An improved decomposition based multi-objective evolutionary algorithm for the operation management of a renewable micro-grid

This study presents a multiobjective energy management to optimize the renewable microgrid operation while satisfying a demand response and various operation constraints. With regard to energy cost minimization, pollutant emission reduction for better utilization of the renewable energy resources, such as wind and solar, as a competitive objective is proposed. Moreover, for maximizing the renewable microgrid operators, demand response benefits satisfying the load demand constraints amongst other operation constraints are incorporated into the operations of the renewable microgrid. The overall problem is formulated as a mixed-integer nonlinear constraint multiobjective optimization problem with different equality and inequality constraints. In this paper, an improved decomposition based multiobjective evolutionary algorithm is presented for optimal operation of the renewable microgrid with renewable energy sources and various devices such as diesel generators, micro-turbines, fuel cells, and battery energy storage. To improve the optimization process, differential evolution (DE) and the niche guided mating selection strategy are incorporated. Meanwhile, decomposition-based multiobjective evolutionary algorithm-DE is extended to tackle the constrained optimization problem. Finally, the proposed algorithm is applied in a renewable microgrid, and its superior performance is compared with the conventional evolutionary algorithms such as the multiobjective genetic algorithm and the original decomposition based multiobjective evolutionary algorithm. Published under license by AIP Publishing.