中国电机工程学报2025，Vol.45Issue(7)：2486-2499,中插4,15.DOI:10.13334/j.0258-8013.pcsee.242305
基于深度强化学习的含储能船舶的海岛-海上渔排能源运输策略研究
Research on Energy Transportation Strategies Between Islands and Offshore Fish Farms for Ships With Energy Storage Based on Deep Reinforcement Learning
摘要
Abstract
This paper addresses the issue of energy interaction between offshore fish farms and islands with surplus wind and solar resources by developing an energy transportation strategy involving fully electric ships for the island-fish farm-coast system.The proposed strategy utilizes deep reinforcement learning,which is well-suited to managing the uncertainties of offshore wind and solar resources and can accommodate large-scale energy transfer models.First,the mobile energy storage battery group is detailed into fully charged,unloaded,and partially charged batteries.Then,the energy transportation problem is modeled as a Markov Decision Process with a hybrid action space.To solve the hybrid action space issue,a parameterized dual deep Q-network based on multi-batch forward propagation is proposed.This method decouples the unrelated discrete and continuous actions using a multi-step forward pass strategy,reducing volatility during the agent's training process and converging to a more optimal solution.Finally,simulation results verify that the proposed strategy effectively facilitates energy transfer between locations.Compared to traditional deep reinforcement learning methods suited for discrete action spaces,the proposed algorithm demonstrates greater flexibility and achieves superior performance in the target scenario.Additionally,comparative analysis in expanding model scales further validates the advantages of the proposed method in addressing large-scale energy transportation challenges.关键词
深度强化学习/全电力船舶/移动式储能电池/混合动作空间Key words
deep reinforcement learning/all-electric ship(AES)/mobile energy storage battery/hybrid action space分类
动力与电气工程引用本文复制引用
朱振山,陈豪,陈炜龙,黄缨惠..基于深度强化学习的含储能船舶的海岛-海上渔排能源运输策略研究[J].中国电机工程学报,2025,45(7):2486-2499,中插4,15.基金项目
福建省科技创新战略联合研究项目(2023R0153).Project Supported by the Joint Research Project of Fujian Provincial Science and Technology Innovation Strategy(2023R0153). （2023R0153）
