Multi-Objective Techno-Economic-Environmental Optimisation of Electric Vehicle for Energy Services

Das, Ridoy, Wang, Yue, Putrus, Ghanim, Kotter, Richard, Marzband, Mousa, Herteleer, Bert and Warmerdam, Jos (2020) Multi-Objective Techno-Economic-Environmental Optimisation of Electric Vehicle for Energy Services. Applied Energy, 257. ISSN 0306-2619

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Das et al - Multi-Objective Techno-Economic-Environmental Optimisation of Electric Vehicle for Energy Services AAM.pdf - Accepted Version

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Official URL: https://doi.org/10.1016/j.apenergy.2019.113965

Abstract

Electric vehicles and renewable energy sources are collectively being developed as a synergetic implementation for smart grids. In this context, smart charging of electric vehicles and vehicle-to-grid technologies are seen as a way forward to achieve economic, technical and environmental benefits. The implementation of these technologies requires the cooperation of the end-electricity user, the electric vehicle owner, the system operator and policy makers. These stakeholders pursue different and sometime conflicting objectives. In this paper, the concept of multi-objective-techno-economic-environmental optimisation is proposed for scheduling electric vehicle charging/discharging. End user energy cost, battery degradation, grid interaction and CO2 emissions in the home micro-grid context are modelled and concurrently optimised for the first time while providing frequency regulation. The results from three case studies show that the proposed method reduces the energy cost, battery degradation, CO2 emissions and grid utilisation by 88.2%, 67%, 34% and 90% respectively, when compared to uncontrolled electric vehicle charging. Furthermore, with multiple optimal solutions, in order to achieve a 41.8% improvement in grid utilisation, the system operator needs to compensate the end electricity user and the electric vehicle owner for their incurred benefit loss of 27.34% and 9.7% respectively, to stimulate participation in energy services.

Item Type: Article
Uncontrolled Keywords: Multi-objective optimisation, Electric vehicle, Battery degradation, Multi-criteria decision-making
Subjects: H800 Chemical, Process and Energy Engineering
Department: Faculties > Engineering and Environment > Geography and Environmental Sciences
Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
Depositing User: Paul Burns
Date Deposited: 11 Oct 2019 10:27
Last Modified: 23 Oct 2020 08:00
URI: http://nrl.northumbria.ac.uk/id/eprint/41086

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