Multi-Objective Optimization of Distribution Networks via Daily Reconfiguration

This paper presents a comprehensive approach to improve the daily performance of an active distribution network (ADN), which includes renewable resources and responsive loads (RLs), using distribution network reconfiguration (DNR). The optimization objectives considered in this work can be described as (i) reducing active losses, (ii) improving the voltage profile, (iii) improving the network reliability, and (iv) minimizing the operation costs. The proposed approach also accounts for the probability of renewable resource failure given the information collected from their initial state at the beginning of each day. Furthermore, solar radiation variations are estimated based on past historical data, and the impact of the performance of renewable resources such as photovoltaics (PVs) is determined hourly based on a Markov model. Since the number of reconfiguration scenarios is very large, stochastic DNR (SDNR) based on the probability distance method is employed to shrink the scenarios set, before a self-adaptive modified crow search algorithm (SAMCSA) is introduced to find an optimal scenario. Finally, the IEEE 33-bus radial distribution system and the 86-bus Taiwan Power Company (TPC) system are investigated as two case studies to verify the effectiveness of the proposed method.