RESEARCH ON MULTI-MICROGRID DAY-AHEAD ECONOMIC OPTIMIZATION SCHEDULING BASED ON IMPROVED SAC ALGORITHM

Zhao Zhihua; Ni Huan

doi:10.19912/j.0254-0096.tynxb.2024-1835

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (2) : 355-364. DOI: 10.19912/j.0254-0096.tynxb.2024-1835

RESEARCH ON MULTI-MICROGRID DAY-AHEAD ECONOMIC OPTIMIZATION SCHEDULING BASED ON IMPROVED SAC ALGORITHM

Zhao Zhihua, Ni Huan

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Abstract

Aiming at the multi-microgrid system model considering the output of electric vehicles, photovoltaics and wind power, an economic optimization scheduling structure for multi microgrid systems based on deep reinforcement learning is established with the minimization of the total operating cost of the system as the objective function, and the state, action, reward function and neural network structure of the improved SAC algorithm are designed by using the framework of the improved SAC algorithm. After simulation and comparative analysis, the scheduling strategy obtained by the algorithm reduces the total operating cost.

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microgrids / deep reinforcement learning / optimization / scheduling / improved SAC algorithm

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Zhao Zhihua, Ni Huan. RESEARCH ON MULTI-MICROGRID DAY-AHEAD ECONOMIC OPTIMIZATION SCHEDULING BASED ON IMPROVED SAC ALGORITHM[J]. Acta Energiae Solaris Sinica. 2026, 47(2): 355-364 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1835

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