In order to solve the problem of intra-network distribution of power resources in microgrid containing clean energy, it is necessary to coordinate the optimization of economic cost and low carbon energy saving, and the existing multi-objective Dung Beetle Optimizer (DBO) algorithm lacked the optimization ability. An improved multi-objective DBO based on non-dominated sorting (NSIDBO) was proposed. Firstly, a microgrid system containing wind-fuel storage and physical constraints of each device was constructed, and a multi-objective cost function based on economy and low carbon was established. Secondly, the Tent mapping based on the disturbance factor was designed, and three parameters were added on this basis to increase the mapping distribution range and improve the initial population diversity. Then, a new type of non-dominated sorting was introduced to find the optimal Pareto front. The roll tracking optimization strategy was designed, and the dynamic step size updated the ball roller to increase the global exploration ability and optimization accuracy of DBO. Finally, an adaptive internal division mechanism was designed to update the proportion of rolling balls and dung beetles, which further improved the convergence of the algorithm. Simulation experiments are conducted on typical 24-hour daily load data provided by IEEE-RTS. The results show that compared with the five comparison algorithms, the proposed NSIDBO method has better comprehensive performance and plays a guiding role in realizing safe and stable control operation of the microgrid. 

含有清洁能源的微网电力资源网内分配需要协调优化经济成本与低碳节能, 而现有的多目标蜣螂优化算法寻优能力不足。针对这一问题, 提出一种基于非支配排序的改进多目标蜣螂算法优化的微电网调度方法NSIDBO。1) 构建含风光柴燃蓄的微电网系统及各单元自身约束模型, 建立基于经济与环保的多目标代价函数; 2) 设计基于扰动因子的Tent 映射, 在此基础上增加3个参数, 增大映射分布范围, 提高初始化种群多样性; 3) 引入新型非支配排序, 找到最优pareto前沿; 4) 设计一种翻滚跟踪优化策略, 以动态步长更新“滚球者”, 增加DBO的全局勘探能力和寻优精度; 5) 设计一种自适应种群内部划分机制, 更新“滚球”和“偷窃蜣螂”的比重, 进一步提升了算法收敛性。选取IEEE-RTS提供的典型日24小时负荷数据进行仿真实验, 结果表明, 所提NSIDBO算法优化含清洁能源微电网调度规划得到的解, 比5种对比算法的综合性能更优, 可以实现微电网的安全与稳定控制。