Multi-Source Energy Mixing for Renewable Energy Microgrids by Particle Swarm Optimization

Distributed intelligence is one of the prominent prospects of future smart grids besides distributed generation, distributed storage and demand side load management. This study illustrates utilization of particle swarm optimization (PSO) method for cost-efficient energy management in multi-source renewable energy microgrids. PSO algorithm is used to find out optimal energy mixing rates that can minimize daily energy cost of a renewable microgrids under energy balance and antiislanding constraints. The optimal energy mixing rates can be used by multi-pulse width modulation (M-PWM) energy mixer units. In our numerical analyses, we consider a multi-source renewable energy grid scenario that includes solar energy system, wind energy system, battery system and utility grid connection. We assume that variable energy pricing is used in utility grid to control energy dispatches between microgrids. This numerical analysis shows that the proposed scheme can adjust energy mixing rates for M-PWM energy mixers to achieve the cost-efficient and energy balanced management of microgrid under varying generation, demand and price conditions. The proposed method illustrates an implementation of distributed intelligence in smart grids.