Energy management in microgrid integrated with ultracapacitor-equipped electric vehicles and renewable resources using Hybrid Algorithm Perspective

A novel optimization method, based on the Big Bang-Big Crunch hybrid algorithm, is employed to manage the timing and integration of EVs into the microgrid, aiming to minimize operational costs. The algorithm efficiently controls the energy flow between the EV batteries and the control system, dynamically adjusting to meet demand while reducing costs. The system was tested on the IEEE 14-bus system with real EV movement patterns over a 24-hour period. The results show a significant cost reduction, with the total cost decreasing from $132,869.9 without EVs to $112,981 when EVs equipped with ultracapacitor batteries are integrated, representing a 15% reduction. Moreover, the proposed method outperforms other algorithms, such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). Compared to these methods, the Big Bang-Big Crunch hybrid algorithm achieves cost reductions of 17% over PSO and 13% over GA, demonstrating its effectiveness in optimizing energy management in a microgrid with renewable energy sources.