基于近端策略优化算法的电化学/氢混合储能系统双层配置及运行优化

Research on Two-Layer Configuration and Operation Optimization Based on Proximal Policy Optimization for Electrochemical/Hydrogen Hybrid Energy Storage System

针对电化学储能和氢储能的互补特性,提出了一种包含电化学和氢储能的混合储能系统配置和运行的综合优化模型,并提出了智能算法进行求解。该模型基于双层决策优化问题,将混合储能系统配置及运行2个不同时间维度的问题分上下层进行综合求解,并考虑了两者间的相互影响,采用强化学习近端策略优化(proximal policy optimization,PPO)算法求解该双层优化模型。以甘肃省某地区的风光数据,通过对比应用多种传统算法求解结果,验证了所用算法在复杂环境下适应度最高且收敛速度最快。研究结果表明,应用该模型最大可降低24%的弃风、弃光率,有效提升系统综合效益。氢储能作为容量型储能配置不受地形因素限制,适用于多样的应用场景,从而为氢储能这一新型储能形态在全国的广泛配置提供了应用示范。

According to the complementary characteristics of electrochemical energy storage and hydrogen storage, an integrated optimization model for the configuration and operation of a hybrid energy storage system is given, including electrochemical energy storage, hydrogen storage proposed and an intelligent algorithm. The model is based on a two-layer decision optimization problem, in which two different time dimensions of the hybrid energy storage system configuration and operation are solved in upper and lower layers, and the interaction between them is considered. A reinforcement learning proximal policy optimization(PPO) algorithm is used to solve the two-layer optimization model. By comparing the results of applying various traditional algorithms to solve the scenery data of a region in Gansu Province, it is verified that the used algorithm has the highest adaptability and the fastest convergence speed in a complex environment. The results show that the application of this model can reduce the abandoning rate of wind and solar power by 24% and effectively improve the comprehensive benefit of the system, and that hydrogen storage as a capacity-based energy storage configuration is not limited by topographical factors and is suitable for diverse application scenarios, thus providing an application demonstration for the widespread deployment of hydrogen storage, a new form of energy storage, in the whole country.