Improved antlion sizing optimization for vehicle-to-grid considering rule-based energy management schemes

Abstract

Renewable Energy Sources (RESs) integration with Electric Vehicles (EVs) and microgrids has become a popular system for providing an economic and green environment. In order to address power challenges, RESs such as solar and wind are exploited and integrated into a microgrid. EVs play a key role in reducing emissions and energy saving due to their free carbon nature, reducing fuel consumption, and can be used as storage or load. Tripoli-Libya (latitude 32.8872° N and longitude 13.1913° E) located in Northern Africa is one of the oils and natural gas producers that has been selected as the study area. However, the country is bedeviled with electric power problems. Microgrids are faced with planning issues, challenges associated with designing a proper model system, as well as stability which results in low power quality. The issue can be addressed by using metaheuristic algorithms combined with Energy Management Strategy (EMS). However, the conventional metaheuristic algorithms face premature convergence and acquire local optima quickly which needs to be improved. Thus, choosing suitable sizing metaheuristic algorithms is recommended to find the global optimum. Therefore, Improved Antlion Optimization (IALO) coupled with the Rule-Based Energy Management Strategy (RB-EMS) is proposed. An RB-EMS is used to control and monitor the flow of energy in the system using simple mathematical equations. Furthermore, in the literature review, rule-based is recommended due to the decision-making and providing the appropriate result. This study examines a grid-connected system aimed at addressing the current power challenges by integrating RESs into Electric Vehicle Charging Facility (EVCF) using Vehicle-to-Grid (V2G) technology. An objective function for the proposed grid-connected system mainly depends on measuring the per unit of generated electricity as Cost of Energy (COE), and reduction in Losses Power Supply Probability (LPSP) as means of stabilizing the system and maximizing the Renewable Energy Fraction (REF). Mathematical modeling for the Photovoltaic (PV), Wind Turbine (WT), EV, inverter, and Battery (BT) as the microgrid components for the case study (Tripoli-Libya) is adopted. The acquired result has been validated with other algorithms Antlion Optimization (ALO), Particle Swarm Optimization (PSO), and Cuckoo Search Algorithm (CSA). The obtained simulation result indicates that the proposed method IALO contributed lower COE ($0.0936 /kWh), and high REF (99.40%) as compared to the counterpart algorithms. The IALO coupled with RB-EMS fills the gap in sizing and planning a cost-effective system to address the sizing limitations. The results affirm the low-cost nature of the proposed model of a grid-connected microgrid system using V2G technology. A further economic assessment is made using the Stochastic Monte Carlo Method (SMCM) used to estimate the load impact by integrating various numbers of EVs and the payback period. Sensitivity analysis was utilized to demonstrate the impact performance of the proposed components under various scenarios.