For an uncertain microgrid with integrated electric vehicles (EVs), a distributionally robust scheduling algorithm is proposed. First, Monte Carlo simulation is adopted to generate the uncertain region of charging/discharging scenarios for EVs, and a hybrid scenario set for renewable energy and normal load is generated via FCM clustering. In the day-ahead scheduling stage, with the feasibility of the hybrid scenario set and its probability-weighted performance index of the economic cost as the objective function, the optimal power output of the microgrid equipment is calculated to achieve optimal performance of hybrid scenarios by using the column-and-constraint generation algorithm. Subsequently, a robustness test is conducted to ensure the feasibility of the day-ahead optimal solution for any scenario. In the intraday scheduling stage, real-time data on renewable generation, normal load, and electric vehicle are utilized to optimize power adjustment of the day-ahead solution. Results show that the proposed method improves the economic performance of the microgrid system. Simulation cases verify the effectiveness of the proposed method.
Citation: Guanglin Song, Pengyuan Zheng, Chen Wei, Jiabin Xue, Dong Wang. Robust optimization algorithm for an uncertain EV-integrated microgrid under hybrid scenarios[J]. AIMS Energy, 2026, 14(1): 235-258. doi: 10.3934/energy.2026010
For an uncertain microgrid with integrated electric vehicles (EVs), a distributionally robust scheduling algorithm is proposed. First, Monte Carlo simulation is adopted to generate the uncertain region of charging/discharging scenarios for EVs, and a hybrid scenario set for renewable energy and normal load is generated via FCM clustering. In the day-ahead scheduling stage, with the feasibility of the hybrid scenario set and its probability-weighted performance index of the economic cost as the objective function, the optimal power output of the microgrid equipment is calculated to achieve optimal performance of hybrid scenarios by using the column-and-constraint generation algorithm. Subsequently, a robustness test is conducted to ensure the feasibility of the day-ahead optimal solution for any scenario. In the intraday scheduling stage, real-time data on renewable generation, normal load, and electric vehicle are utilized to optimize power adjustment of the day-ahead solution. Results show that the proposed method improves the economic performance of the microgrid system. Simulation cases verify the effectiveness of the proposed method.
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Guanglin Song, Pengyuan Zheng, Chen Wei, Jiabin Xue, Dong Wang. Robust optimization algorithm for an uncertain EV-integrated microgrid under hybrid scenarios[J]. AIMS Energy, 2026, 14(1): 235-258. doi: 10.3934/energy.2026010
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