GROWDERS & PLATOS, session 2 Optimal use of storage systems Petra de Boer Project coordinator GROWDERS Roger Cremers Devel...
Introduction Workshop Leaders Energy storage Electric vehicles Smart Grids Petra de Boer Energy systems Grid integration O...
Agenda <ul><li>14 February 2011, 16:00 – 17:30 </li></ul><ul><ul><li>Introduction of GROWDERS project </li></ul></ul><ul><...
Introduction of GROWDERS <ul><li>EC funded under 6 th  Framework Programme </li></ul><ul><li>Coordinated by KEMA </li></ul...
Applications of Electricity Storage T&D: Asset management Voltage control Power quality Grid stability Trading/Generation:...
Summary decision making process New development Generate alternative solutions Technical evaluation per alternative soluti...
INTRODUCTION OF PLATOS
What is PLATOS? <ul><li>Tool that assists network planners to optimise the location, size and types of energy storage syst...
Need for new tool <ul><li>Utilities are faced with increasing number of distributed energy sources. Storage devices can fa...
Typical questions <ul><li>Can a storage system alleviate the problems in my distribution network? </li></ul><ul><li>I have...
Main question <ul><li>WHAT IS THE  </li></ul><ul><li>OPTIMAL STORAGE BASED SOLUTION  </li></ul><ul><li>FOR MY POWER SYSTEM...
Requirements for the new tool <ul><li>Automatic generation of storage based solutions </li></ul><ul><li>Automatic technica...
Main modules of PLATOS Alternative solutions module Optimisation module System analysis module
System analysis module <ul><li>Needed for standard system analyses </li></ul><ul><ul><li>Load flow calculations </li></ul>...
Optimisation module <ul><li>Needed for automatic generation of solutions </li></ul><ul><li>Needed for automatic assessment...
Alternative solutions module <ul><li>Needed for comparison of storage based solutions with classical solutions to network ...
Realisation of the tool <ul><li>All PLATOS modules are realised within the Digsilent PowerFactory simulation package (a we...
OPTIMISATION MODULE
Optimisation <ul><li>Optimizing the location, type and size of mobile storage systems is a combinatorial problem with many...
Number of possible solutions <ul><li>Example </li></ul><ul><ul><li>Power system with 100 nodes </li></ul></ul><ul><ul><li>...
Genetic algorithms <ul><li>The combinatorial problem is solved by application of an artificial evolution, in particular us...
Artificial evolution Old solutions Best solutions New solutions Select Inherit Mutate Repeat
Create 1000 random solutions  Analyze each solution:  Costs, Benefits, Performance…  Select best 50 solutions Generate 100...
Basic design of optimization module Solution generator Alleviation algorithm 1 Alleviation algorithm 2 Trading algorithm A...
The optimization process <ul><li>The optimisation process can be influenced by many factors: </li></ul><ul><ul><li>Number ...
 
 
 
Power system modelling <ul><li>Network topology </li></ul><ul><li>Component types (impedances, typical costs) </li></ul><u...
Required input data <ul><li>Network data </li></ul><ul><ul><li>Load patterns </li></ul></ul><ul><ul><li>Component data </l...
Variables related to available storage systems <ul><li>Available types of storage systems </li></ul><ul><ul><li>Maximum di...
Other variables <ul><li>Variables related to genetic algorithm </li></ul><ul><ul><li>Including user defined solutions </li...
Algorithms within PLATOS <ul><li>Alternative solutions algorithms </li></ul><ul><li>Genetic algorithm </li></ul><ul><li>Ov...
Alternative solutions <ul><li>Automatic assessment of alternative (e.g. classical) solutions to the network problems </li>...
Features of genetic algorithm <ul><li>Automatic generation of storage solutions </li></ul><ul><li>Generation of solutions ...
Features of overload alleviation algorithm <ul><li>Automatic determination of overload locations and overload severity </l...
Features of voltage alleviation algorithm <ul><li>Automatic determination of locations with under and/or overvoltage condi...
Features of voltage dip alleviation algorithm <ul><li>Automatic determination of required storage capacity to alleviate vo...
PERFORMANCE INDICATORS
Performance indicators  (1/2) <ul><li>Performance indicators indicate the performance of each individual solution </li></u...
Performance indicators  (2/2)
PLATOS output <ul><li>Output will include: </li></ul><ul><ul><li>Optimal locations of storage systems </li></ul></ul><ul><...
Graphical output of PLATOS  (1/3) Solutions without performance indicator Evaluated solution
 
 
Main features of PLATOS <ul><li>Optimization of storage application in power systems </li></ul><ul><ul><li>Optimization of...
POSSIBLE APPLICATIONS
Possible applications of PLATOS <ul><li>Use as planning tool </li></ul><ul><ul><li>Development of storage application alte...
Possible applications of PLATOS (2) <ul><li>Use as analysis tool </li></ul><ul><ul><li>Assessement of benefits of specific...
Possible applications of PLATOS (3) <ul><li>Use as buying tool </li></ul><ul><ul><li>Potential buyers of equipment (e.g. s...
Possible applications of PLATOS (4) <ul><li>Use as selling tool </li></ul><ul><ul><li>Manufacturers of equipment (e.g. sto...
Typical network problems <ul><li>Typical problems in a power system </li></ul><ul><ul><li>Undervoltage at the end of the f...
What is the optimal location, size, type? <ul><li>Answer depends on many different factors </li></ul><ul><li>PLATOS consid...
Other application of PLATOS <ul><li>Second Life project </li></ul><ul><ul><li>Main question: is it possible to use deprici...
Conclusions <ul><li>Storage systems can be applied for many different purposes </li></ul><ul><li>The optimal location, typ...
DEMONSTRATION
Running cases in PLATOS <ul><li>Steps </li></ul><ul><ul><li>Step 1: Make model of power system </li></ul></ul><ul><ul><li>...
 
Network problems
Nodes and connections to be monitored
Solution 1
Solution 3
Solution 6
Solution 23
Solution 59
Solution 62
Solution 80
Performance
Conclusions <ul><li>Storage systems can be applied for many different purposes </li></ul><ul><li>The optimal location, typ...
Workshop <ul><ul><li>Wednesday 11th May 2011 in Mannheim </li></ul></ul><ul><ul><li>Including a site visit to the storage ...
Thank you for your attention www.growders.eu Petra de Boer  Roger Cremers  Gabriël Bloemhof +31 26 356 25 52  + 31 26 356 ...
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Grid Connected Electricity Storage Systems (2/2)

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Development and use of Renewable Energy Sources is one of the key elements in European Electricity Research. However, connecting energy sources such as photovoltaics and wind turbines to the electricity grid causes significant effects on these networks. Bottlenecks are stability, security, peaks in supply & demand and overall management of the grid. Energy storage systems provide means to overcome technical and economic hurdles for large-scale introduction of distributed sustainable energy sources. The GROW-DERS project (Grid Reliability and Operability with Distributed Generation using Flexible Storage) investigates the implementation of (transportable) distributed storage systems in the networks. The project is funded by the European Commission (FP6) and the consortium partners are KEMA, Liander, Iberdrola, MVV, EAC, SAFT, EXENDIS, CEA-INES and IPE.

In this project 3 storage systems (2 Li-ion battery systems and a flywheel) have been demonstrated at different test locations in Europe. Additionally, a dedicated software tool, PLATOS (PLAnning Tool for Optimizing Storage), has been developed by KEMA to optimize the energy management of electricity networks using storage. For each network, the location, size and type of storage systems is evaluated for all possible configurations and the most attractive option is selected.

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  • GROWDERS partners KEMA, Netherlands - project coordinator SAFT, France CEA, France Exendis, Netherlands IPE, Poland Liander, Netherlands Electricity Authority of Cyprus, Cyprus MVV, Germany IBERDROLA, Spain
  • Storage appears to have an application in all areas of the grid – current &amp; future.
  • You can recognise the solution generator
  • Voorbeeld met 200 oplossingen
  • Grid Connected Electricity Storage Systems (2/2)

    1. 1. GROWDERS & PLATOS, session 2 Optimal use of storage systems Petra de Boer Project coordinator GROWDERS Roger Cremers Developer PLATOS tool Gabriël Bloemhof Consultant decision making models 14 February 2011 16:00 – 17:30 17 February 2011 16:00 – 17:30
    2. 2. Introduction Workshop Leaders Energy storage Electric vehicles Smart Grids Petra de Boer Energy systems Grid integration Optimization Power Factory Developer PLATOS Roger Cremers Gabriël Bloemhof
    3. 3. Agenda <ul><li>14 February 2011, 16:00 – 17:30 </li></ul><ul><ul><li>Introduction of GROWDERS project </li></ul></ul><ul><ul><li>Benefits of grid connected storage </li></ul></ul><ul><ul><li>4 field tests in Europe using storage </li></ul></ul><ul><ul><li>Decision making model in PLATOS </li></ul></ul><ul><li>17 February 2011, 16:00 – 17:30 </li></ul><ul><ul><li>Introduction of PLATOS </li></ul></ul><ul><ul><li>Possible applications </li></ul></ul><ul><ul><li>Demonstration of PLATOS </li></ul></ul>
    4. 4. Introduction of GROWDERS <ul><li>EC funded under 6 th Framework Programme </li></ul><ul><li>Coordinated by KEMA </li></ul><ul><li>Goal: To demonstrate the technical and economical possibilities of existing electricity storage technologies. </li></ul><ul><ul><li>Realization of Transportable Flexible storage systems </li></ul></ul><ul><ul><li>Realization of an Assessment tool for optimal distribution network management </li></ul></ul><ul><ul><li>Description of conceptual directions for EU regulatory framework </li></ul></ul>Grid Reliability and Operability with Distributed Generation using Transportable Storage
    5. 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. 6. Summary decision making process New development Generate alternative solutions Technical evaluation per alternative solution (check constraints ) Per alternative solution: Define optimal investment phases Evaluate expected objectives (costs, reliability, image, …) Decide (with uncertainty) Inventory / problem definition Scenarios + probabilities Re-evaluate Physical implementation of first step
    7. 7. INTRODUCTION OF PLATOS
    8. 8. What is PLATOS? <ul><li>Tool that assists network planners to optimise the location, size and types of energy storage systems in electrical power systems </li></ul><ul><li>Developed within GROWDERS project </li></ul><ul><ul><li>Demonstration of technical and economical possibilities of existing electricity storage technologies </li></ul></ul>
    9. 9. Need for new tool <ul><li>Utilities are faced with increasing number of distributed energy sources. Storage devices can facilitate the implementation of these sources in the power system </li></ul><ul><li>The implementation of storage devices in power systems faces the utilities with a lot a of questions that need to be answered </li></ul><ul><li>A tool can assist to address the relevant issues involved with storage applications </li></ul>
    10. 10. Typical questions <ul><li>Can a storage system alleviate the problems in my distribution network? </li></ul><ul><li>I have a limited amount of money to buy storage systems. What systems should I buy? </li></ul><ul><li>I need a certain amount of storage capacity in my power system. Should I buy only one storage device or multiple smaller devices? </li></ul><ul><li>Can storage based solutions compete with classical solutions? </li></ul>
    11. 11. Main question <ul><li>WHAT IS THE </li></ul><ul><li>OPTIMAL STORAGE BASED SOLUTION </li></ul><ul><li>FOR MY POWER SYSTEM? </li></ul>
    12. 12. Requirements for the new tool <ul><li>Automatic generation of storage based solutions </li></ul><ul><li>Automatic technical and economical assessment of individual solutions </li></ul><ul><li>Comparison of storage based solutions with classical solutions </li></ul><ul><li>Clear presentation of results </li></ul>
    13. 13. Main modules of PLATOS Alternative solutions module Optimisation module System analysis module
    14. 14. System analysis module <ul><li>Needed for standard system analyses </li></ul><ul><ul><li>Load flow calculations </li></ul></ul><ul><ul><li>Short circuit calculations </li></ul></ul>Alternative solutions module Optimisation module System analysis module
    15. 15. Optimisation module <ul><li>Needed for automatic generation of solutions </li></ul><ul><li>Needed for automatic assessment of solution performance </li></ul>Alternative solutions module Optimisation module System analysis module
    16. 16. Alternative solutions module <ul><li>Needed for comparison of storage based solutions with classical solutions to network problems </li></ul>Alternative solutions module Optimisation module System analysis module
    17. 17. Realisation of the tool <ul><li>All PLATOS modules are realised within the Digsilent PowerFactory simulation package (a well known and widely used tool for power system analysis) </li></ul><ul><li>Advantages </li></ul><ul><ul><li>All standard power system analysis tools are readily available </li></ul></ul><ul><ul><li>Graphical user interface already present </li></ul></ul><ul><ul><li>Easy access to library of power system components </li></ul></ul><ul><ul><li>Powerful macro programming language </li></ul></ul><ul><ul><li>Easy access to all relevant parameters </li></ul></ul>
    18. 18. OPTIMISATION MODULE
    19. 19. Optimisation <ul><li>Optimizing the location, type and size of mobile storage systems is a combinatorial problem with many possible solutions </li></ul><ul><li>Key question is how to find global optimum in an efficient way </li></ul>
    20. 20. Number of possible solutions <ul><li>Example </li></ul><ul><ul><li>Power system with 100 nodes </li></ul></ul><ul><ul><li>2 storage units to be installed </li></ul></ul>
    21. 21. Genetic algorithms <ul><li>The combinatorial problem is solved by application of an artificial evolution, in particular using a genetic algorithm </li></ul><ul><li>Basics of genetic algorithm </li></ul><ul><ul><li>Step 1: Create random solutions </li></ul></ul><ul><ul><li>Step 2: Analyze all solutions </li></ul></ul><ul><ul><li>Step 3: Select the best solutions </li></ul></ul><ul><ul><li>Step 4: Create new solutions based on the best ones </li></ul></ul><ul><ul><li>Step 5: Go to step 2 </li></ul></ul>
    22. 22. Artificial evolution Old solutions Best solutions New solutions Select Inherit Mutate Repeat
    23. 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. 24. Basic design of optimization module Solution generator Alleviation algorithm 1 Alleviation algorithm 2 Trading algorithm Alleviation algorithm 3 Solution assessment Output data processing Performance indicator Performance indicator Performance indicator Performance indicator Overall performance indicator Input data processing
    25. 25. The optimization process <ul><li>The optimisation process can be influenced by many factors: </li></ul><ul><ul><li>Number of generations </li></ul></ul><ul><ul><li>Number of genes </li></ul></ul><ul><ul><li>Mutation parameters </li></ul></ul><ul><ul><ul><li>Storage location mutation parameter </li></ul></ul></ul><ul><ul><ul><li>Storage type mutation parameter </li></ul></ul></ul><ul><ul><ul><li>Storage size mutation parameter </li></ul></ul></ul><ul><li>By choosing certain parameters the user defines his optimization strategy </li></ul>
    26. 29. Power system modelling <ul><li>Network topology </li></ul><ul><li>Component types (impedances, typical costs) </li></ul><ul><li>Load types </li></ul><ul><li>Load and generation patterns </li></ul><ul><li>Number, location and size of fixed storage systems </li></ul>
    27. 30. Required input data <ul><li>Network data </li></ul><ul><ul><li>Load patterns </li></ul></ul><ul><ul><li>Component data </li></ul></ul><ul><ul><li>Network topology </li></ul></ul><ul><ul><li>Generation patterns </li></ul></ul><ul><li>Solution space for optimization routine </li></ul><ul><ul><li>Number, location and size of fixed storage units </li></ul></ul><ul><ul><li>Power system components to be monitored </li></ul></ul><ul><ul><li>Data required for assessment of solutions </li></ul></ul><ul><ul><li>Number of storage systems </li></ul></ul><ul><ul><li>Type of storage systems </li></ul></ul><ul><ul><li>Size of storage systems </li></ul></ul>
    28. 31. Variables related to available storage systems <ul><li>Available types of storage systems </li></ul><ul><ul><li>Maximum discharging power </li></ul></ul><ul><ul><li>Minimum charging power </li></ul></ul><ul><li>Available sizes of storage systems </li></ul><ul><li>Typical costs </li></ul><ul><ul><li>EURO/kW </li></ul></ul><ul><ul><li>EURO/kWh </li></ul></ul><ul><li>Maximum number of discharging cycles </li></ul>XS M XS + - L XL + -
    29. 32. Other variables <ul><li>Variables related to genetic algorithm </li></ul><ul><ul><li>Including user defined solutions </li></ul></ul><ul><li>Variables related to optimisation process </li></ul><ul><li>Variables related to calculation of performance </li></ul><ul><ul><li>Appraisals </li></ul></ul><ul><ul><li>Penalties </li></ul></ul>
    30. 33. Algorithms within PLATOS <ul><li>Alternative solutions algorithms </li></ul><ul><li>Genetic algorithm </li></ul><ul><li>Overload alleviation algorithm </li></ul><ul><li>Voltage alleviation algorithm </li></ul><ul><li>Advanced trading algorithms </li></ul><ul><li>Dip alleviation algorithm </li></ul><ul><li>Storage management algorithm </li></ul>
    31. 34. Alternative solutions <ul><li>Automatic assessment of alternative (e.g. classical) solutions to the network problems </li></ul><ul><ul><li>Other tap changer settings </li></ul></ul><ul><ul><li>Replacement of power connections </li></ul></ul><ul><ul><li>Additional power connections </li></ul></ul><ul><li>Result of assessment is used as starting point for assessment of storage based solutions </li></ul>
    32. 35. Features of genetic algorithm <ul><li>Automatic generation of storage solutions </li></ul><ul><li>Generation of solutions is influenced by calculated performance of previous solutions </li></ul><ul><li>Automatic rejection of bad solutions e.g. solutions that have </li></ul><ul><ul><li>too high investment costs </li></ul></ul><ul><ul><li>too small storage capacity </li></ul></ul><ul><ul><li>too small charging and discharging power </li></ul></ul><ul><ul><li>too large charging and discharging power </li></ul></ul><ul><li>Possibility for providing educated guesses too speed up the optimisation process </li></ul>+ =
    33. 36. Features of overload alleviation algorithm <ul><li>Automatic determination of overload locations and overload severity </li></ul><ul><li>Automatic determination of required storage capacity to solve the overloading problems </li></ul><ul><li>Automatic determination of power setpoints for storage inverters taking into account operating constraints (minimum SOC, maximum SOC) </li></ul><ul><li>Automatic determination of performance indicator taking into account </li></ul><ul><ul><li>Effect of storage on overload </li></ul></ul><ul><ul><li>Investment costs </li></ul></ul><ul><ul><li>Energy losses </li></ul></ul><ul><ul><li>Used storage cycles </li></ul></ul><ul><li>Graphical representation of results </li></ul>
    34. 37. Features of voltage alleviation algorithm <ul><li>Automatic determination of locations with under and/or overvoltage conditions </li></ul><ul><li>Automatic determination of required storage capacity to solve the voltage problems </li></ul><ul><li>Automatic determination of power setpoints for storage inverters taking into account operating constraints </li></ul><ul><li>Automatic determination of performance indicator taking into account </li></ul><ul><ul><li>Effect of storage on voltage </li></ul></ul><ul><ul><li>Investment costs </li></ul></ul><ul><ul><li>Energy losses </li></ul></ul><ul><ul><li>Used storage cycles </li></ul></ul><ul><li>Graphical representation of results </li></ul>
    35. 38. Features of voltage dip alleviation algorithm <ul><li>Automatic determination of required storage capacity to alleviate voltage dips at predefined locations </li></ul><ul><li>Automatic determination of power setpoints for storage inverters taking into account operating constraints </li></ul><ul><li>Automatic determination of performance indicator </li></ul><ul><li>Dedicated inputs </li></ul><ul><ul><li>Voltage dip tables </li></ul></ul><ul><ul><li>Cost table </li></ul></ul>
    36. 39. PERFORMANCE INDICATORS
    37. 40. Performance indicators (1/2) <ul><li>Performance indicators indicate the performance of each individual solution </li></ul><ul><li>Performance indicators are expressed in terms of EUR </li></ul><ul><li>Performance indicators take into account the costs and benefits of a particular solution </li></ul><ul><li>Number and type of performance indicators to be used are determined by the user </li></ul>Performance indicator = Benefits - Costs
    38. 41. Performance indicators (2/2)
    39. 42. PLATOS output <ul><li>Output will include: </li></ul><ul><ul><li>Optimal locations of storage systems </li></ul></ul><ul><ul><li>Optimal number and type of storage systems </li></ul></ul><ul><ul><li>Required specifications for storage system </li></ul></ul><ul><ul><li>Optimal set points for storage systems </li></ul></ul><ul><ul><li>Performance indicator of each algorithm and each individual solution </li></ul></ul><ul><li>Results are available in Excel and graphically </li></ul>
    40. 43. Graphical output of PLATOS (1/3) Solutions without performance indicator Evaluated solution
    41. 46. Main features of PLATOS <ul><li>Optimization of storage application in power systems </li></ul><ul><ul><li>Optimization of location, size and type </li></ul></ul><ul><ul><li>Optimization criteria can be changed by the user </li></ul></ul><ul><ul><li>Monitoring of optimization process </li></ul></ul><ul><li>Performance indicators can be defined by the user </li></ul><ul><ul><li>Definition of points of interest within power system </li></ul></ul><ul><ul><li>Both technical and economical performance indicators </li></ul></ul><ul><li>Graphical and tabular output </li></ul><ul><li>Comparison with classical non storage based solutions </li></ul><ul><li>User definable load and generation patterns </li></ul><ul><li>Tool can be used for each voltage level </li></ul>
    42. 47. POSSIBLE APPLICATIONS
    43. 48. Possible applications of PLATOS <ul><li>Use as planning tool </li></ul><ul><ul><li>Development of storage application alternatives that fulfill predifined objectives of the user without exceeding technical or economical constraints. </li></ul></ul><ul><ul><ul><li>Planning of new storage systems in existing power systems </li></ul></ul></ul><ul><ul><ul><li>Relocation of existing storage systems in existing power systems </li></ul></ul></ul><ul><ul><ul><li>Planning of charging facilities for electric vehicles </li></ul></ul></ul>
    44. 49. Possible applications of PLATOS (2) <ul><li>Use as analysis tool </li></ul><ul><ul><li>Assessement of benefits of specific storage systems with regard to voltage improvement, load alleviation, dip alleviation etc. </li></ul></ul><ul><ul><li>Analysis of different charging and discharging regimes </li></ul></ul><ul><ul><li>Determination of required storage size and power </li></ul></ul>
    45. 50. Possible applications of PLATOS (3) <ul><li>Use as buying tool </li></ul><ul><ul><li>Potential buyers of equipment (e.g. storage systems, inverters etc.) can use the tool to compare bids of different suppliers </li></ul></ul>
    46. 51. Possible applications of PLATOS (4) <ul><li>Use as selling tool </li></ul><ul><ul><li>Manufacturers of equipment (e.g. storage systems, inverters) can use the tool to convince potential customers of the advantages of using their equipment </li></ul></ul>
    47. 52. Typical network problems <ul><li>Typical problems in a power system </li></ul><ul><ul><li>Undervoltage at the end of the feeder </li></ul></ul><ul><ul><li>Overload at the beginning of the feeder </li></ul></ul><ul><ul><li>Voltage dip (caused by e.g. short circuit in adjacent feeder) </li></ul></ul><ul><li>Questions: </li></ul><ul><ul><li>Can storage solve the problem? </li></ul></ul><ul><ul><li>What is the optimal location for storage? </li></ul></ul><ul><ul><li>What is the optimal size of the storage? </li></ul></ul><ul><ul><li>What is the optimal power of the storage? </li></ul></ul><ul><ul><li>Remaining issues </li></ul></ul>
    48. 53. What is the optimal location, size, type? <ul><li>Answer depends on many different factors </li></ul><ul><li>PLATOS considers: </li></ul><ul><ul><li>Desired voltage profile at specific locations </li></ul></ul><ul><ul><li>Storage system should not introduce other network problems </li></ul></ul><ul><ul><li>Energy losses </li></ul></ul><ul><ul><li>Economics </li></ul></ul><ul><ul><li>Required minimum discharging power to alleviate network problems </li></ul></ul><ul><ul><li>Duration of the network problems </li></ul></ul><ul><ul><li>Required minimum absolute charging power to charge when possible </li></ul></ul><ul><ul><li>Ampacity of power system components </li></ul></ul><ul><ul><li>Storage system should be able to supply a known power during a known period </li></ul></ul><ul><ul><li>Storage system should store a known amount of energy within a known period </li></ul></ul><ul><ul><li>A storage system with a size larger than required is not useful for solving the network problems </li></ul></ul><ul><ul><li>Power system should be able to accommodate for a certain storage size </li></ul></ul>
    49. 54. Other application of PLATOS <ul><li>Second Life project </li></ul><ul><ul><li>Main question: is it possible to use depriciated car batteries for storage purposes in distribution systems? </li></ul></ul><ul><li>Topics </li></ul><ul><ul><li>Capacity of depriciated car batteries </li></ul></ul><ul><ul><li>Remaining life time </li></ul></ul><ul><ul><li>Relationship between number of discharging cycles and life time </li></ul></ul><ul><ul><li>Benefits of using old car batteries </li></ul></ul><ul><li>PLATOS </li></ul><ul><ul><li>Gives insight in the problem </li></ul></ul><ul><ul><li>Can help finding the answers to the questions posed </li></ul></ul>
    50. 55. Conclusions <ul><li>Storage systems can be applied for many different purposes </li></ul><ul><li>The optimal location, type and size of storage system to be used depends on many factors </li></ul><ul><li>The more functions the storage system needs to fullfill, the more complex the decisions with regard to using storage systems become </li></ul><ul><li>PLATOS can support the decision making process by </li></ul><ul><ul><li>Providing better insight in the problems </li></ul></ul><ul><ul><li>Providing solutions that can be compared in a tranparent way </li></ul></ul>
    51. 56. DEMONSTRATION
    52. 57. Running cases in PLATOS <ul><li>Steps </li></ul><ul><ul><li>Step 1: Make model of power system </li></ul></ul><ul><ul><li>Step 2: Connect loads and generators </li></ul></ul><ul><ul><li>Step 3: Set load and generation patterns </li></ul></ul><ul><ul><li>Step 4: Create input textfile </li></ul></ul><ul><ul><li>Step 5: Run PLATOS </li></ul></ul><ul><ul><li>Step 6: Evaluate results </li></ul></ul>
    53. 59. Network problems
    54. 60. Nodes and connections to be monitored
    55. 61. Solution 1
    56. 62. Solution 3
    57. 63. Solution 6
    58. 64. Solution 23
    59. 65. Solution 59
    60. 66. Solution 62
    61. 67. Solution 80
    62. 68. Performance
    63. 69. Conclusions <ul><li>Storage systems can be applied for many different purposes </li></ul><ul><li>The optimal location, type and size of storage system to be used depends on many factors </li></ul><ul><li>The more functions the storage system needs to fullfill, the more complex the decisions with regard to using storage systems become </li></ul><ul><li>PLATOS can support the decision making process by </li></ul><ul><ul><li>Providing better insight in the problems </li></ul></ul><ul><ul><li>Providing solutions that can be compared in a tranparent way </li></ul></ul>
    64. 70. Workshop <ul><ul><li>Wednesday 11th May 2011 in Mannheim </li></ul></ul><ul><ul><li>Including a site visit to the storage systems </li></ul></ul><ul><ul><li>For more information contact Petra de Boer </li></ul></ul><ul><ul><li>[email_address] </li></ul></ul><ul><ul><li>+31 26 356 2552 </li></ul></ul>
    65. 71. 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 6150 [email_address] [email_address] [email_address]
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