Optimizing Environmental and Economic Performance of Grid-Connected PV Charging Stations with Battery Energy Storage Systems (BESS) and an Advanced Energy Management System (EMS)

Abstract

Battery energy storage systems (BESS) with an energy management system (EMS) were suggested in this research that consists of a grid-connected photovoltaic (PV) charging station (CS) equipped with battery energy storage. The primary aim of this energy management system was to regulate the amount of energy sent to the electric vehicle (EV) while taking into account the cost and carbon dioxide emissions caused in grid connection. Consequently, our research offered a two-stage optimization process with multiple objectives to lessen the financial and ecological footprint of the charging station. An energy schedule was generated in the first optimization stage by considering factor of grid CO2 emissions on an hourly basis, the PV forecast, the power cost with the BESS initial state of charge (SoC). The maximum power that the grid was allowed to deliver the EV was the same as the output from this first stage. The second optimization step employed, model predictive control (MPC) to regulate the flow of energy among BESS, the PV, and the grid. An operational Photo Voltaic/BESS charging station used to validate the proposed EMS. The new EMS was then used to evaluate the charging station’s efficiency in this research for one month of data, taking three main aspects into consideration: environmental, economic, and energy. The optimization results suggest that the new energy profile ensures a 36% drop in emissions and a 33% drop in energy cost.

Keywords

• energy management
• model predictive control
• optimization
• electrical vehicle
• photovoltaic