A Hazelnut Tree Search Algorithm for Enhancing Large-Scale Electric Vehicle Charging on the Power Grid

Electric vehicle (EV) adoption on a large scale would drastically change patterns of electricity demand, posing both opportunities and challenges for modern power systems. However, the increased and often unpredictable demand from simultaneous EV charging can strain grid infrastructure, leading to potential overloads and instability. This paper proposes the Hazelnut Tree Search Algorithm (HTSA) as an effective optimization method for large-scale EV charging. The objective of the proposed method is to minimize electricity costs and improve grid efficiency in the EV charging. By optimizing charging schedules, HTSA enhances grid stability and improves load balancing throughout the power grid (PG). By then, the proposed method is implemented in MATLAB platform and compared to other existing methods such as Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and NSGA-II. Results show that HTSA reduces electricity costs to 4.3 INR/kWh, and achieves 96% of higher efficiency compared to other existing methods. This superior performance demonstrates HTSA’s potential for optimizing EV charging strategies while minimizing grid disruptions and enhancing the integration of renewable energy sources (RES).