摘要
分布式风电源(distributed wind generation,DWG)和负荷出力的时序性和不确定性,将造成停电损失功率的时序性和不确定性,进而影响配电网供电可靠性。本文基于Monte?Carlo随机模拟,将考虑时序性和不确定性的停电损失可靠性指标引入模型。建立综合考虑最小化DWG投资成本、最小化网损以及最小化停电损失的多目标优化配置模型。为了提高算法的收敛性能,采用改进多目标粒子群优化算法求解。该算法运用小生境技术进行多目标全局寻优,同时利用NSGA-II非支配排序策略和拥挤度距离选择最优粒子,引导粒子跳出局部最优。针对已获得的Pareto解,采用最短归一化距离的方法选择最终方案。最后,以IEEE 33节点配电网为例进行仿真实验,结果验证了所提模型和算法的可行性和有效性。
The timing and uncertainty of distributed wind generation(DWG) and load output will result in the timing and uncertainty of outage power, which in turn will affect the power supply reliability of the distribution network. In this paper, the power outage loss index of the timing and uncertainty is introduced in the model based on Monte-Carlo stochastic simulation. A multi-objective optimal allocation model of DWG is established with consideration of minimum investment cost of DWG, minimum network loss and minimum power outage. For improving the convergence performance of the algorithm, an improved multi-objective particle swarm optimization algorithm is used for the calculation. In this algorithm, the multi-objective global optimization is performed by the niche technology and,at the same time,non-dominated sorting strategy and crowding distance is used to select optimal particles, which lead the particles to jump out of local optimum. As for the obtained Pareto solution, the final solution is selected with the shortest normalized distance method. Finally, the simulation experiment is carried out with IEEE 33 node distribution network, and the feasibility and effectiveness of the proposed model and algorithm are verified.
引文
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