基于深度强化学习的梯级水蓄风光互补系统优化调度策略研究  

Research on Optimized Dispatching Strategy of Cascade Hydropower-pumping-storage-wind-photovoltaic Multi-energy Complementary System Based on Deep Reinforcement Learning

作　　者：刘建行 刘方 LIU Jianhang;LIU Fang(College of Electrical Engineering,Shanghai University of Electric Power,Shanghai 200090,China)

机构地区：[1]上海电力大学电气工程学院,上海200090

出　　处：《广东电力》2024年第5期10-22,共13页Guangdong Electric Power

基　　金：上海市2021年度青年科技英才“扬帆计划”项目(21YF1414600)。

摘　　要：对常规水电站进行抽水蓄能功能重塑,使其由“电源供应者”逐步转为“电源供应者+‘电池’调节者”,是解决大规模灵活性资源需求的重要技术手段。以梯级水蓄风光互补系统(cascade hydropower-pumping-storage-wind-photovoltaic multi-energy complementary system,CHPMCS)为研究对象,首先针对其发电抽蓄双向运行工况灵活转换和互补消纳特征,以系统发电效益最大为目标建立短期优化运行模型;其次,考虑CHPMCS出力连续可调的特点,提出将优化调度问题转换为马尔可夫决策过程,从而将多约束优化问题转换为无约束深度强化学习问题;然后,针对深度确定性策略梯度(deep deterministic policy gradient,DDPG)算法训练效率低、易陷入局部最优等缺陷,采用改进DDPG算法对优化调度决策过程进行求解。最后,通过算例验证所提模型和算法的有效性。结果表明:CHPMCS通过水电功能重塑,有效提升了灵活性和调节能力,可以提高新能源的消纳能力和水资源的利用率,并通过“低储高发”提高系统发电效益。It is an important technical means to address large-scale flexible resource demands reshaping the pumped storage function of conventional hydropower stations,gradually shifting their role from a power supplier to a power supplier+battery regulator.In this regard,this paper takes the cascade hydropower-pumping-storage-wind-photovoltaic multi-energy complementary system(CHPMCS)as the research object and establishes a short-term optimal operation model with the objective of maximizing the benefits of system power generation in view of the flexible conversion of power generation-pumping and storage bidirectional operating conditions and the characteristics of complementary consumption.Secondly,considering the continuous and adjustable output of the CHPMCS,the paper proposes to transform the optimized dispatching problem into a Markov decision process,thereby transforming the multi constraint optimization problem into an unconstrained deep reinforcement learning problem.Then,to address the shortcomings of low training efficiency and susceptibility to local optima in the deep deterministic policy gradient(DDPG)algorithm,it uses an improved DDPG algorithm to solve the optimized dispatching decision process.Finally,it verifies the effectiveness of the proposed model and algorithm through numerical examples.The results show that the CHPMCS can effectively enhance its flexibility and regulatory ability through the reshaping of hydropower functions,improve the consumption capacity of new energy and the utilization rate of water resources,and improve the power generation efficiency of the system through low storage and high generation.

关 键 词：梯级水蓄风光互补系统 优化调度 新能源消纳 深度强化学习 改进深度确定性策略梯度算法 

分 类 号：TM73[电气工程—电力系统及自动化]