Research on Data-Driven Optimal Scheduling of Power System

Jianxun Luo, Wei Zhang (), Hui Wang, Wenmiao Wei and Jinpeng He
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Jianxun Luo: School of Information and Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China
Wei Zhang: School of Information and Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China
Hui Wang: Department of Electrical Engineering, Shandong University, Jinan 250061, China
Wenmiao Wei: Automation Academy, Huazhong University of Science and Technology, Wuhan 430074, China
Jinpeng He: School of Information and Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China

Energies, 2023, vol. 16, issue 6, 1-15

Abstract: The uncertainty of output makes it difficult to effectively solve the economic security dispatching problem of the power grid when a high proportion of renewable energy generating units are integrated into the power grid. Based on the proximal policy optimization (PPO) algorithm, a safe and economical grid scheduling method is designed. First, constraints on the safe and economical operation of renewable energy power systems are defined. Then, the quintuple of Markov decision process is defined under the framework of deep reinforcement learning, and the dispatching optimization problem is transformed into Markov decision process. To solve the problem of low sample data utilization in online reinforcement learning strategies, a PPO optimization algorithm based on the Kullback–Leibler (KL) divergence penalty factor and importance sampling technique is proposed, which transforms on-policy into off-policy and improves sample utilization. Finally, the simulation analysis of the example shows that in a power system with a high proportion of renewable energy generating units connected to the grid, the proposed scheduling strategy can meet the load demand under different load trends. In the dispatch cycle with different renewable energy generation rates, renewable energy can be absorbed to the maximum extent to ensure the safe and economic operation of the grid.

Keywords: grid dispatching optimization; proximal policy optimization algorithm; importance sampling; deep reinforcement learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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