Robust economic dispatch for industrial microgrids with electric vehicle demand response

Shiliang Guo, Jianqi He, Kai Ma, Jie Yang, Yaochen Wang and Pengpeng Li

Renewable Energy, 2025, vol. 240, issue C

Abstract: The uncoordinated charging and discharging behavior of electric vehicles (EVs) will affect the economy and reliability of the industrial microgrid (IMG). Demand response (DR) program provides a promising approach to address these challenges, as coordinating EVs and industrial loads can enhance microgrid’s flexibility. However, the impact of real-time market electricity price uncertainty on DR cannot be ignored. To tackle this issue, this study develops a scheduling model aimed at minimizing the total operational costs of IMG. The proposed model integrates bidirectional energy trading involving renewable energy, EVs, and energy storage systems, and sets priority and penalty costs to different power types connected to the grid. Furthermore, a comprehensive DR program combines price-based demand response and incentives from microgrid operators is proposed, and the impact of electricity price on scheduling is analyzed. A robust optimization method is employed to address electricity price uncertainty, while nonlinear terms are linearized to construct a mixed integer linear model. Simulation results show that under electricity price uncertainty, the proposed strategy reduces microgrid operating costs by 10.01% and improves local consumption rate of renewable energy to 98.72% compared to the case without considering DR program. These results confirm the effectiveness of the proposed model.

Keywords: Electric vehicles; Demand response; Industrial microgrid; Electricity price uncertainty; Robust optimization (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:240:y:2025:i:c:s096014812402278x

DOI: 10.1016/j.renene.2024.122210

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