多主体博弈下基于改进NashQ算法的风电场调度策略
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TM734

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福建省教育厅中青年教师教育科研项目(JAT190043);福州大学科研启动项目(510901)


A wind power dispatching strategy based on improved NashQ under multi-agent game
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    摘要:

    针对可再生能源消纳与发电市场的博弈问题,研究不同场景中风电的调度策略,提出基于改进NashQ算法的风电调度策略模型。首先,在市场上博弈环境下建立风电优化调度模型,计及风电上网的预测偏差考核惩罚、风力发电经济效益与环境效益,考虑可再生能源的弃电限制,在这一基础上,对比风电独立运行、风—光、风—储联合运行下的风电调度策略;其次,采用JS散度优化各个智能体的学习率,提高多智能体强化学习的收敛效率;最后,在Matlab中搭建电网模型进行分析,仿真结果验证:改进NashQ方法相较于NashQ、NETRL算法的收敛速度有明显提升,风—车联合运行模式在多主体博弈下有较好吸引力。

    Abstract:

    Aiming at the problem of renewable energy gaming in power generation market, this paper studies wind power dispatching strategies in different scenarios, and proposes an improved NashQ wind power dispatching strategy. Firstly, a wind power optimal dispatch model under a game environment is established. In the dispatch model, the punishment for wind power forecast error, the environmental and economic benefits of wind power, and the cost of the curtailment of renewable energy are all considered. On this basis, the dispatch strategies are compared for the independent wind power operation mode, wind-vehicle operation mode, and wind-storage joint operation mode. Secondly, The Jensen-Shannon divergence is introduced for the learning rate of the intelligent agents. The convergence efficiency of multi-agent reinforcement learning is then improved. Finally, a microgrid model is constructed in Matlab for simulation. It is shown that the improved NashQ method has a significantly higher convergence speed than the NashQ and NETRL algorithms, and the wind-vehicle joint operation model has a better performance in the market games.

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郑海林,朱振山,温步瀛,等.多主体博弈下基于改进NashQ算法的风电场调度策略[J].电力科学与技术学报,2022,37(6):62-72.
ZHENG Hailin, ZHU Zhenshan, WEN Buying, et al. A wind power dispatching strategy based on improved NashQ under multi-agent game[J]. Journal of Electric Power Science and Technology,2022,37(6):62-72.

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  • 在线发布日期: 2023-01-16
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