With the rapid development of new energy technologies,a multi-objective optimization method based on deep reinforcement learning was proposed for the configuration and design of hybrid energy systems.Firstly,a deep reinforcement learning method based on recurrent neural networks was designed to approximate the time-consuming process of objective calculation in the optimization of system configuration schemes.The energy system operation control strategy could be optimized in a fast manner by means of online optimization.Secondly,taking economy,reliability and environmental benefits as optimization objectives,a multi-objective evolutionary algorithm was used to optimize the configuration of hybrid energy system,and Pareto solution set satisfying different user preferences was calculated.Finally,an off grid hybrid energy system was taken as an example to verify the effectiveness of this method.The proposed method was obviously superior to various traditional optimization methods.
关键词
深度强化学习/多目标优化/混合能源系统规划
Key words
deep reinforcement learning/multi-objective optimization/hybrid energy system planning
维普
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