Social group entropy optimization for day-ahead heat and power scheduling of an isolated microgrid

Abstract

This article presents day-in-advance microgrid heat and electric power scheduling with and without demand-side management. The objective is to minimize the cost of heat and electric power generation, considering solar and wind power uncertainty. This scheduling problem is solved using a social group entropy optimization (SGEO) technique. The proposed microgrid comprises diesel generators, biomass-fuel-fired combined heat and power (BCHP) units, mini-hydro power plants, solar photovoltaic plants, wind turbine generators (WTGs), a hydrogen storage system (HSS) and plug-in electric vehicles (PEVs). A system with three diesel generators, three BCHP units, one mini-hydro power plant, two WTGs, two solar photovoltaic plants, one HSS and PEVs is studied to evaluate the suggested SGEO technique. Test results of the proposed SGEO technique are compared with those from self-organizing hierarchical particle swarm optimization with time-varying acceleration coefficients, fast convergence evolutionary programming and differential evolution, and the recommended SGEO is shown to produce superior results.