Azadikhouy Masoumeh
Department of Mathematics, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
Sci Rep. 2025 Jul 13;15(1):25320. doi: 10.1038/s41598-025-10849-7.
The rapid integration of electric vehicles (EVs) into distribution grids introduces significant challenges, including heightened energy losses, voltage instability, and increased operational costs. Traditional optimization methods often address these issues in isolation, failing to balance the complex, multi-objective nature of modern grids with high EV penetration and renewable variability. This paper proposes a mixed-integer multi-objective optimization framework to simultaneously minimize operational costs, energy losses, load shedding, and voltage deviations over a 24-hour horizon. The model integrates EV charging/discharging dynamics, renewable energy management, demand-side flexibility, and coordinated control of grid devices such as On-Load Tap Changers (OLTC) and Static Voltage Regulators (SVR). A novel Multi-Objective Moth Swarm Algorithm (MOMSA) is introduced to efficiently navigate the non-convex solution space, leveraging moth-inspired exploration-exploitation mechanisms. Simulations on a 33-bus distribution network demonstrate MOMSA's superiority over conventional algorithms (e.g., HOA, PSO, GA), achieving a 19.2% cost reduction compared to non-EV-integrated scenarios and outperforming peers by 7.4-15.7% in total cost, energy loss reduction, and voltage stability. Sensitivity analyses under varying electricity prices, renewable intermittency, and uncoordinated EV charging validate the model's robustness, highlighting its adaptability to real-world uncertainties. The results underscore MOMSA's capability to enhance grid reliability, economic efficiency, and sustainability in EV-rich environments, addressing critical gaps in existing literature through comprehensive multi-objective coordination and scalable optimization.
电动汽车（EV）快速接入配电网带来了重大挑战，包括能量损耗增加、电压不稳定以及运营成本上升。传统的优化方法往往孤立地解决这些问题，无法在高电动汽车渗透率和可再生能源波动性的情况下平衡现代电网复杂的多目标特性。本文提出了一种混合整数多目标优化框架，以在24小时内同时最小化运营成本、能量损耗、负荷削减和电压偏差。该模型整合了电动汽车充电/放电动态、可再生能源管理、需求侧灵活性以及诸如有载分接开关（OLTC）和静态电压调节器（SVR）等电网设备的协调控制。引入了一种新颖的多目标蛾群算法（MOMSA），利用受蛾启发的探索-利用机制有效地在非凸解空间中寻优。在一个33节点配电网的仿真结果表明，MOMSA优于传统算法（如HOA、PSO、GA），与未接入电动汽车的场景相比，成本降低了19.2%，在总成本、能量损耗降低和电压稳定性方面比同类算法性能优7.4 - 15.7%。在不同电价、可再生能源间歇性和电动汽车无序充电情况下的敏感性分析验证了该模型的鲁棒性，突出了其对现实世界不确定性的适应性。结果强调了MOMSA在电动汽车密集环境中增强电网可靠性、经济效率和可持续性的能力，通过全面的多目标协调和可扩展优化解决了现有文献中的关键空白。
