Optimal planning and design of a microgrid with integration of energy storage and electric vehicles considering cost savings and emissions reduction

The focus of this study is on the concurrent coordination of electric vehicles and responsive loads in a microgrid setting, with the aim of minimizing operational costs and emissions while considering the variability of wind and photovoltaic power sources. The proposed approach employs electric vehicles for peak shaving and load curve adjustment. Additionally, responsive loads are utilized to provide the necessary resources to accommodate the inherent instabilities of wind and photovoltaic outputs. Moreover, a highly developed two-phase framework is provided for ascertaining the anticipated operational costs of a microgrid, encompassing both energy and reserve costs. The first phase attempts to minimize the costs associated with the production and retention of backup power, whereas the second phase focuses on decreasing the costs of adjusting unit schedules in reaction to variations in wind and photovoltaic energy generation. This research delivers an objective optimization problem, which is subsequently addressed through the utilization of the modified sparrow search algorithm (MSSA), a reliable and efficient optimization technique. The hourly findings obtained from a 24-hour study of an MG model demonstrate the superior performance of the MSSA algorithm relative to other established methodologies. The model under consideration has been executed within a microgrid that incorporates diverse distributed generations. The findings of the simulation indicate that the integration of electric vehicles and responsive loads results in a reduction of operational costs and emissions within the system. Additionally, the uncertainties associated with wind and photovoltaic sources are mitigated. The results obtained from the simulation demonstrate that the proposed MSSA algorithm outperforms other established optimization algorithms.