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Dynamic Economic Scheduling with Self-Adaptive uncertainty in Distribution Network Based on Deep Reinforcement Learning

引用

Energy Engineering 2024年第6期121卷 1671-1695页

作者： Guanfu Wang Yudie Sun Jinling Li Yu Jiang Chunhui Li Huanan Yu He Wang Shiqiang Li State Grid Liaoning Electric Power Co. Ltd.Liaoyang Power Supply CompanyLiaoyang111000China Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of Education(Northeast Electric Power University)Jilin132012China Jilin Northeast Electric Power University Science and Technology Development Co. Ltd.Jilin132012China

Traditional optimal scheduling methods are limited to accurate physical models and parameter settings, which aredifficult to adapt to the uncertainty of source and load, and there are problems such as the inability to make dynamicdecisions continuously. This paper proposed a dynamic economic scheduling method for distribution networksbased on deep reinforcement learning. Firstly, the economic scheduling model of the new energy distributionnetwork is established considering the action characteristics of micro-gas turbines, and the dynamic schedulingmodel based on deep reinforcement learning is constructed for the new energy distribution network system with ahigh proportion of new energy, and the Markov decision process of the model is defined. Secondly, Second, for thechanging characteristics of source-load uncertainty, agents are trained interactively with the distributed networkin a data-driven manner. Then, through the proximal policy optimization algorithm, agents adaptively learn thescheduling strategy and realize the dynamic scheduling decision of the new energy distribution network ***, the feasibility and superiority of the proposed method are verified by an improved IEEE 33-node simulationsystem.

关键词： Self-adaptive the uncertainty of sources and load deep reinforcement learning dynamic economic scheduling

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