摘要
作为多类分布式能源的集成者,微网在促进清洁低碳能源发展方面有巨大潜力。然而,可再生能源出力的不确定性给微网的管理带来了挑战,同时也将这种不确定因素带给外部电网。文章基于实时市场,构建了一个包含新能源机组、传统机组和需求响应资源的微网环境,并采用了能够利用环境信息的深度确定性策略梯度算法,这种无模型(Model-free)的强化学习算法有助于充分利用累积的数据信息,能够更好地适应不确定环境,在连续的状态空间和动作空间中进行学习提升。仿真结果表明,所提算法能够有效应对微网中的不确定因素,降低微网运行成本。
As an integrator of distributed energy,micro-grid has great potential in promoting the development of clean and low-carbon energy.However,the uncertainty of renewable energy output brings challenges to the management of micro-grid,and also brings this uncertainty to the external grid.Based on the real-time market,this paper constructs a micro-grid environment including new energy units,traditional units and demand response resources,and adopts a deep deterministic strategy gradient algorithm which can utilize the environmental information.This model-free reinforcement learning algorithm helps to make full use of the accumulated data information,which can better adapt to the uncertain environment and improve in the continuous state space and action space.Simulation results show that the proposed algorithm can reduce the operating cost of micro-grid while dealing with the uncertain factors effectively.
作者
郭国栋
龚雁峰
Guo Guodong;Gong Yanfeng(State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources(North China Electric Power University),Beijing 102206,China)
出处
《电测与仪表》
北大核心
2021年第9期78-88,共11页
Electrical Measurement & Instrumentation
基金
国家电网公司科技资助项目(SGTYHT/15-JS-191)。
关键词
微网
深度强化学习
电力市场
可再生能源
micro-grid
deep reinforcement learning
electricity market
renewable energy
作者简介
郭国栋(1995-),男,博士研究生,研究方向为人工智能在电力系统中的应用。Email:gbjdsf@163.com;龚雁峰(1977-),男,教授,博士生导师,研究方向为电力系统保护与控制、电力系统稳定分析、先进测控与计算机技术在电力系统应用等。Email:yanfeng.gong@ncepu.edu.cn。
