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.

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. 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

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. 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. INTRODUCTION OF PLATOS

13. Main modules of PLATOS Alternative solutions module Optimisation module System analysis module

18. OPTIMISATION MODULE

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. 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

39. PERFORMANCE INDICATORS

41. Performance indicators (2/2)

43. Graphical output of PLATOS (1/3) Solutions without performance indicator Evaluated solution

47. POSSIBLE APPLICATIONS

56. DEMONSTRATION

59. Network problems

60. Nodes and connections to be monitored

61. Solution 1

62. Solution 3

63. Solution 6

64. Solution 23

65. Solution 59

66. Solution 62

67. Solution 80

68. Performance

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]