Research on Multi-Time Scale Optimal Scheduling of Virtual Power Plant Based on Real-Time Feedback of Fuzzy-Probability Strategy

被引：0

作者：

Li X. ^{[1
]}

Zhao D. ^{[1
]}

机构：

[1] School of Electrical and Electronic Engineering, North China Electric Power University, Beijing

来源：

Diangong Jishu Xuebao/Transactions of China Electrotechnical Society | 2021年 / 36卷 / 07期

关键词：

Fuzzy-probability strategy; Markov decision process (MDP); Multi-time scale; Optimal scheduling; Virtual power plan (VPP);

D O I：

10.19595/j.cnki.1000-6753.tces.200929

中图分类号：

学科分类号：

摘要：

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. © 2021, Electrical Technology Press Co. Ltd. All right reserved.

引用

页码：1446 / 1455

页数：9

共 20 条

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