Open Access

ARTICLE

Microgrid Scheduling with the Participation of Electric Vehicles under Extreme Weather Conditions

Zujun Ding, Zhi Liu, Peng Huang, Yuhan Qian, Chengyi Li, Zizhuo Yu, Hui Huang, Baolian Liu, Wan Chen, Jie Ji^*
Electric Engineering Department, Huaiyin Institute of Technology, Huaiyin, China
* Corresponding Author: Jie Ji. Email:

Energy Engineering https://doi.org/10.32604/ee.2025.074440

Received 11 October 2025; Accepted 27 November 2025; Published online 09 March 2026

Abstract

Under extreme weather conditions (such as hurricanes and heatwaves causing sudden drops in renewable energy output and surges in load), microgrid operations face severe challenges due to the uncertainty of renewable energy and load fluctuations. Although existing research has focused on microgrid optimal scheduling or electric vehicle integration, there has not yet been a systematic approach to multi-timescale scheduling that combines electric vehicle fleets under extreme weather scenarios, and particularly, explicit modeling of weather events and their impact on component failure rates and transmission lines is lacking. This paper proposes, for the first time, a multi-timescale optimal scheduling strategy integrated with an electric vehicle fleet, filling this gap. By constructing a microgrid model containing diesel generators, micro gas turbines, renewable energy sources, energy storage, and demand response loads, and defining four typical extreme weather scenarios (high solar & high wind, high solar & low wind, low solar & high wind, low solar & low wind) to simulate the impact of extreme events, a day-ahead and intraday coordinated framework aiming to minimize total operating costs is established. In this framework, the day-ahead stage formulates a preliminary plan based on wind and solar forecasts, while the intraday stage employs the mobile energy storage characteristics of the electric vehicle fleet for rolling adjustments to cope with renewable fluctuations and sudden load changes. Simulations based on actual data from Huai’an City in 2024 show that this strategy can significantly reduce microgrid operating costs (by 5.6%–7.2%), increase renewable energy utilization (94%–96%), reduce carbon emissions (17.8%–22.6%), and enhance the system’s economic performance and resilience under extreme weather conditions.

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Keywords

Microgrid; electric vehicle cluster; multi-time scale scheduling; extreme weather; renewable energy absorption; optimal operation; demand response

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