Cost-Optimal Scheduling in Electric Vehicle-Enabled Microgrids Using an Improved Artificial Bee Colony Algorithm

This paper proposes a cost-optimal operation framework for grid-connected AC microgrids with integrated electric vehicles (EVs), aiming to minimize total operational costs through intelligent scheduling of distributed energy resources. The proposed system comprises solar photovoltaic (PV) generation, diesel generators (DGs), battery energy storage systems (BESS), and multiple EVs with controlled charging. A constrained optimization model is formulated, incorporating power balance, generator and storage constraints, grid exchange limits, and EV-specific charging schedules. To solve this complex scheduling problem, an improved artificial bee colony (ABC) algorithm is developed, integrating chaotic initialization and enhanced neighborhood search to accelerate convergence and improve solution quality. Comparative simulations are conducted against standard ABC, genetic algorithm (GA), particle swarm optimization (PSO), and non-dominated sorting genetic algorithm II (NSGA-II) under identical microgrid configurations. Results show that the improved ABC algorithm achieves the lowest total cost of 148.07 (USD/day), reducing costs by up to 9.66% compared to GA, while also demonstrating faster convergence. The inclusion and coordination of EVs as flexible energy assets significantly enhance operational efficiency, reduce diesel reliance, and promote better utilization of renewable energy. This research highlights the superior performance of the improved ABC algorithm and the transformative role of EVs in cost-effective and sustainable microgrid operation.