In this paper we present a novel composite methodology for obtaining spatial projections of the impacts and opportunities arising from the integration of plug-in electric vehicles with future smart electricity grids. We link models of future plug-in electric vehicle uptake, travel by household members, household electricity demand, and recharge of electric vehicles. The analysis is disaggregated in each case to a mesh block or local government area level; vehicle usage and household energy demand fluctuate on a hourly, daily and seasonal basis, subject also to the longer-term trends projected for uptake of the new technology. A similarly fine grain is applied with respect to socio-economic variables. The uptake model combines features of choice modelling, multi-criteria analysis and technology diffusion theory; in this case it was applied to four competing technologies (BEV, PHEV, HEV, ICE), and calibration revealed seven major determinants of uptake: performance, annual costs, purchase cost, household income, driving distance, demographic suitability, and risk or inconvenience. The travel model projects likely patterns of vehicle usage and travel duration based on existing patterns of private vehicle usage. The household demand model includes detailed representation of housing type and usage of electrical appliances. The charge-discharge model embodies plausible algorithms for managing household electricity usage in conjunction with electric vehicle batteries. In the paper we describe the various models and report projected impacts of electric vehicles on peak electrical grid loads for the Australian state of Victoria. The impacts are presented on a spatial basis, to the level of individual mesh blocks and network feeders, under a range of energy management scenarios.
Electric vehicles and the smart grid: Spatial modelling of impacts and opportunities
EVS, Electric Vehicle Symposium, 26 ; 2775-2783
2012
9 Seiten, 7 Bilder, 1 Tabelle, 12 Quellen
Aufsatz (Konferenz)
Englisch
Elektrofahrzeug , Elektrizität , Energiebedarf , Fahrdistanz , Smart Grid , Elektrogerät , Energiemanagement , Plug-in-Hybridfahrzeug , Laden (elektrisch) , Kraftfahrzeugbatterie , Risiko , Betriebskosten , Spitzenlast , räumliche Datenmodellierung , Stromnetz , Haushalt (Familie) , batterieelektrisches Fahrzeug , Motor mit innerer Verbrennung , Anschaffungskosten
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