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18th Annual Conference June 1st- 3rd
Hamburg
The new importance of Demand Side Integration in the German Power System
Dipl.-Ing. Hans Schäfers, Head of Research C4DSI
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• HAW, CC4E & C4DSI
• Why research DSI ?
• Two Projects at C4DSI– E-Harbours
– Smart Power Hamburg
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• HAW, CC4E & C4DSI
HAW Hamburg: University of Applied Sciences
Technologie and
Informatics
LifeSciences
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
CC4E – Erneuerbare Energien und Energieeffizienz der HAW
Technologie and
Informatics
LifeSciences
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
CC4E – Erneuerbare Energien und Energieeffizienz der HAW
Technologie and
Informatics
LifeSciences
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
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• Why research DSI ?
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The political aims concerning the energy turnaround:
Europe aims at realizing an ambitious 20-20-20 agenda
• 20% less energy consumption < 20%• 20% less CO2
• 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.
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The generation/consumption balance and the electricity grid setpoint
Setpoint50 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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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
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Po
we
r in
GW
Resulting residual load (RE generation minus load) expected for 2020 in Germany
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Po
we
r in
GW
Resulting residual load (RE generation minus load) expected for 2020 in Germany
Plus generation from existing (!) base load
PP
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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)
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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 onidentifying and integrating flexible loads and storage facilities
on the electrical and thermal demand side.
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• Two Public R&D Projects at C4DSI1. E-Harbours
2. Smart Power Hamburg
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• Two Public R&D Projects at C4DSI1. E-Harbours
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, SEHamburg University of Applied Sciences, GEPure Energy Centre, UKRobert Gordon University, UKUddevalla Energy, SEVITO, BE
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
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
Land use and identified/analysed companies in the port of Hamburg
Survey in Hamburg
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
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• Two Public R&D Projects at C4DSI
2. Smart Power Hamburg
Gefördert durch:
aufgrund eines Beschlusses des deutschen Bundestages
GEFÖRDERT DURCH:
FÖRDERPROGRAMM:
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
Konsortialführer Wissenschaftliche Begleitung
Gefördert durch:
aufgrund eines Beschlusses des deutschen Bundestages
MANAGEMENT SYSTEM AS OPEN PLATTFROM CONCEPT
OPERATED HAMBURG ENERGY
DEMAND
STORAGEGENERATION
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
Dipl. Ing. (FH) Hans Schäfers
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
3 Bisherige Ergebnisse Mittwoch 2. Juli 2008
Szenario 1: Fahrplaneinhaltung
Modellbildung und Simulation des
3 Bisherige Ergebnisse Mittwoch 2. Juli 2008
Szenario 2: Pos MRL -> Regler Min
Modellbildung und Simulation des
3 Bisherige Ergebnisse Mittwoch 2. Juli 2008
Szenario 2: Neg. MRL -> Regler Max
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Thank you for your attention.