Multi-objective non-weighted optimization to explore new efficient design of electrical microgrids

• Development of a decision-support tool for innovative electrical microgrid design. Microgrid design with technological and management parameters. Physical modelling conversion storage technologies sequential simulation. Multi-objective non-weighted optimization NSGA-II genetic algorithm. Variety compromises between economic, technical environmental indicators. Centralized networks induce dependency local territories their power supply. However, thanks to microgrids, can increase decision-making autonomy network that matches values. Technological choices are critical minimize microgrids negative impacts on environment. Influence the latter space is rarely discussed whereas extending would help find microgrids. The purpose this paper several various performances parameters objectives. solutions’ variety therefore extends decision-makers’ space. A has been developed answer goal. Design both physical implemented in simulation operation. performance allows use algorithms perform multi-objective optimizations. Two two-objective optimizations performed. Results show how diversity terms helps user choosing Especially, it underlines potential approach close but different