Distribution network cluster division strategy for active power flow optimization problem

Abstract

Cluster division can effectively solve the problem of massive data analysis and a large number of equipment regulation caused by large-scale access of new energy to the distribution network. However, existing research on cluster partitioning algorithms exhibits low accuracy and may yield unreasonable outcomes. In order to solve the above problems, factors that should be considered in the cluster division strategy of distribution network are described when a large number of distributed power sources are connected, and the scale limit index is designed accordingly. By studying the process of genetic algorithm, the reason why genetic algorithm shows poor global optimization ability is found out, and then the algorithm is enhanced. Simulation results demonstrate that the proposed scale limit index successfully avoids unreasonable partitioning outcomes. The proposed improved genetic algorithm greatly improves the accuracy of the algorithm. Because the genetic algorithm has no convergence criterion, the reduction of the number of iterations can not directly reduce the experiment time. In summary, the research effectively improve the accuracy of genetic algorithm and enhance the efficiency of cluster division in distributed network.

Keywords

• new energy
• active distribution network
• cluster division
• scale limit index
• power flow optimization
• genetic algorithm