Fichtner, Wolf

Mainzer, Kai

McKenna, Russell

English

KITopen

KIT – University of the State of Baden-Wuerttemberg and National Research Center of the Helmholtz AssociationINSTITUTE FOR INDUSTRIAL PRODUCTION (IIP), CHAIR OF ENERGY ECONOMICSwww.kit.eduA model for the identification and optimal planning of emission reduction measures in urban energy systemsKai Mainzer, Russell McKenna, Wolf FichtnerUrban Transitions Global Summit 2016, Shanghai, 08.09.2016commons.wikimedia.orgKai Mainzer,Chair of Energy Economics, IIP2AgendaIntroductionMotivationRelated workMethodData acquisition: Demand structure; PV, Wind & Biomass potentialsOptimization of the urban energy systemResultsDetailed scenario resultsScenario comparisonConclusion and outlook08.09.2016Introduction    >    Method    >    Results    >    ConclusionKai Mainzer,Chair of Energy Economics, IIP3IntroductionCities declare emission reduction targets & climate protection plans, e.g. Covenant of Mayors Initiative: need for energy conceptsLocal renewable energy & efficiency potentials exist, but their exact extent, optimal combinations and contribution towards reaching overarching goals are mostly unknown: cities need decision supportInvestment decisions are long term and capital intensive; interdependencies between technologies: complexity of the problemMathematical models can provide decision support for urban planning08.09.2016Introduction >    Method    >    Results    >    Conclusionsource: [Cook 2015]Kai Mainzer,Chair of Energy Economics, IIP4IntroductionRelated work08.09.2016Introduction >    Method    >    Results    >    Conclusionx - - - - Several models, for a review see e.g. [Keirstead 2012]x x - - - deeco [Bruckner 1996]; URBS [Richter 2004]x x - x - iPlan [Winkelmüller 2006]; EnyCity [Gerbracht 2009]x (x) - x - KomMod [Eggers 2015](x) - x x - Regionenmodell [Steinert 2015]- - x - - Many potential studies, for a review see [Angelis-Dimakis 2011]Requirements for model development:Analysis & Optimization of urban energy systemsUnit commitment and investment planningDetermination of potentials for renewable energies and energy efficiencyTechnologies on supply and demand sideTransferability of the methodx x x x x Own development Existing models can not be used, since the required input data is not available in other regionsKai Mainzer,Chair of Energy Economics, IIP5MethodAnalysis of demand structure (1/2)08.09.2016Introduction    >    Method >    Results    >    ConclusionInfrastructure: Availability of power-, gas- and district heating gridsElectricity demand simulation based on appliance ownership and user activity profilesGeodata: OpenStreetMapKai Mainzer,Chair of Energy Economics, IIP6MethodAnalysis of demand structure (2/2)08.09.2016Introduction    >    Method >    Results    >    ConclusionBuildings: Creation of a typology, based on sizes and age distributionHeat demand mappingbased on building types and technology configurationsGeodata: OpenStreetMapKai Mainzer,Chair of Energy Economics, IIP7Geodata: OpenStreetMap, Satellite images: Bing Mapsmore details in: [Mainzer 2016]MethodPV potential estimation08.09.2016Introduction    >    Method >    Results    >    ConclusionData gatheringBuilding footprintsSatellite imagesDetermination of roof orientations through line detection algorithmsDetection of roof structures like chimneys, roof windows, etc.Algorithm iterates stepwise over usable areas, places as many modules as possiblesimulation of irradiation, energy yield & costs calculationKai Mainzer,Chair of Energy Economics, IIP8MethodWind potential estimationDetermination of available area considering landuse, topographyChoice of turbines based on wind frequency distribution & characteristics08.09.2016Introduction    >    Method >    Results    >    ConclusionGeodata: OpenStreetMap; Topography: NASA SRTMKai Mainzer,Chair of Energy Economics, IIP9MethodBiomass potential estimationLanduse (forests, farmland, …) => Determination of suitable areasCalculation of optimal conversion path: biogas plant, biomass-CHP, …Determination of optimal biomass plant location byminimization of transport distances, considering also distances to settlements, direction of wind (to minimize odor)08.09.2016Introduction    >    Method >    Results    >    ConclusionGeodata: OpenStreetMapBiomass plant location with minimum transport distances, considering residential areasKai Mainzer,Chair of Energy Economics, IIP10MethodOptimization of the urban energy system08.09.2016Introduction    >    Method >    Results    >    Conclusionsystem boundary: municipalityTypgebäude 1energy service demandroom comfortlightingenergy & resourceimportcoal, gas, biomass, ……energy conversion technologies, e.g. heat pump, halogen lightinginfrastructure power distribution network district heating networkbuilding type 1…2015 2020 20508 model years,72 time slicesTechno-economical parameters• Technological availability• Investment, import, fix & variable costs• …electricityenergy conversion technologies, e.g. biomass plantapplianceswaste heatwaste heatLocal renewable energy  potential• area availability• climate• global irradiance• ambient heatKai Mainzer,Chair of Energy Economics, IIP11MethodOptimization of the urban energy systemMethodology: Mixed-integer linear programming (MILP), implemented in GAMSobjective function(s): minimize……Total discounted system cost…CO2 emissions…Energy importconstraintsenergy balancemaximum energy flowsland use & available potentialsemission restrictionscost restrictions…08.09.2016Introduction    >    Method >    Results    >    Conclusion𝑚𝑖𝑛 ෍𝑚𝑦∈YEARS𝛼𝑚𝑦 ∗ 𝑁𝑚𝑦 ∗𝐼𝑚𝑝𝑜𝑟𝑡𝐹𝑙𝑜𝑤𝑠𝐶𝑜𝑠𝑡𝑠𝑚𝑦+𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝐺𝑟𝑖𝑑𝐶𝑜𝑠𝑡𝑠𝑚𝑦+𝐼𝑛𝑡𝑒𝑟𝑚𝑒𝑑𝑖𝑎𝑟𝑦𝐹𝑙𝑜𝑤𝑠𝐶𝑜𝑠𝑡𝑠𝑚𝑦+𝑈𝑛𝑖𝑡𝑠𝐼𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡𝐴𝑛𝑛𝑢𝑖𝑡𝑖𝑒𝑠𝑚𝑦+𝑈𝑛𝑖𝑡𝑠𝐹𝑖𝑥𝐶𝑜𝑠𝑡𝑠𝑚𝑦+𝑃𝑟𝑜𝑐𝑒𝑠𝑠𝐴𝑐𝑡𝑖𝑣𝑖𝑡𝑖𝑒𝑠𝑉𝑎𝑟𝐶𝑜𝑠𝑡𝑠𝑚𝑦+𝐸𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑠𝐶𝑜𝑠𝑡𝑠𝑚𝑦+𝐿𝑎𝑛𝑑𝑈𝑠𝑒𝐶𝑜𝑠𝑡𝑠𝑚𝑦+𝐿𝑜𝑐𝑎𝑙𝑆𝑜𝑢𝑟𝑐𝑖𝑛𝑔𝐶𝑜𝑠𝑡𝑠𝑚𝑦Kai Mainzer,Chair of Energy Economics, IIP12ResultsDetailed scenario resultsOptimal choice & combination of technologiesheating systemsbuilding insulationappliances…Optimal degree of renewable energy utilizationDevelopment of costs, emissions, energy import and primary energy consumption for different scenarios08.09.2016Introduction    >    Method    >    Results >    ConclusionKai Mainzer,Chair of Energy Economics, IIP13ResultsScenario comparison Trade-offs: e.g. significant emission reduction can be achieved with only minor additional costs08.09.2016Introduction    >    Method    >    Results >    Conclusion3 extreme scenarios: what is possible in terms of emissions, costs, etc.With values derived from these extreme scenarios, trade-off scenarios can be foundThis can also be used to increase the level of autarky cost-effectivelyKai Mainzer,Chair of Energy Economics, IIP14Conclusion and outlookMathematical models can provide decision support for urban planningEnergy system models need to provide automated methods for data acquisition in order to be transferrable to other citiesThe presented model provides these methods and thus enables urban planners to find optimal pathways for reaching their specific targetsApplication to case study demonstrates its use and possible results:In cost-minimization scenario, targets may not be reachedFurther scenario comparisons can reveal advantageous trade-off scenariosFurther work:Additional scenarios (especially price development)implementation of sensitivity analysisApplication and validation with more (international) case studies08.09.2016Introduction    >    Method    >    Results    >    ConclusionKai Mainzer,Chair of Energy Economics, IIP15Literature & Related PublicationsCook, R.; Dufour, M. et al. (2015): Energy Union & Governance: The role of local actors. Edited by E3G.Mainzer, K.; Schlund, D. et al. (2016): Rooftop PV Potential Estimations: Automated Orthographic Satellite Image Recognition Based on Publicly Available Data. In: Proceedings of EU PVSEC 2016. Munich, 20 - 24 June 2016. Available online at http://www.eupvsec-proceedings.com/proceedings?fulltext=mainzer&paper=38595.Keirstead, J.; Jennings, M. et al. (2012): A review of urban energy system models: Approaches, challenges and opportunities. In Renewable and Sustainable Energy Reviews 16 (6), pp. 3847–3866. DOI: 10.1016/j.rser.2012.02.047.Bruckner, T. (1996): Dynamische Energie- und Emissionsoptimierung regionaler Energiesysteme. Dissertation,UniversitätWürzburg.Richter, S. (2004): Entwicklung einer Methode zur integralen Beschreibung und Optimierung urbaner Energiesysteme. Dissertation, Universität Augsburg.Winkelmüller, S. (2006): Optimierung der Nachfrage- und Erzeugungsstruktur kommunaler Energiesysteme am Beispiel von Wien. Dissertation, Universität Augsburg.Gerbracht, H.; Kunze, R. et al. (2009). In Möst et al.: Energiesystemanalyse. Tagungsband des Workshops "Energiesystemanalyse" vom 27. November 2008 am KIT-Zentrum Energie, Karlsruhe.Eggers, J.; Stryi-Hipp, G. (2013): KomMod as a tool to support municipalities on their way to becoming smart energy cities. In : Proceedings of the International Sustainable Building Conference Graz 2013. Graz.Steinert, C. (2015): Das Regionenmodell (FREM) – Basis detaillierter Analysen kommunaler Energiekonzepte. Workshop "Urbane Energiesystemmodelle". FfE - Forschungsstelle für Energiewirtschaft e.V. Karlsruhe, 12.11.2015.Angelis-Dimakis, A.; Biberacher, M. et al. (2011): Methods and tools to evaluate the availability of renewable energy sources. In Renewable and Sustainable Energy Reviews 15 (2), pp. 1182–1200. DOI: 10.1016/j.rser.2010.09.049.Mainzer, K.; Fath, K. et al. (2014): A high-resolution determination of the technical potential for residential-roof-mounted photovoltaic systems in Germany. In Solar Energy 105, pp. 715–731. DOI: 10.1016/j.solener.2014.04.015.Killinger, S.; Mainzer, K.; et al. (2015): A regional optimisation of renewable energy supply from wind and photovoltaics with respect to three key energy-political objectives. In Energy. DOI: 10.1016/j.energy.2015.03.050.Mainzer, K.; McKenna, R. et al. Integrating residential energy efficiency measures into optimizing urban energy system models. In: Proceedings of eceee 2015 Summer Study on energy efficiency. Presqu'île de Giens, 05.06.2015.Mainzer, K.; McKenna, R. et al. (2015): Rolling Horizon Planning Methods in Long-Term Energy System Analysis MILP Models. CORS/INFORMS International Meeting 2015. Montréal, 17.06.2015.08.09.2016Kai Mainzer,Chair of Energy Economics, IIP16 08.09.2016Thank you very much for your attentioncontact: Kai Mainzerkai.mainzer@kit.eduKarlsruhe Institute of Technology (KIT)Institute for Industrial Production (IIP)Chair of Energy Economics

A model for the identification and optimal planning of emission reduction measures in urban energy systems

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A model for the identification and optimal planning of emission reduction measures in urban energy systems

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