Optimizing EV Energy Management Using Monarch Butterfly and Quantum Genetic Algorithms

In the evolving energy landscape, the integration of electric vehicles (EVs) presents both challenges and opportunities for efficient ‎energy management. This study introduces a hybrid optimization framework that combines Monarch Butterfly Optimization (MBO) ‎and Quantum Genetic Algorithm (QGA) to address the multi-objective problem of EV charging/discharging scheduling and energy ‎storage management in the context of dynamic market transactions. By leveraging the complementary strengths of MBO and QGA, ‎the proposed method delivers robust and adaptive solutions to complex optimization scenarios. The framework aims to minimize ‎charging costs, reduce peak electricity demand, and enhance grid stability, while accounting for the stochastic nature of EV usage ‎patterns and fluctuating energy prices. Extensive simulations and comparative analyses demonstrate the effectiveness of the ‎approach in diverse market environments. The results underscore the potential of integrating nature-inspired and quantum-inspired ‎algorithms for smart grid applications, offering a promising pathway for sustainable and intelligent EV energy management. The ‎integration of MBO and QGA improves the optimization process by using the inherent merits of both algorithms, allowing for ‎robust solutions to complicated optimization issues‎.