Grid Connected Electricity Storage Systems (2/2)
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Transcript of 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
22
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
24
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
40
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
46
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 [email protected]+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 [email protected] [email protected] [email protected]