A rule-based power management strategy for Vehicle-to-Grid system using antlion sizing optimization

Stable and reliable electricity is a prerequisite to the development of any nation and improving the living standard of the society. By exploiting Renewable Energy Sources (RESs) such as solar energy (PV) and wind energy (WT) as widely acknowledged RESs to be more environmentally friendly and address power difficulties. They are acknowledged to be an interesting option to charge Electric Vehicles (EVs) via Vehicle-to-Grid (V2G) technology. On the contrary, to the best knowledge of the authors, there are few numbers published articles on RESs (PV and WT) integrated with Electric Vehicle Charging Station (EVCS) to charge EVs. This research is aimed at implementing a metaheuristic optimization algorithm named Antlion Optimizer (ALO) algorithm for sizing the system components to meet the load demand and minimize the two objective functions - Losses Power Supply Probability (LPSP) and Cost of Electricity (COE) while maximizing the Renewable Energy Fraction (REF). In addition, the Rule-Based Energy Management Strategy (RB-EMS) is proposed to control the flow of power in the system. The integration of RESs to the utility grid with V2G technology and V2G services is discussed. Furthermore, the State-of-the-art for improving the stability and reliability of the grid-connected system is presented. As a result, the attained result from the proposed method shows better performance results as 0.0936 $/kWh, 0.1044%, and 0.9940% for COE, LPSP, and REF, respectively. The research is conducted in different microgrid configurations using a MATLAB environment.

Automated summary

This research utilizes the Antlion Optimizer algorithm to optimize system components, meet load demand, minimize costs, and maximize renewable energy fraction. By proposing a rule-based energy management strategy to control power flow, integrating RESs with V2G technology into the grid, and enhancing the stability of the power system.