Samiei Moghaddam Mahmoud, Azadikhouy Masoumeh, Salehi Nasrin, Hosseina Majid
Department of Electrical Engineering, Damghan Branch, Islamic Azad University, Damghan, Iran.
PhD of Shiraz university Department of Mathematics, Shiraz, Iran.
Sci Rep. 2025 Apr 17;15(1):13324. doi: 10.1038/s41598-025-97271-1.
Electric vehicle (EV) integration into distribution grids introduces significant challenges in maintaining grid stability, minimizing operational costs, and ensuring overall system efficiency. In response to these challenges, a novel multi-objective optimization model is proposed that concurrently minimizes energy losses, energy procurement costs, load shedding, and voltage deviations over a 24-hour period, while also accounting for the operational costs associated with EV and battery management. The model is optimized using the Hiking Optimization Algorithm (HOA), which leverages an adaptive search mechanism based on Tobler's Hiking Function. This mechanism enhances the exploration of the solution space and effectively avoids local optima, resulting in superior performance compared to conventional methods. Simulation results on a 33-bus distribution grid demonstrated that, with EV integration, operational costs were reduced by 19.3%, energy losses decreased by 59.7%, load shedding was minimized by 75.4%, and voltage deviations improved by 43.5% relative to a scenario without EVs. Additionally, the model eliminated photovoltaic (PV) curtailment, thereby ensuring optimal utilization of renewable energy resources. When benchmarked against alternative optimization techniques, the HOA achieved a 4.4% lower total cost than the Komodo Mlipir Algorithm (KMA) and reduced energy losses by 24.5% compared to Particle Swarm Optimization (PSO). These results clearly demonstrate the model's effectiveness in enhancing grid stability, optimizing costs, and improving operational efficiency. The proposed approach offers a scalable and reliable solution for modern grid management in the context of increasing EV penetration and renewable energy integration.
电动汽车（EV）接入配电网给维持电网稳定性、降低运营成本以及确保整体系统效率带来了重大挑战。针对这些挑战，提出了一种新颖的多目标优化模型，该模型能在24小时内同时将能量损耗、能源采购成本、负荷削减和电压偏差降至最低，同时还考虑了与电动汽车及电池管理相关的运营成本。该模型使用徒步优化算法（HOA）进行优化，该算法基于托布勒徒步函数利用自适应搜索机制。这种机制增强了对解空间的探索，并有效避免局部最优，与传统方法相比性能更优。在一个33节点配电网的仿真结果表明，与无电动汽车的情况相比，接入电动汽车后，运营成本降低了19.3%，能量损耗减少了59.7%，负荷削减降至最低达75.4%，电压偏差改善了43.5%。此外，该模型消除了光伏（PV）功率削减，从而确保了可再生能源资源的最优利用。与其他优化技术相比，HOA的总成本比科莫多Mlipir算法（KMA）低4.4%，与粒子群优化算法（PSO）相比能量损耗减少了24.5%。这些结果清楚地证明了该模型在增强电网稳定性、优化成本和提高运营效率方面的有效性。所提出的方法为电动汽车渗透率不断提高和可再生能源整合背景下的现代电网管理提供了一种可扩展且可靠的解决方案。
