An MINLP optimization method to solve the RES-hybrid system economic dispatch of an electric vehicle charging station

Abstract

Power systems’ increased running costs and overuse of fossil fuels have resulted in continuing energy scarcity and momentous energy gap challenges worldwide. Renewable energy sources can meet exponential energy growth, lower reliance on fossil fuels, and mitigate global warming. An MINLP optimization method to solve the RES-hybrid system economic dispatch of electric vehicle charging stations is proposed in this paper. This technique bridges the gap between theoretical models and real-world implementation by balancing technical optimization with practical deployment constraints, making a timely and meaningful contribution. These contributions extend the practical application of MINLP in modern grid operations by aligning optimization outputs with the stochastic character of renewable energy, which is still a gap in the existing literature. The proposed economic dispatch simulation results over 24 h at an hourly resolution show that all generation units contributed proportionately to meeting EVCS demand: solar PV (51.29%), ESS (13.5%), grid (29.92%), and wind generator (8.29%). The RES-hybrid energy management systems at charging stations are designed to make the best use of solar PV power during the EVCS charging cycle. The supply–demand load profile problem dynamic in EVCS are designed to reduce reliance on grid electricity supplies while increasing renewable energy usage and reducing carbon impact.