Multi-Objective Optimization for Coordinated Day-Ahead Scheduling Problem of Integrated Electricity-Natural Gas System with Microgrid

Abstract

This paper presents a multi-objective optimization algorithm for coordinated day-ahead scheduling problem of integrated electricity-natural gas system with microgrid (IENGS-M). Mathematically, the day-ahead scheduling of IENGS-M is formulated as a multi-objective optimization problem considering multitudinous constraints. In order to solve the problem efficiently, we introduce an acceleration of differential evolution, Lévy search strategy and a treatment mechanism to multitudinous and complex constraints into the original Non-dominated Sorting Genetic Algorithm-III (NSGA-III). Furthermore, a decision making method based on a fuzzy function approach is used to determine a final optimal solution from the Pareto-optimal solutions. Simulation studies are carried out on a modified IEEE 39-bus system and 15-node gas system to verify the effectiveness of the modified NSGA-III (MNSGA-III), in comparisons with the NSGA-II and NSGA-III. The simulation results show that the Pareto-optimal solutions obtained by MNSGA-III has better convergence performance and diversity than the NSGA-II and NSGA-III.