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DECISION SUPPORT FOR Renewable Energy Provisioning

Prof. Dr. Ursula Eicker University of Applied Sciences Stuttgart Urban Data and Workflows for building sector transformation strategies. DECISION SUPPORT FOR Renewable Energy Provisioning Load management and storage, solutions for sector coupling

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DECISION SUPPORT FOR Renewable Energy Provisioning

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  1. Prof. Dr. Ursula EickerUniversity of Applied Sciences StuttgartUrban Data and Workflows forbuildingsectortransformationstrategies

  2. Roleof urban 3D modelingtools • DECISION SUPPORT FOR • Renewable Energy Provisioning • Load management and storage, solutions for sector coupling • Energyefficiencyforthebuildingsector: potential andcost

  3. INSEL 4D:: Urban datamodelgeneration and simulationengines

  4. Urban Modeling platform Model import Preprocessing Weather Out

  5. Influence of Building dataqualityfor a district (Ludwigsburg Grünbühl) Comparison of “census data model”, “detailed data model” and monitored heating demand deduced from gas consumptions Total deviation: Standard datamodel – gas data: +30% Detaileddatamodel – gas data: +10%

  6. Improving urban datasetsbyintegrationofusers • Crowd-sourcingtoimprovebuildingconstruction and usageparameters

  7. Improving urban datasetsbysensorbasedmethods Bildquelle: EUROSENSE GmbH

  8. Application: scenariomodelingofheating and coolingdemand Citywidebuilding stock modeling VisualizationofmodeledheatingdemandresidentialsectorExample: Ludwigsburg (City) Visualizationofmodeledheatingdemandof a cityquarter and ofsavings after refurbishment Example: Ludwigsburg (Grünbühl)

  9. Heatdensityvisualisation and automateddistrictheatingnetworkplanning

  10. Visualization Web-basedvisualization TouchLab

  11. Applications in casestudy – County of Ludwigsburg • 39 municipalities • 535,000 inhabitants • 686 km² area • 111,559 buildings • Capital City: Ludwigsburg • Sixth-biggestcountydistrictofGermany

  12. Case Study County of Ludwigsburg • UsingthePhotovoltaicPotential (wholeregion) • In somemunicipalitiesmorethan 100 % oftheannualelectricitydemandcanbecoveredby PV modules • The averagetechnical potential is 76% • The economic PV potential is 55%

  13. Case Study County of Ludwigsburg • The politically agreed target is to reduce CO2 emissions by 2% every year. Costsof PV for 2% annual CO2 reduction 155 €/inhabitant New PV in 3% ofthebuildingseachyear

  14. Case Study New York • Energyanalysis in a New York Network (Borough Hall)

  15. ConEdison time resolvedhourlyloaddata (Borough Hall) Hour

  16. Comparisonmonthlycoolingelectricitymeasured – simulated (Borough Hall)

  17. Case Study New York • Coolingelectricityreductionbyrefurbishment 28% coolingelectricitysavings in the hottest month

  18. Conclusions • Urban modelingtoolssupport urban decisionmaking • Urban modelingbased on 3D citydata and simulationenvironment INSEL canquantifyefficiency and renewablepotentials in the urban district • A reliable and standardised urban datamodelisnecessary and needstobeimprovedbycrowdsourcingornewsensingmethods • CASE STUDIES show: • Strong reduction in heatingdemandbyrefurbishment (43 to 46% for medium refurbishment in Germany and New York, 55 to 58% fordeepretrofit) • Total reduction in coolingelectricity 17%, in the hottest month 28% in New York • PV contributionsignificant in urban areas

  19. Thankyouforyourattention • Prof. Dr. habil. Ursula Eicker • Research Center SustainableEnergy Technologies • University of Applied Sciences Stuttgart, Germany

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