针对分布式电源选址定容规划中求解维度高、离散与连续变量共存的问题，提出了一种先确定备选位置，再用启发式算法进行容量和位置优化的方法。首先，依据有功损耗改善率、介数中心性及紧密中心性3个指标对节点进行筛选，结合节点负荷情况对邻近节点进行合并，形成备选节点集。随后，构建了以最小化总有功损耗、电压稳定性和分布式电源总容量为目标的优化模型。为有效求解该模型，提出了一种基于混沌映射、自适应权重和莱维飞行策略的改进蜣螂算法，以优化备选节点中的分布式电源位置和容量。最后，通过IEEE⁃33和IEEE⁃69节点系统的仿真验证表明，所提方法在降低总有功损耗和节点电压偏差方面表现优异。

Aiming at the problem of high-dimensional solving and coexisting discrete and continuous variables in the site selection and capacity planning of distributed power generation, a method is proposed to first determine candidate locations and then a heuristic algorithm is used to optimize the capacity and location. Firstly, the nodes are screened according to the three indicators: active loss improvement rate, betweenness centrality and closeness centrality, and adjacent nodes are merged according to the node power load conditions to form a set of candidate nodes. Subsequently, an optimization model with the goal of minimizing total active loss, maximizing voltage stability, and minimizing total capacity of distributed power generation is constructed. In order to effectively solve the model, dung beetle algorithm is improved based on chaotic mapping, adaptive weights and Levy flight strategy to optimize the locations and capacities of distributed generation in the candidate nodes. Finally, simulation verifications on IEEE-33 and IEEE-69 node systems show that the proposed method performs well in reducing total active power loss and node voltage deviation.