Economic-Environmental Scheduling of Community Microgrid using Evolutionary Algorithm

Abstract

The community microgrid (CMG), a local energy network, is considered to be an essential part of the future electric grid. Due to the presence of complex operational constraints, scheduling of CMG energy resources is a challenging optimization problem. Most of the related works mainly focused on the economic schedule, such as scheduling at minimum operating cost. In this work, we also consider the environmental impact, such as minimizing the pollutant emission. The scheduling problem is formulated as a bi-objective optimization problem with minimization of pollutant emission from the generators as well as operating cost. Single-objective economic scheduling is also generated to compare the results with the bi-objective approach. The proposed solution approach is based on a differential evolution algorithm that is integrated with a heuristic. The proposed heuristic is basically a repairing mechanism that can produce feasible solutions quickly; thus, enhancing the algorithm performance. The performance of the heuristic-based algorithm is compared with that of without heuristic. The simulation results validate the effectiveness of the proposed algorithm.