Powerful macro programming language– Easy access to all relevant parameters

18

OPTIMISATION MODULE

19

Optimisation

Optimizing the location, type and size of mobile storage systems is a combinatorial problem with many possible solutions

Key question is how to find global optimum in an efficient way

20

Number of possible solutions

Example– Power system with 100 nodes– 2 storage units to be installed 4950

2

100

Number of possible solutions

0.0E+00

2.0E+06

4.0E+06

6.0E+06

8.0E+06

1.0E+07

1 2 3 4 5

Desired number of storage units

Nu

mb

er

of

so

luti

on

s

50 nodes

100 nodes

21

Genetic algorithms

The combinatorial problem is solvedby application of an artificial evolution,in particular using a genetic algorithm

Basics of genetic algorithm– Step 1: Create random solutions

– Step 2: Analyze all solutions

– Step 3: Select the best solutions

– Step 4: Create new solutions based on the best ones

– Step 5: Go to step 2

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Artificial evolution

Old solutions

Best solutions

New solutions

Select Inherit

Mutate

Repeat

23

Create 1000 random solutions

Analyze each solution: Costs, Benefits, Performance…

Select best 50 solutions

Generate 1000 new solutions on best 50:Survival, Inheritance, Mutation, Cross-over

Repeat until convergence

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Solutiongenerator

Alleviationalgorithm 1

Alleviationalgorithm 2

Tradingalgorithm

Alleviationalgorithm 3

Solutionassessment

Output dataprocessing

Performanceindicator

Performanceindicator

Performanceindicator

Performanceindicator

Overallperformanceindicator

Input dataprocessing

Basic design of optimization module

25

The optimization process

The optimisation process can be influenced by many factors:– Number of generations– Number of genes– Mutation parameters

Storage location mutation parameter Storage type mutation parameter Storage size mutation parameter

By choosing certain parameters the user defines his optimization strategy

26

27

28

29

Power system modelling

Network topology Component types (impedances, typical costs) Load types Load and generation patterns Number, location and size of fixed storage systems

Electricity demand House A Meekspolder

00.10.2

0.30.40.50.60.7

0.80.9

1

0 1 2 3 4 5 6 7

Day of week

Po

wer

[kW

]

Winter

30

Required input data

Network data– Load patterns– Component data– Network topology– Generation patterns

Solution space for optimization routine– Number, location and size of fixed storage units– Power system components to be monitored– Data required for assessment of solutions– Number of storage systems– Type of storage systems– Size of storage systems

31

Variables related to available storage systems

Available types of storage systems– Maximum discharging power– Minimum charging power

Available sizes of storage systems Typical costs

– EURO/kW– EURO/kWh

Maximum number of discharging cycles

XS

M

XS

+

-

L

XL

+

-

32

Other variables

Variables related to genetic algorithm– Including user defined solutions

Variables related to optimisation process Variables related to calculation of performance

– Appraisals– Penalties

33

Algorithms within PLATOS

Alternative solutions algorithms Genetic algorithm Overload alleviation algorithm Voltage alleviation algorithm Advanced trading algorithms Dip alleviation algorithm Storage management algorithm

34

Alternative solutions

Automatic assessment of alternative (e.g. classical) solutions to the network problems– Other tap changer settings– Replacement of power connections– Additional power connections

Result of assessment is used as starting point for assessment of storage based solutions

35

Features of genetic algorithm

Automatic generation of storage solutions Generation of solutions is influenced by calculated

performance of previous solutions Automatic rejection of bad solutions e.g. solutions that

have– too high investment costs– too small storage capacity– too small charging and discharging power– too large charging and discharging power

Possibility for providing educated guesses too speed up the optimisation process

+ =

36

Features of overload alleviation algorithm

Automatic determination of overload locations and overload severity

Automatic determination of required storage capacity to solve the overloading problems

Automatic determination of power setpoints for storage inverters taking into account operating constraints (minimum SOC, maximum SOC)

Automatic determination of performance indicator taking into account– Effect of storage on overload – Investment costs– Energy losses– Used storage cycles

Graphical representation of results

37

Features of voltage alleviation algorithm

Automatic determination of locations with under and/or overvoltage conditions

Automatic determination of required storage capacity to solve the voltage problems

Automatic determination of power setpoints for storage inverters taking into account operating constraints

Automatic determination of performance indicator taking into account– Effect of storage on voltage – Investment costs– Energy losses– Used storage cycles

Graphical representation of results

38

Features of voltage dip alleviation algorithm

Automatic determination of required storage capacity to alleviate voltage dips at predefined locations

Automatic determination of power setpoints for storage inverters taking into account operating constraints

Automatic determination of performance indicator Dedicated inputs

– Voltage dip tables– Cost table

39

PERFORMANCE INDICATORS

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Performance indicators (1/2)

Performance indicators indicate the performance of each individual solution

Performance indicators are expressed in terms of EUR Performance indicators take into account the costs

and benefits of a particular solution Number and type of performance indicators to be used

are determined by the user

Performance indicator = Benefits - Costs

4141

Performance indicators (2/2)

80.0060.0040.0020.000.00 [-]

1.25E+6

1.00E+6

7.50E+5

5.00E+5

2.50E+5

0.00E+0

-2.50E+5

Opti: Performance

80.0060.0040.0020.000.00 [-]

4.00E+5

3.00E+5

2.00E+5

1.00E+5

0.00E+0

-1.00E+5

Opti: OA_PIOpti: OA_CostsOpti: OA_Benefits

80.0060.0040.0020.000.00 [-]

8.00E+5

6.00E+5

4.00E+5

2.00E+5

0.00E+0

-2.00E+5

Opti: VA_PI

Opti: VA_CostsOpti: VA_Benefits

DIg

SIL

EN

T

42

PLATOS output

Output will include:– Optimal locations of storage systems

– Optimal number and type of storage systems

– Required specifications for storage system

– Optimal set points for storage systems

– Performance indicator of each algorithm and each individual solution

Results are available in Excel and graphically

43

Graphical output of PLATOS (1/3)

2

50 kWh

2

50 kWh

2

50 kWh

2

50 kWh

2

50 kWh

3

100 kWh

5

2 kWh

4

1000 kWh

3

200 kWh

7

5 kWh

Solutions withoutperformance indicator

Evaluated solution

44

45

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Main features of PLATOS

Optimization of storage application in power systems– Optimization of location, size and type– Optimization criteria can be changed by the user– Monitoring of optimization process

Performance indicators can be defined by the user– Definition of points of interest within power system– Both technical and economical performance indicators

Graphical and tabular output

Comparison with classical non storage based solutions

User definable load and generation patterns

Tool can be used for each voltage level

47

POSSIBLE APPLICATIONS

48

Possible applications of PLATOS

Use as planning tool– Development of storage application alternatives that fulfill

predifined objectives of the user without exceeding technical or economical constraints. Planning of new storage systems in existing power systems Relocation of existing storage systems in existing power systems Planning of charging facilities for electric vehicles

49

Possible applications of PLATOS (2)

Use as analysis tool– Assessement of benefits of specific storage systems with

regard to voltage improvement, load alleviation, dip alleviation etc.

– Analysis of different charging and discharging regimes– Determination of required storage size and power

50

Possible applications of PLATOS (3)

Use as buying tool– Potential buyers of equipment (e.g. storage systems, inverters

etc.) can use the tool to compare bids of different suppliers

51

Possible applications of PLATOS (4)

Use as selling tool– Manufacturers of equipment (e.g. storage systems, inverters)

can use the tool to convince potential customers of the advantages of using their equipment

52

Typical network problems

Typical problems in a power system– Undervoltage at the end of the feeder– Overload at the beginning of the feeder– Voltage dip (caused by e.g. short circuit in adjacent feeder)

Questions:– Can storage solve the problem?– What is the optimal location for storage?– What is the optimal size of the storage?– What is the optimal power of the storage?– Remaining issues

53

What is the optimal location, size, type?

Answer depends on many different factors PLATOS considers:

– Desired voltage profile at specific locations– Storage system should not introduce other network problems– Energy losses– Economics– Required minimum discharging power to alleviate network problems– Duration of the network problems– Required minimum absolute charging power to charge when possible– Ampacity of power system components – Storage system should be able to supply a known power during a known period– Storage system should store a known amount of energy within a known period– A storage system with a size larger than required is not useful for solving the

network problems– Power system should be able to accommodate for a certain storage size

54

Other application of PLATOS

Second Life project– Main question: is it possible to use depriciated car batteries for

storage purposes in distribution systems? Topics

– Capacity of depriciated car batteries– Remaining life time– Relationship between number of discharging cycles and life

time– Benefits of using old car batteries

PLATOS– Gives insight in the problem– Can help finding the answers to the questions posed

55

Conclusions

Storage systems can be applied for many different purposes

The optimal location, type and size of storage system to be used depends on many factors

The more functions the storage system needs to fullfill, the more complex the decisions with regard to using storage systems become

PLATOS can support the decision making process by – Providing better insight in the problems– Providing solutions that can be compared in a tranparent way

56

DEMONSTRATION

57

Running cases in PLATOS

Steps– Step 1: Make model of power system– Step 2: Connect loads and generators– Step 3: Set load and generation patterns– Step 4: Create input textfile– Step 5: Run PLATOS– Step 6: Evaluate results

58

59

Showing nodes with voltage below set lower voltage level... ==============================SOLUTION SPACE DETAILS

Actual solution space number: 1Number of generations: 4

Number of genes per generation: 20Number of best genes per generation: 4Desired number of storage locations: 3

==============================

===========================SIMULATION PROGRESS

Completed generations: 0.00 %Completed solutions: 0.00 %===========================

===========================ACTUAL SOLUTION DETAILS

Actual solution space: 0 Actual solution number: 0 Overall solution number: 0

===========================

===========================SIMULATION RESULTS

Actual performance: -10000000.00 Best performance: -10000000.00

Best solution number: 0 ===========================

Actual performance: -10000000.00

======================================SECOND ALTERNATIVE VA SOLUTION

Desired cable type:

======================================

DIg

SIL

EN

T

Network problems

60

Nodes and connections to be monitoredShowing monitored power system components... ==============================

SOLUTION SPACE DETAILS

Actual solution space number: 1Number of generations: 4

Number of genes per generation: 20Number of best genes per generation: 4Desired number of storage locations: 3

==============================

===========================SIMULATION PROGRESS

Completed generations: 0.00 %Completed solutions: 0.00 %===========================

===========================ACTUAL SOLUTION DETAILS

Actual solution space: 0 Actual solution number: 0 Overall solution number: 0

===========================

===========================SIMULATION RESULTS

Actual performance: -10000000.00 Best performance: -10000000.00

Best solution number: 0 ===========================

Actual performance: -10000000.00

======================================SECOND ALTERNATIVE VA SOLUTION

Desired cable type:

======================================

DIg

SIL

EN

T

61

Solution 1

62

Solution 3

63

Solution 6

64

Solution 23

65

Solution 59

66

Solution 62

67

Solution 80

68

Performance

80.0060.0040.0020.000.00 [-]

1.25E+6

1.00E+6

7.50E+5

5.00E+5

2.50E+5

0.00E+0

-2.50E+5

Opti: Performance

80.0060.0040.0020.000.00 [-]

4.00E+5

3.00E+5

2.00E+5

1.00E+5

0.00E+0

-1.00E+5

Opti: OA_PIOpti: OA_CostsOpti: OA_Benefits

80.0060.0040.0020.000.00 [-]

8.00E+5

6.00E+5

4.00E+5

2.00E+5

0.00E+0

-2.00E+5

Opti: VA_PI

Opti: VA_CostsOpti: VA_Benefits

DIg

SIL

EN

T

69

Conclusions

Storage systems can be applied for many different purposes

The optimal location, type and size of storage system to be used depends on many factors

The more functions the storage system needs to fullfill, the more complex the decisions with regard to using storage systems become

PLATOS can support the decision making process by – Providing better insight in the problems– Providing solutions that can be compared in a tranparent way

70

Workshop

Wednesday 11th May 2011 in MannheimIncluding a site visit to the storage systems

For more information contact Petra de BoerPetra.deboer@kema.com+31 26 356 2552

Thank you for your attention

www.growders.eu

Petra de Boer Roger Cremers Gabriël Bloemhof+31 26 356 25 52 + 31 26 356 3240 + 31 26 356 6150petra.deboer@kema.com roger.cremers@kema.com gabriel.bloemhof@kema.com