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Multi-Objective Optimization of ev Charging and Discharging for Different Stakeholders

CSEE Journal of Power and Energy Systems 2023年第6期9卷 2301-2308页

作者： Shaofeng Lu Bing Han Fei Xue Lin Jiang Kejun Qian the Shien-Ming Wu School of Intelligent Engineering South China University of TechnologyGuangzhou 511442China the Department of Electrical and Electronic Engineering School of Advanced TechnologyXi'an Jiaotong-Liverpool UniversitySuzhou 215123China the Department of Electrical Engineering and Electronics University of LiverpoolLiverpoolUK Suzhou Power Supply Company State Grid Corporation of ChinaSuzhou 215000China

As a dynamic energy storage system,electric vehicles(ev)play important roles in future power *** this paper,a model for ev aggregator participation in the electricity market has been built with a focus on the feasibility issue of the model arising from economic interest inconsistencies between different stakeholders:ev owners and *** the model,the ev aggregator attends day-ahead energy and reserve markets for profit maximization by scheduling charging and discharging behaviors of *** issue exists since different stakeholders have different interests which are not necessarily consistent,*** maximization leads to increasing ev owners'charging *** investigate the economic relationship between the two stakeholders,two multi-objective optimization methods(weighted sum and$\varepsilon$-constraint methods)are proposed to take the aggregator profit and ev owners'charging fee into account in the model.A sensitivity analysis is applied to examine the aggregator profit under different price scenarios,which reveals the internal relationship between ev owners'charging fees and aggregator *** proposed ev charging and discharging strategy in this paper could be used to determine the settlement price between the aggregator and owners to ensure the feasibility of participation from both ev owners and stakeholders in electricity markets.

关键词： Aggregator economic inconsistency issue electricity market ev owners ev multi-objective optimization

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