MICRO-GRID EMC INCLUDING EV LOAD IN A RESIDENTIAL AREA

This paper addresses the problem of Micro-Grid (MG) Energy Management Control (EMC) including Electric Vehicle (EV) scheduling with considering a reduction in the overall operating cost of MG in a residential grid. The main motivation for this study is the impact of the daily load profile combined with electric vehicles (EVs) on the grid. Unless the EV integration with load is monitored and controlled, the MG may experience an unexpectedly high or low load. So, EMS is a trend in recent years for optimal planning of MG. On the other hand, the available energy stored in the energy storage Battery can be utilized to free the distribution system from some of the congested load at certain times or to allow the grid to charge more EVs at any time of the day, including peak hours. This work was implemented by using four metaheuristic algorithms (Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Hybrid population-based algorithm (PSOGSA), and Capuchin Search Algorithm (CapSA) for optimal operation with minimum total daily cost without and with EVs included in MG by two different daily profile of EV. The MG used in this paper consisted of a diesel generator (DG), Battery storage device, photovoltaic (PV) system, and Wind turbine unit (WT). For a more dispatchable practical MG, Emissions from DG and deterioration of storage devices in addition to the cost of charging the EVs have been taken into account. The results demonstrate that CapSA is a suitable method for generating robust models for EMS. This means that the proposed CapSA approach can be applied in a wide range of complex nonlinear systems.