Fuzzy Adaptive Granulation Multi-Objective Multi-microgrid Energy Management

This paper develops an energy management approach for a multi-microgrid (mmg) taking into account multiple objectives involving plug-in electric vehicle (pev), photovoltaic (pv) power, and a distribution static compensator (dstatcom) to improve power provision sharing. in the proposed approach, there is a pool of fuzzy microgrids granules that compete with each other to prolong their lives while monitored and evaluated by the specific fuzzy sets. in addition, based on the hourly reconfiguration of microgrids (mgs), granules learn to dispatch cost-effective resources. in order to promote an interactive service, a well-defined multi-objective approach is derived from fuzzy granulation analysis to improve power quality in mmgs. a combination of the meta-heuristic approach of genetic algorithm (ga) and particle swarm optimization (pso) eliminates the computational difficulty of the non-linearity and uncertainty analysis of the system and improves the precision of the results. the proposed approach is successfully applied to a 69-bus mmg test with the results reported in terms of the stored energy improvement, daily voltage profile improvement, mmg operations, and cost reduction.