| 引用本文: | 赵鹏杰,吴俊勇,王燚,张和生.基于深度强化学习的微电网优化运行策略[J].电力自动化设备,2022,42(11): |
| ZHAO Pengjie,WU Junyong,WANG Yi,ZHANG Hesheng.Optimal operation strategy of microgrid based on deep reinforcement learning[J].Electric Power Automation Equipment,2022,42(11): |
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| 摘要: |
| 风电、光伏、负荷的不确定性给含有高比例可再生能源的微电网制定运行策略带来了挑战，人工智能技术的发展为解决微电网运行优化问题提供了新思路。基于强化学习框架，将微电网运行问题转化为马尔可夫决策过程，以最大化微电网经济利益和居民满意度为目标，提出一种基于深度强化学习的微电网在线调度方法。为了在深度强化学习训练的过程中高效利用经验，设计一种优先经验存储的深度确定性策略梯度(PES-DDPG)算法，学习各类环境下不同时段的微电网最优调度策略。算例结果表明，PES-DDPG算法能够为微电网提供有效的调度策略，并实现微电网的实时优化。 |
| 关键词: 深度强化学习 微电网 马尔可夫模型 优化运行 |
| DOI：10.16081/j.epae.202205032 |
| 分类号:TM73 |
| 基金项目:中央高校基本科研业务费专项资金资助项目(2020YJS162) |
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| Optimal operation strategy of microgrid based on deep reinforcement learning |
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ZHAO Pengjie, WU Junyong, WANG Yi, ZHANG Hesheng
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School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China
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| Abstract: |
| The uncertainty of wind power, photovoltaic and load brings challenges to the formulation of operation strategy for microgrid with high proportion of renewable energy, and the development of artificial intelligence technology provides a new idea for solving the operation optimization problem of microgrid. Based on the reinforcement learning framework, the operation problem of microgrid is transformed into a Markov decision process, and an online scheduling method of microgrid based on deep reinforcement learning is proposed, which takes the maximum economic benefit of microgrid and residents’ satisfaction as its object. In order to effectively use the experience in the training process of deep reinforcement learning, a PES-DDPG(Priority Experience Storage Deep Deterministic Policy Gradient) algorithm is designed to learn the optimal scheduling strategy of microgrid for different periods under each type of environment. Case results show that PES-DDPG algorithm can provide effective scheduling strategy for microgrid and realize real-time optimization of microgrid. |
| Key words: deep reinforcement learning microgrid Markov model optimal operation |
