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18 th Annual Conference June 1 st - 3 rd Hamburg. The new importance of Demand Side I ntegration in the German Power System Dipl.-Ing. Hans Schäfers, Head of Research C4DSI. HAW, CC4E & C4DSI Why research DSI ? Two Projects at C4DSI E-Harbours Smart Power Hamburg.
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18th Annual Conference June 1st- 3rdHamburg The newimportanceof Demand Side Integration in the German Power System Dipl.-Ing. Hans Schäfers, Head of Research C4DSI
HAW, CC4E & C4DSI • Whyresearch DSI ? • Two Projects at C4DSI • E-Harbours • Smart Power Hamburg
HAW Hamburg HAW Hamburg: University of Applied Sciences Design, Media, Information Econmics & Social Sciences Technologie and Informatics Life Sciences 2nd largestuniversity in Hamburg 4 Departments, 19 Faculties, 63 degreeprograms 13,600 students, 370 Profs. + 400 Assistant Profs.
HAW Hamburg CC4E – Erneuerbare Energien und Energieeffizienz der HAW Design, Media, Information Econmics & Social Sciences Technologie and Informatics Life Sciences • Pooling ofresearchactivities in renewableenergyandenergyefficiencyatthe „Competence Center Erneuerbare Energien & Energieeffizienz“ • Installation of a fieldofexpertise in Northern Germany • ActivityAreas: Teaching, Research, Transfer Partnerships , Networking • CooperationwithUniversities, CopaniesandotherInstitutions
HAW Hamburg CC4E – Erneuerbare Energien und Energieeffizienz der HAW Design, Media, Information Economics & Social Sciences Technologie and Informatics Life Sciences • Research activities in Demand Side Integration at CC4E in the „Center for Demand Side Integration“ • Interdisciplinaryresearchteamwith a strong focus on DSI in cities • Current Public Projects: E-Harbours, Smart Power Hamburg • Private R&D Projects • New Partners welcome
The politicalaimsconcerningtheenergyturnaround: • Europe aimsatrealizing an ambitious20-20-20 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.
The generation/consumption balance and the electricity grid setpoint Setpoint 50 Hz Useofreserve power Generation Load Reasonsfor larger deviations: Deviation from generation prognosis (esp. wind) Drop out of generation units Deviation from load prognosis Large load noise Drop out of larger loads
Energie-Campus HAW: Forschungs-/Innovationsprojekte Demand Side Integration Problem: A risingshareofrenewablesleadstohigherfluctuations in power generation . Base loadas a conceptvanishes. Simulation of a share of 47% REG (Weather data of 2007). Source: FraunhoferIWES, 2010
Resulting residual load (RE generation minus load) expected for 2020 in Germany Power in GW
Resulting residual load (RE generation minus load) expected for 2020 in Germany Plus generationfromexisting (!) baseload PP Power in GW
Energie-Campus HAW: Forschungs-/Innovationsprojekte Demand Side Integration Indespensablepartofthesolution: Smoothingfluctuations via Integration ofthe Demand Side (Demand Response) andstorageofsurplusgeneration(e. g. Power to Gas) Influence of DR and PS (Potential of 2006) at 47% REG (Weather Data 2007). Quelle: Fraunhofer IWES
Conclusion • For a renewableenergysystemwe do not onlyneedtherenewablegenerationcapacity but also a very flexible (new) energysystemwhichcontains • flexible generationsites (nobaseloadgenerationneeded) • flexible electricloads • facilitiesforsurplusenergystorage • The C4DSI focusesitsresearch on • identifyingandintegrating flexible loadsandstoragefacilities on theelectricaland thermal demandside.
Two Public R&D Projects at C4DSI 1. E-Harbours 2. Smart Power Hamburg
TwoPublic R&D Projects at C4DSI 1. E-Harbours
E- harbours: • Supported by: EU InterregNorth SeaRegionsProgramme • Duration: 01/09/2010 - 31/08/2013 • Total Eligible Budget: € 4,820,120; ERDF Grant: € 2,410,060. • Lead Beneficiary: Municipality of Zaanstad • Partners: Municipalityof Amsterdam, NL • Port ofAntwerp, BE • City of Malmö, SE • Hamburg University of Applied Sciences, GE • Pure EnergyCentre, UK • Robert Gordon University, UK • UddevallaEnergy, SE • VITO, BE
The objectivesof e-harbours: • The challengeistocreatea moresustainableenergymodel in harbourregions on thebasisofinnovative intelligent energynetworks (smart grids). • e-harboursfocuses 3 objectives in 7 showcases: • Increasetheuseofrenewableenergiesand flexible loads in harboursregions • Increasetheuseof smart energygridstoatuneenergydemandandsupply • Increasetheuseofelectrictranport 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
Survey in Hamburg Land useandidentified/analysedcompanies in theportof Hamburg
So far:CloserExaminationof 3 coldstoragefacilities • K1: 600 kW (440 kW) cooling power • K3: 132kW (100 kW) cooling power • K4: 200 kW (180 kW) cooling power • Examinationoffinancial potential regarding • structuredpurchase • untypical grid usage (lowduringpeak, high during off-peak) • sellingreservecapacity • combinationoftheabove
Two Public R&D Projects at C4DSI 2. Smart Power Hamburg
SMART POWER HAMBURG Smart Power Hamburg – A jointresearchproject Gefördert durch: • Lead Partner: HAMBURG ENERGIE • Scientific Research bytwouniversities:RWTH Aachen and HAW Hamburg • Fundedby BMWi aspartofthefunding initiative EnEff:Wärme • Running time until end of 2014 Förderprogramm: SMART POWER HAMBURG Konsortialführer Wissenschaftliche Begleitung
Storage SMART POWER HAMBURG DEMAND Flexibilityof CHP viaheatstroage IN URBAN INFRASTRUCTURE - Bunker - Swimming Pools - Heatdistr. grids CombinedHeatand Power Production Smart Metering Generation Demand Response HVAC in publicproperties Flexible Power Generation Management System as open PlattfromConceptoperated Hamburg Energy Energy Efficiency ofproperties in the Pool Energyservicesfor CHP andpropertyowners Services totheenergysystem(DSO &TSO)
Pre-Runner to SPH: E-Island 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). Dipl. Ing. (FH) Hans Schäfers Schaefers@sumbi.de
3 Bisherige Ergebnisse Mittwoch 2. Juli 2008 Szenario 1: Fahrplaneinhaltung
3 Bisherige Ergebnisse Mittwoch 2. Juli 2008 Modellbildung und Simulation des Szenario 2: Pos MRL -> Regler Min
3 Bisherige Ergebnisse Mittwoch 2. Juli 2008 Modellbildung und Simulation des Szenario 2: Neg. MRL -> Regler Max