分布式能源2025，Vol.10Issue(5)：82-91,10.DOI:10.16513/j.2096-2185.DE.25100080
基于深度强化学习的农村风光水储微电网容量配置研究
Capacity Configuration of Rural Wind-PV-Hydro-Storage Microgrids Based on Deep Reinforcement Learning
摘要
Abstract
To address weak infrastructure,poor voltage stability,and low renewable-energy utilization in rural areas,this paper proposes a siting-and-sizing model for distributed generation(DG)that simultaneously optimizes voltage quality and economic performance.One objective aims to minimize voltage deviations caused by DG integration,thereby enhancing distribution-network power quality;the other seeks to minimize the levelized cost of energy(LCOE)over the full life cycle of the DG portfolio,accounting for investment,operation and maintenance expenses,and energy yield.The model is solved with a double deep Q-network(DDQN),yielding a configuration that balances voltage stability and cost.Simulation on a modified IEEE 33-bus rural feeder shows that the DDQN-based scheme markedly improves voltage profiles while reducing upgrade costs.Furthermore,comparative analyses with the deep Q-network(DQN),non-dominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ),and multi-objective particle swarm optimization(MOPSO)methods verify the superiority of the proposed approach,highlighting the efficiency,adaptability,and robustness of reinforcement learning for complex energy-system optimization.关键词
配电网/可再生能源微电网/强化学习/平准化度电成本(LCOE)/容量配置Key words
distribution networks/renewable energy microgrids/reinforcement learning/levelized cost of energy(LCOE)/capacity allocation分类
能源与动力引用本文复制引用
方勇,王国瑞,席海阔..基于深度强化学习的农村风光水储微电网容量配置研究[J].分布式能源,2025,10(5):82-91,10.基金项目
国网冀北电力有限公司重点科技项目(SGTYHT/21-JS-223)This work is supported by Key Scientific Research Project of State Grid Jibei Electric Power Co.,Ltd.(SGTYHT/21-JS-223) （SGTYHT/21-JS-223）
