A genetic algorithm optimization approach for smart energy management of microgrid

Optimal management and planning of microgrids (MG) are the most important goals for operators. In this study, a Multiobjective Genetic Algorithm (MOGA) is applied to the technical and economic problems of the MG. This stochastic programming considers demand response (DR) programs, reactive loads, and uncertainties due to renewable energies. Demand-side management (DSM) is how to manage and schedule the generation and con-sumption with the objective of cost and greenhouse gases (GHG) emissions minimization. In this work, with the contribution of various customers to demand response programs and reserve schedules, a reduction in the operation cost of the microgrid has resulted. This method facilitates obtaining a complete and comprehensive microgrid model for energy management in the power system, and the results demonstrate that participation in demand response programs and reactive loads can reduce generation, reservation, startup costs, and the amount of pollution. Regarding reservation costs, a 16% reduction was obtained in the presence of the load response, and wind power is a good compromise between cost and pollution among various resources.

Automated summary

This study applies a multi-objective genetic algorithm to optimize the technical and economic problems of microgrids, taking into account demand response programs, reactive loads, and uncertainties of renewable energies. The results demonstrate that participation in demand response programs and reactive load management can reduce operating costs and pollution.