According to the characteristics of an AC/DC hybrid microgrid, this paper presents a new method for network planning of an AC/DC hybrid microgrid based on clustering partition and multi-agent reinforcement learning algorithm. The planning method is divided into three steps, namely, sub-microgrid partition, sub-network planning, and main network planning. On the basis of the principle of partition, a clustering partition model considering the location, type, and capacity of distributed generators (DG) and load is established, to divide this system into different AC and DC sub-microgrids. Then, the optimization model of the sub-network considering the annual cost of the converter and the optimization model of the main network considering the change of network loss of the sub-microgrid are proposed, which form a bilayer optimization model of the AC/DC hybrid microgrid. The optimal clustering partition is obtained by combining the k-means clustering algorithm with the particle swarm optimization algorithm, and considering the interaction between main network planning and sub-network planning, the network planning model is solved by the multi-agent reinforcement learning algorithm based on the Stackelberg equilibrium game. A numerical example is given to verify the validity and accuracy of the above models and algorithms.

中文翻译：

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基于聚类和多智能体强化学习的交直流混合微网最优网络规划

针对AC / DC混合微电网的特点，提出了一种基于聚类划分和多智能体强化学习算法的AC / DC混合微电网网络规划的新方法。该规划方法分为三步，分别是亚微电网划分，子网络规划和主网络规划。根据分区原理，建立了考虑分布式发电机（DG）的位置，类型，容量和负荷的聚类分区模型，将该系统划分为不同的交，直流亚微电网。然后，提出了考虑变流器年成本的子网络优化模型和考虑亚微电网的网络损耗变化的主网络优化模型，形成AC / DC混合微电网的双层优化模型。通过将k-means聚类算法与粒子群优化算法相结合，获得最优的聚类分区，并考虑主网络规划与子网络规划之间的交互作用，采用基于多智能体强化学习算法对网络规划模型进行求解。在Stackelberg平衡博弈中。数值例子验证了上述模型和算法的有效性和准确性。通过基于Stackelberg均衡博弈的多智能体强化学习算法求解网络规划模型。数值例子验证了上述模型和算法的有效性和准确性。通过基于Stackelberg均衡博弈的多智能体强化学习算法求解网络规划模型。数值例子验证了上述模型和算法的有效性和准确性。