Multi-objective day-ahead energy management of a microgrid considering responsive loads and uncertainty of the electric vehicles

Abstract Currently, the demand side management has found an important role in the effective operation of the distribution network and microgrids. This paper investigates the impact of the stochastic behavior of electric vehicles (arrival and departure time and state of the charge) and responsive loads as tools of demand side managementin the efficient operation of the grid-connected microgrid based on combined cooling, heating and power systems. It proposes a multi-objective model for the feeder reconfiguration, economic dispatching, and capacitor switching with considering electric vehicles and responsive loads.The proposed model takes the operational costs, active power losses, voltage stability index, and greenhouse gas emissions as objective functions. The combined cooling, heating and power system based microgrid has been equipped with non-dispatchable distributed generations (wind turbine and photovoltaic cells) as well as electrical and thermal energy storage systems. For an accurate modeling, the stochastic behavior of the non-dispatchable generations, thermal and electrical demands, and electric vehicles are considered. Multi objective hybrid big bang big crunch algorithm along with a fuzzy scaling and max geometric mean operator are applied to achieve the optimal solutions. The simulation results show that considering the electric vehicles and responsive loads on the energy management system can decrease the operation cost and emissions by 18.12% and 4.91%, respectively.