This work aims at solving complex problems of the optimal scheduling model of active distribution network, teaching strategies are proposed to improve the global search ability of particle swarm optimization. Moreover, based on the improved Euclidean distance cyclic crowding sorting strategy, the convergence ability of Li Zhiquan algorithm is improved. With the cost and voltage indexes of the energy storage system of the distribution network as the goal, different optimized configuration schemes are constructed, and the improved HTL-MOPSO algorithm is adopted to find the solution. The results show that compared with the traditional TV-MOPSO algorithm, the proposed algorithm has better convergence performance and optimization ability, and has a lower economic cost. In short, the algorithm proposed can provide a basis for improving the optimization of active distribution network scheduling strategies.

这项工作旨在解决主动配电网最优调度模型的复杂问题，提出了提高粒子群优化算法全局搜索能力的教学策略。此外，基于改进的欧氏距离循环拥挤排序策略，提高了李志全算法的收敛能力。以配电网储能系统的成本和电压指标为目标，构建了不同的优化配置方案，并采用改进的HTL-MOPSO算法寻找解决方案。结果表明，与传统的TV-MOPSO算法相比，该算法具有更好的收敛性能和优化能力，具有较低的经济成本。简而言之，