Multi‐objective long‐term reconfiguration of autonomous microgrids through controlled mutation differential evolution algorithm

Abstract

This study provides a solution for the feeder reconfiguration of autonomous microgrids (MGs). The objective is to minimise power loss, switching costs, and enhance voltage stability index, considering time-variations of loads. The daily load profiles for different seasons (spring, summer, fall, and winter) of different customers (i.e. residential, industrial, and commercial) are considered. In order to reduce the dimensions of the optimisation problem, k-means algorithm is implemented that clusters seasonal/yearly load profile into a few groups. The daily load profile is obtained based on the average of the group which has maximum members. This ensures the selection of a subset of load profiles that effectively represent the entire year's profile to reduce the complexity and execution time of the model. Subsequently, a new method is developed to break the daily load profile into intervals that guarantee less switching frequency for dynamic network reconfiguration. A controlled mutation differential evolution algorithm (CMDEA) compatible with long-term reconfiguration problem is developed with superior performance compared to conventional DEA and invasive weed optimisation algorithm. The CMDEA is employed to solve the reconfiguration problem on 33-bus and 69-bus autonomous MGs. Simulation results validate the effectiveness of the proposed method to reduce operational costs and computational burden in a smart grid environment.