Abstract：The day-ahead plan and operation scheme of virtual power plant (VPP) are the strong guarantee for realizing economic or technical benefits under its internal and external randomness. The multi-level optimization scheduling framework of VPP including day-ahead plan, rolling plan and real-time scheduling is proposed, and optimization models of different scales are established to achieve optimization robustness through the idea of ‘multi-level scheduling and step-by-step refinement’. In the day-ahead plan, a model is established with the goal of maximizing market benefits of virtual power plants. In intra-day optimization, based on the Markov decision process (MDP), the two-layer optimization of 30-min scale rolling plan and 5-min scale real-time scheduling are combined to reduce the modeling level. In the MDP model, the entropy feedback function is established to characterize the rolling optimization process, and the fuzzy-probability strategy is established to characterize the real-time optimization process to adjust the state of charge of energy storage (state of charge, SOC), which are finally effectively track the goals of day-ahead plan to maximize economic benefits. Finally, the proposed scheme was verified by calculation examples.
李翔宇, 赵冬梅. 基于模糊-概率策略实时反馈的虚拟电厂多时间尺度优化调度[J]. 电工技术学报, 2021, 36(7): 1446-1455.
Li Xiangyu, Zhao Dongmei. Research on Multi-Time Scale Optimal Scheduling of Virtual Power Plant Based on Real-Time Feedback of Fuzzy-Probability Strategy. Transactions of China Electrotechnical Society, 2021, 36(7): 1446-1455.
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2021, Vol. 36 
