- 1 -

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

- 2 -

• HAW, CC4E & C4DSI

• Why research DSI ?

• Two Projects at C4DSI– E-Harbours

– Smart Power Hamburg

- 3 -

• 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

- 7 -

• Why research DSI ?

- 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 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.

- 9 -

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:

- 10 -

- 11 -

- 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 -

Po

we

r in

GW

Resulting residual load (RE generation minus load) expected for 2020 in Germany

- 14 -

Po

we

r in

GW

Resulting residual load (RE generation minus load) expected for 2020 in Germany

Plus generation from existing (!) base load

PP

- 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 -

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.

- 17 -

• Two Public R&D Projects at C4DSI1. E-Harbours

2. Smart Power Hamburg

- 18 -

• 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

- 25 -

• 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

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

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

- 32 -

Thank you for your attention.