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The EV-olution of the power system: A spatio-temporal optimisation model to investigate the impact of electric vehicle deployment

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  • Heuberger, Clara F.
  • Bains, Praveen K.
  • Mac Dowell, Niall

Abstract

Power system models have become an essential part of strategic planning and decision-making in the energy transition. While techniques are becoming increasingly sophisticated and manifold, the ability to incorporate high resolution in space and time with long-term planning is limited. We introduce ESONE, the Spatially granular Electricity Systems Optimisation model. ESONE is a mixed-integer linear program, determining investment in power system generation and transmission infrastructure while simultaneously optimising operational schedule and optimal power flow on an hourly basis. Unique data clustering combined with model decomposition and an iterative solution procedure enable computational tractability. We showcase the capabilities of the ESONE model by applying it to the power system of Great Britain under CO2 emissions reduction targets. We investigate the effects of a spatially distributed large-scale roll-out of electric vehicles (EVs). We find EV demand profiles correlate well with offshore and onshore wind power production, reducing curtailment and boosting generation. Time-of-use-tariffs for EV charging can further reduce power supply and transmission infrastructure requirements. In general, Great Britain’s electricity system absorbs additional demand from ambitious deployment of EVs without substantial changes to system design.

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  • Heuberger, Clara F. & Bains, Praveen K. & Mac Dowell, Niall, 2020. "The EV-olution of the power system: A spatio-temporal optimisation model to investigate the impact of electric vehicle deployment," Applied Energy, Elsevier, vol. 257(C).
    • Handle: RePEc:eee:appene:v:257:y:2020:i:c:s0306261919314023
    DOI: 10.1016/j.apenergy.2019.113715
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    18. Demirhan, C. Doga & Tso, William W. & Powell, Joseph B. & Pistikopoulos, Efstratios N., 2021. "A multi-scale energy systems engineering approach towards integrated multi-product network optimization," Applied Energy, Elsevier, vol. 281(C).
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