多智能体深度强化学习驱动的跨园区能源交互优化调度

Deep Reinforcement Learning-driven Cross-Community Energy Interaction Optimal Scheduling

为协调多园区综合能源系统各个园区之间的能量交互,多能源子系统之间的能源转换,实现综合能源系统整体优化调度,提出一种利用多智能体深度强化学习算法学习不同园区的负荷特征,并在此基础上进行决策的综合调度模型。该模型将多园区综合能源系统的调度问题转化为马尔科夫决策过程,并利用深度强化学习算法进行求解,避免了对多园区、多能源子系统之间复杂的能量耦合关系进行建模。仿真结果表明,所提方法可以很好地捕捉到不同园区的负荷特性,并利用其中的互补特性协调不同园区之间进行合理的能量交互,可以实现弃风率由16.3%降低至0,并可以使总运行成本降低5445.6元,具有良好的经济效益和环保效益。

In order to coordinate energy interactions among various communities and energy conversions among multi-energy subsystems within the multi-community integrated energy system under uncertain conditions,and achieve overall optimization and scheduling of the comprehensive energy system,this paper proposes a comprehensive scheduling model that utilizes a multi-agent deep reinforcement learning algorithm to learn load characteristics of different communities and make decisions based on this knowledge.In this model,the scheduling problem of the integrated energy system is transformed into a Markov decision process and solved using a data-driven deep reinforcement learning algorithm,which avoids the need for modeling complex energy coupling relationships between multi-communities and multi-energy subsystems.The simulation results show that the proposed method effectively captures the load characteristics of different communities and utilizes their complementary features to coordinate reasonable energy interactions among them.This leads to a reduction in wind curtailment rate from 16.3%to 0%and lowers the overall operating cost by 5445.6 Yuan,demonstrating significant economic and environmental benefits.