适应随机序贯决策的分布式储能优化规划方法  

Optimization Programming Method for Distributed Energy Storage Suitable for Stochastic Sequential Decision-making

作　　者：高松 黄河 李妍[2] 姜家兴 GAO Song;HUANG He;LI Yan;JIANG Jiaxing(Jiangsu Electric Power Company,Nanjing 210024,China;State Key Laboratory of Advanced Electromagnetic Engineering and Technology,Huazhong University of Science and Technology,Wuhan 430074,China)

机构地区：[1]江苏省电力有限公司,南京210024 [2]华中科技大学强电磁工程与新技术国家重点实验室,武汉430074

出　　处：《高电压技术》2022年第11期4385-4392,共8页High Voltage Engineering

基　　金：国家重点研发计划(2017YFB0902800)。

摘　　要：在双碳战略和相关能源政策背景下,为平抑规模化接入分布式能源的潮流随机波动,分布式储能将在配电网逐步推广应用。建立适应随机序贯决策的分布式储能规划模型,将电压幅值、储能动作频次和用电成本作为即时回报优化分布式储能响应,基于优化的分布式储能组合序贯动作进行储能参数配置;基于竞争深度Q网络(dueling deep Q network,DDQN)的深度增强学习方法开展自学习优化,并以全寿命周期投资收益最大化确定分布式储能布点与配置方案。最后在IEEE33节点算例系统接入分布式光伏和储能的条件下,论证了方法的合理有效性。Under the background of double-carbon strategy(carbon emission peak and carbon neutrality)and related energy policies,in order to stabilize the stochastic fluctuation of power flow caused by large-scale access to distributed energy,distributed energy storage will be gradually popularized and applied in the distribution network.In this paper,a distributed energy storage planning model adapted to stochastic sequential decision-making is established,and the voltage amplitude,energy storage action frequency and electricity cost are defined as immediate reward to optimize the distributed energy storage response.Energy storage parameter configuration is based on optimal combined sequential actions.The deep reinforcement learning method based on dueling deep Q network is used to carry out self-learning optimization,and the site and configuration scheme of distributed energy storage is determined with the goal of maximizing the investment income of the whole life cycle.Finally,on condition that the IEEE 33-node system is connected to distributed photovoltaic and energy storage,the rationality and effectiveness of the method are verified.

关 键 词：分布式储能 优化规划 随机序贯决策 深度强化学习 竞争深度Q网络 光伏 

分 类 号：TM715[电气工程—电力系统及自动化] TP18[自动化与计算机技术—控制理论与控制工程]