Sequential Optimization of EV Charging Scheduling with Photovoltaic and Battery Integration Using Quadratic Programming

This study develops a sequential quadratic programming algorithm for managing electric vehicle (EV) charging power in a charging station powered by a photovoltaic (PV) system, the power grid, and battery energy storage. The proposed algorithm maximizes PV energy utilization and minimizes operation costs while respecting EV charging demands and operational constraints. The methodology employs a sequential approach, in which the EV charging schedule is performed upon each EV’s arrival. Optimization balances the contribution of PV, stored, and grid energy for each EV to reduce costs and improve the use of renewable energy sources. A cost function is introduced, comprising a linear component to minimize operational costs and a quadratic component to maximize energy utilization from PV by penalizing deviations between EV charging power and available PV power. The case study incorporates real-world data, including meteorological information, dynamic energy tariffs, and historical EV charging patterns. It uses a homogeneous Poisson process to model the arrival of electric vehicles each day, and the study is conducted for a year. The simulation results demonstrate that the proposed algorithm effectively increases PV energy utilization and significantly reduces operational costs, validating its performance under realistic conditions.