1. - 1 - 18 th Annual Conference June 1 st - 3 rd Hamburg The new importance of Demand Side Integration in the German Power System Dipl.-Ing. Hans Schäfers, Head of Research C4DSI

2. - 2 - HAW, CC4E & C4DSI Why research DSI ? Two Projects at C4DSI – E-Harbours – Smart Power Hamburg

3. - 3 - HAW, CC4E & C4DSI

4. HAW Hamburg: University of Applied Sciences Technologie and Informatics Life Sciences Econmics & Social Sciences Design, Media, Information 2nd largest university in Hamburg 4 Departments, 19 Faculties, 63 degree programs 13,600 students, 370 Profs. + 400 Assistant Profs. HAW Hamburg

5. CC4E – Erneuerbare Energien und Energieeffizienz der HAW Technologie and Informatics Life Sciences Econmics & Social Sciences Design, Media, Information  Pooling of research activities in renewable energy and energy efficiency at the „Competence Center Erneuerbare Energien & Energieeffizienz“ HAW Hamburg  Installation of a field of expertise in Northern Germany  Activity Areas: Teaching, Research, Transfer Partnerships, Networking  Cooperation with Universities, Copanies and other Institutions

6. CC4E – Erneuerbare Energien und Energieeffizienz der HAW Technologie and Informatics Life Sciences Economics & Social Sciences Design, Media, Information  Research activities in Demand Side Integration at CC4E in the „Center for Demand Side Integration“ HAW Hamburg  Interdisciplinary research team with a strong focus on DSI in cities  Current Public Projects: E-Harbours, Smart Power Hamburg  Private R&D Projects  New Partners welcome

7. - 7 - Why research DSI ?

8. - 8 - The political aims concerning the energy turnaround: Europe aims at realizing an ambitious 20-20-20 agenda 20% less energy consumption < 20% 20% less CO 2 20% demand coverage by renewable energies Some European countries go further than that: Germany aims at an share of 35% electricity from renewables by 2020, 50% by 2030 and 80% by 2050.

9. - 9 - The generation/consumption balance and the electricity grid setpoint Setpoint 50 Hz Load Generation Use of reserve power Deviation from load prognosis Large load noise Drop out of larger loads Deviation from generation prognosis (esp. wind) Drop out of generation units Reasons for larger deviations:

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12. - 12 - Problem: A rising share of renewables leads to higher fluctuations in power generation. Base load as a concept vanishes. Simulation of a share of 47% REG (Weather data of 2007). Source: Fraunhofer IWES, 2010 Energie-Campus HAW: Forschungs-/Innovationsprojekte Demand Side Integration

13. - 13 - Power in GW Resulting residual load (RE generation minus load) expected for 2020 in Germany

14. - 14 - Power in GW Resulting residual load (RE generation minus load) expected for 2020 in Germany Plus generation from existing (!) base load PP

15. - 15 - Energie-Campus HAW: Forschungs-/Innovationsprojekte Demand Side Integration Influence of DR and PS (Potential of 2006) at 47% REG (Weather Data 2007). Quelle: Fraunhofer IWES Indespensable part of the solution: Smoothing fluctuations via Integration of the Demand Side (Demand Response) and storage of surplus generation (e. g. Power to Gas)

16. - 16 - Conclusion For a renewable energy system we do not only need the renewable generation capacity but also a very flexible (new) energy system which contains flexible generation sites (no base load generation needed) flexible electric loads facilities for surplus energy storage The C4DSI focuses its research on identifying and integrating flexible loads and storage facilities on the electrical and thermal demand side.

17. - 17 - Two Public R&D Projects at C4DSI 1. E-Harbours 2. Smart Power Hamburg

18. - 18 - Two Public R&D Projects at C4DSI 1. E-Harbours

19. E- harbours: Supported by: EU Interreg North Sea Regions Programme Duration: 01/09/2010 - 31/08/2013 Total Eligible Budget: € 4,820,120; ERDF Grant: € 2,410,060. Lead Beneficiary: Municipality of Zaanstad Partners:Municipality of Amsterdam, NL Port of Antwerp, BE City of Malmö, SE Hamburg University of Applied Sciences, GE Pure Energy Centre, UK Robert Gordon University, UK Uddevalla Energy, SE VITO, BE

20. The objectives of e-harbours: The challenge is to create a more sustainable energy model in harbour regions on the basis of innovative intelligent energy networks (smart grids). e-harbours focuses 3 objectives in 7 show cases: 1)Increase the use of renewable energies and flexible loads in harbours regions 2)Increase the use of smart energy grids to atune energy demand and supply 3)Increase the use of electric tranport in harbours

21. Show Case 1 and 2 Hamburg and Antwerp Aim: Find flexible loads in harbour companies Connect them to virtual power plants Apply/develop necessary business models

22. Land use and identified/analysed companies in the port of Hamburg Survey in Hamburg

23. So far: Closer Examination of 3 cold storage facilities K1: 600 kW (440 kW) cooling power K3: 132kW (100 kW) cooling power K4: 200 kW (180 kW) cooling power Examination of financial potential regarding –structured purchase –untypical grid usage (low during peak, high during off- peak) –selling reserve capacity –combination of the above

25. - 25 - Two Public R&D Projects at C4DSI 2. Smart Power Hamburg

26. Gefördert durch: aufgrund eines Beschlusses des deutschen Bundestages G EFÖRDERT DURCH : F ÖRDERPROGRAMM : Lead Partner: HAMBURG ENERGIE Scientific Research by two universities: RWTH Aachen and HAW Hamburg Funded by BMWi as part of the funding initiative EnEff:Wärme Running time until end of 2014 SMART POWER HAMBURG Smart Power Hamburg – A joint research project SMART POWER HAMBURG KonsortialführerWissenschaftliche Begleitung

27. Gefördert durch: aufgrund eines Beschlusses des deutschen Bundestages M ANAGEMENT S YSTEM AS OPEN P LATTFROM C ONCEPT OPERATED H AMBURG E NERGY DEMAND S TORAGE G ENERATION Combined Heat and Power Production Smart Metering Flexibility of CHP via heat stroage IN URBAN INFRASTRUCTURE - Bunker - Swimming Pools - Heat distr. grids Flexible Power Generation Demand Response HVAC in public properties Energy Efficiency of properties in the Pool Energy services for CHP and property owners Services to the energy system (DSO &TSO) SMART POWER HAMBURG

28. Dipl. Ing. (FH) Hans Schäfers Schaefers@sumbi.de Simulation of a balancing group of 120 public properties at MVL in a network of 120 Smart Meters and 20 Standard Load Management Devices (Matlab/Simulink). Pre-Runner to SPH: E-Island

29. 3 Bisherige Ergebnisse Mittwoch 2. Juli 2008 Szenario 1: Fahrplaneinhaltung

30. Modellbildung und Simulation des 3 Bisherige Ergebnisse Mittwoch 2. Juli 2008 Szenario 2: Pos MRL -> Regler Min

31. Modellbildung und Simulation des 3 Bisherige Ergebnisse Mittwoch 2. Juli 2008 Szenario 2: Neg. MRL -> Regler Max

32. - 32 - Thank you for your attention.