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GROWDERS & PLATOS, session 1 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
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
Agenda ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Introduction of GROWDERS ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Grid   Reliability  and  Operability  with  Distributed   Generation  using  Transportable   Storage
GROW-DERS ,[object Object],[object Object],[object Object]
Electricity Networks of the Future ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Electricity storage systems will enable the Smart Grid by solving many of the technical challenges
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
Where is Storage Targeting? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Summary of Technologies covered
Main activities in GROWDERS Design Optimization Tool Design Storage System  Build Prototypes Field tests x 3 Combined  Pilot Develop Optimization Tool Validation & Optimization Nov 2010 2007 June 2011
Field tests of GROWDERS ,[object Object],[object Object],[object Object],[object Object]
Test site Bronsbergen (Zutphen)
Test site Bronsbergen (Zutphen)
Holiday parc Bronsbergen in Zutphen ,[object Object],[object Object]
Flywheel system
Results Zutphen ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Battery test site Zamudio and Chambery
Transportable storage systems
Results Chambery and Zamudio ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Final field test Mannheim ,[object Object],[object Object],[object Object]
Lessons learned GROWDERS ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
The need of storage in the grid ,[object Object],[object Object],[object Object],[object Object],[object Object],Demand and DG Network Storage
Questions ,[object Object],[object Object],[object Object],[object Object],[object Object],? ? ? Demand and DG Network Storage
Planning tool for optimizing storage  (PLATOS) ,[object Object]
Complexity of Decision Making ,[object Object],[object Object],[object Object],[object Object]
Setting expectations ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Distribution system planning in general Scenario 1 Scenario 2 Scenario n Decision  process  (Sub)Scenarios Optimal solutions Optimal distribution network Bottlenecks
Changing balance of objectives (Risk  management ) Avoid Control Intuition Well funded (Net present value,  Life cycle costing) Certainty Customer oriented (Cost-benefit analysis) COST PERFORMANCE RISK
Uncertainties in distribution system planning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
RESIDENTIAL DEMAND ELECTRICAL APPLIANCES ENERGY EFFICIENCY CUSTOMER BEHAVIOURS METERS & DISPLAYS SUPPLIER TRANSACTIONS DISTRIBUTION NETWORK TRANSMISSION NETWORK MICRO- GENERATION DISTRIBUTED GENERATION CENTRALISED GENERATION INTER CONNECTIONS Elements influencing demand ELECTRIC VEHICLES CENTRALISED STORAGE smart metering smart grids
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
What are the aspects of a good decision?  ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],New development Generate alternative solutions Technical evaluation per 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
Important to distinguish  I ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],New development Generate alternative solutions Technical evaluation per 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
Important to distinguish  II ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],New development Generate alternative solutions Technical evaluation per 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
Important to distinguish  III ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Projects   Strategy Planning  Operations
Problem definition ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Set borders Time Complexity New development Generate alternative solutions Technical evaluation per 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
Problem definition in GROWDERS (1/2) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Problem definition in GROWDERS (2/2) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Generating alternatives (general) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],“ DO NOTHING” is also an Alternative to be considered!
Storage alternatives within GROWDERS ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Added complexity through storage  (1/2) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Added complexity through storage (2/2) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Other alternatives within GROWDERS “conventional” solutions ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
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
Typical constraints DSO ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Constraints within GROWDERS ,[object Object],[object Object],[object Object],Prediction error Demand ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Different objectives  ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],    n t t t i CF NPV 0 ) 1 ( CF = Cashflow i = interest t = time ( year ) n =  period     n t t t i CF NPV 0 ) 1 ( CF = Cashflow i = interest t = time ( year ) n =  period
Objectives within GROWDERS ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Storage Solution Generator Alleviation Algorithm:  Overloading Alleviation Algorithm: Over/ undervoltage Alleviation Algorithm: Voltage Dips Trading Algorithm Solution Assessment Output data processing Performance Indicator 1 Performance Indicator 2 Performance Indicator 3 Performance Indicator 4 + + + Non-storage Solution Generator Performance Indicator
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
Additional options for decision making ,[object Object],[object Object],[object Object],[object Object]
Summary ,[object Object],[object Object],[object Object]
Conclusions Decision making about storage ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
What’s next ?
Next session ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Example of PLATOS
Thank you for your attention www.growders.eu Petra de Boer  Roger Cremers  Gabriël Bloemhof +31 26 356 2552  + 31 26 356 3240  + 31 26 356 6150 [email_address]   [email_address]   [email_address]

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Grid Connected Electricity Storage Systems (1/2)

  • 1. GROWDERS & PLATOS, session 1 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. 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.
  • 4.
  • 5.
  • 6.
  • 7. 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
  • 8.
  • 9.
  • 10. Main activities in GROWDERS Design Optimization Tool Design Storage System Build Prototypes Field tests x 3 Combined Pilot Develop Optimization Tool Validation & Optimization Nov 2010 2007 June 2011
  • 11.
  • 14.
  • 16.
  • 17. Battery test site Zamudio and Chambery
  • 19.
  • 20.
  • 21.
  • 22.
  • 23.
  • 24.
  • 25.
  • 26.
  • 27. Distribution system planning in general Scenario 1 Scenario 2 Scenario n Decision process (Sub)Scenarios Optimal solutions Optimal distribution network Bottlenecks
  • 28. Changing balance of objectives (Risk management ) Avoid Control Intuition Well funded (Net present value, Life cycle costing) Certainty Customer oriented (Cost-benefit analysis) COST PERFORMANCE RISK
  • 29.
  • 30. RESIDENTIAL DEMAND ELECTRICAL APPLIANCES ENERGY EFFICIENCY CUSTOMER BEHAVIOURS METERS & DISPLAYS SUPPLIER TRANSACTIONS DISTRIBUTION NETWORK TRANSMISSION NETWORK MICRO- GENERATION DISTRIBUTED GENERATION CENTRALISED GENERATION INTER CONNECTIONS Elements influencing demand ELECTRIC VEHICLES CENTRALISED STORAGE smart metering smart grids
  • 31. 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
  • 32.
  • 33.
  • 34.
  • 35.
  • 36.
  • 37.
  • 38.
  • 39.
  • 40.
  • 41.
  • 42.
  • 43.
  • 44. 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
  • 45.
  • 46.
  • 47.
  • 48.
  • 49. 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
  • 50.
  • 51.
  • 52.
  • 54.
  • 56. Thank you for your attention www.growders.eu Petra de Boer Roger Cremers Gabriël Bloemhof +31 26 356 2552 + 31 26 356 3240 + 31 26 356 6150 [email_address] [email_address] [email_address]

Editor's Notes

  1. 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
  2. Storage appears to have an application in all areas of the grid – current & future.
  3. 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