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.

该研究提出了一种综合调度模型，利用多智能体深度强化学习算法学习不同社区的负载特征，并基于这些知识做出决策，以在不确定条件下协调各社区之间的能源交互和多能源子系统之间的能量转换，实现综合能源系统的整体优化和调度。模拟结果表明，该方法有效捕捉到不同社区的负载特征，并利用它们的互补特性，在它们之间协调合理的能源交互，从而将风电弃能率从 16.3% 降低到 0%，并降低了 5445.6 元的总运营成本，具有显著的经济和环境效益。