Many-Objective Distribution Network Reconfiguration Via Deep Reinforcement Learning Assisted Optimization Algorithm

With the increasing penetration of renewable energy (RE), the operations of distribution network are threatened and some issues may appear, i.e., large voltage deviation, deterioration of statistic voltage stability, high power loss, etc. In turn, RE accommodation would be significantly impacted. Therefore, we propose a many-objective distribution network reconfiguration (MDNR) model, with the consideration of RE curtailment, voltage deviation, power loss, statistic voltage stability, and generation cost. This aims to assess the trade-off among these objectives for better operations of distribution networks. As the proposed model is a non-convex, non-linear, many-objective optimization problem, it is difficult to be solved. We further propose a deep reinforcement learning (DRL) assisted multi-objective bacterial foraging optimization (DRL-MBFO) algorithm. This algorithm combines the advantages of DRL and MBFO, and is targeted to find the Pareto front of proposed MDNR model with better searching efficiency. Finally, we conduct case study on the modified IEEE 33-bus, 69-bus, and 118-bus power distribution systems, and results verify the effectiveness of the MDNR model and outperformance of the proposed DRL-MBFO.

Automated summary

The article introduces a many-objective distribution network reconfiguration (MDNR) model and a deep reinforcement learning assisted multi-objective bacterial foraging optimization (DRL-MBFO) algorithm. Through case studies on modified power distribution systems, the effectiveness of the MDNR model and the superiority of the proposed DRL-MBFO are verified.