基于改进多目标粒子群算法的配电网储能选址定容

发布时间：2018-08-05 10:22

【摘要】：以系统节点电压波动、负荷波动以及储能系统总容量为目标建立了储能选址定容优化模型。求解过程中提出了一种改进多目标粒子群算法(improved multi-objective particle swarm optimizer,IMOPSO)。该算法根据粒子与种群最优粒子的距离来指导惯性权重的取值,使得各粒子的惯性权重可以自适应调整,并在二者距离较小时引入交叉变异操作,避免陷入局部最优解,同时采用动态密集距离排序来更新非劣解集并指导种群全局最优解的选取,在保持解集规模的同时使解的分布更均匀。为避免决策者偏好对最终结果的影响,采用基于信息熵的序数偏好法从最优Pareto解集中选取储能的最优接入方案。以IEEE-33节点配电系统为例进行仿真验证,结果表明该方法在储能选址定容问题求解中具有很好的收敛性以及全局搜索能力。
[Abstract]:Taking the voltage fluctuation, load fluctuation and the total capacity of the energy storage system as the targets, the optimal model of energy storage location is established. An improved multi-objective particle swarm optimization algorithm (improved multi-objective particle swarm optimizer) is proposed. According to the distance between particles and population optimal particles, the algorithm can guide the value of inertia weight, so that the inertia weight of each particle can be adjusted adaptively, and cross mutation operation is introduced when the distance between them is small, so as to avoid falling into the local optimal solution. At the same time, the dynamic dense distance ordering is used to update the non-inferior solution set and to guide the selection of the global optimal solution of the population, so that the solution distribution is more uniform while maintaining the size of the solution set. In order to avoid the influence of decision makers' preference on the final result, the ordinal preference method based on information entropy is used to select the optimal access scheme of energy storage from the optimal Pareto solution set. The simulation results of IEEE-33 node distribution system show that the proposed method has good convergence and global searching ability in the solution of energy storage location and capacity problem.
【作者单位】：华北电力大学电气与电子工程学院;中国电力科学研究院;
【基金】：国家863高技术基金项目(2014AA052004)~~
【分类号】：TM91

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