Al-Dhaifallah Mujahed, Refaat Mohamed M, Alaas Zuhair, Abdel Aleem Shady H E, Ali Ziad M
Control and Instrumentation Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia.
IRC for Sustainable Energy Systems (IRC-SES), King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia.
Sci Rep. 2024 Oct 27;14(1):25607. doi: 10.1038/s41598-024-76410-0.
The increasing adoption of electric vehicles (EVs) presents both opportunities and challenges for power networks. While EVs have the potential to reduce carbon emissions, accommodating their growing power demand requires careful planning to prevent overloading and mitigate environmental impacts. This paper introduces an integrated hosting capacity model to facilitate higher EV penetration while maintaining environmental standards. In addition to EV charging stations, the model incorporates transmission lines, reactive power compensators, energy storage systems, and thyristor-controlled series compensators to ensure a reliable power supply. The model aims to maximize EV charging station deployment, minimize greenhouse gas emissions, and optimize net present value through hosting capacity strategies. Three hosting capacity plans are proposed to analyze the impact of prioritizing one of these objectives over the others in network configurations. Accurate EV demand forecasting is critical for this model, and a swarm intelligence forecasting algorithm is proposed to explore various forecasting approaches. The model is complex and involves nonlinear multi-objective optimization. To solve it, a new hybrid optimization algorithm is introduced, combining the features of the Marine Predators Algorithm and the Honey Badger Algorithm. Three hybridization schemes-Series Hybrid Scheme, Population Division Scheme, and Switching Strategy Scheme-are developed to address the optimization challenges effectively. The results show that the first and second hybridization schemes are the most effective for solving the EV load forecasting models, with a robustness of at least 90%. In contrast, the robustness of the third scheme reaches only 30% in some models. Simulation studies on the IEEE 9-bus network and the IEEE 30-bus system validate the model's effectiveness in integrating EVs while achieving environmental sustainability objectives. The findings show the superiority of the proposed hybrid schemes in solving the hosting capacity model in terms of finding optimal solutions. However, the third scheme required less computing time than the others, with its convergence time being at least 33.3% shorter.
电动汽车（EV）的日益普及给电网带来了机遇和挑战。虽然电动汽车有潜力减少碳排放，但要满足其不断增长的电力需求，需要精心规划以防止过载并减轻环境影响。本文介绍了一种综合承载能力模型，以在保持环境标准的同时促进更高的电动汽车渗透率。除了电动汽车充电站，该模型还纳入了输电线路、无功功率补偿器、储能系统和晶闸管控制串联补偿器，以确保可靠的电力供应。该模型旨在通过承载能力策略最大化电动汽车充电站的部署，最小化温室气体排放，并优化净现值。提出了三种承载能力计划，以分析在网络配置中优先考虑这些目标之一对其他目标的影响。准确的电动汽车需求预测对该模型至关重要，为此提出了一种群智能预测算法来探索各种预测方法。该模型复杂，涉及非线性多目标优化。为了解决这个问题，引入了一种新的混合优化算法，结合了海洋捕食者算法和蜜獾算法的特点。开发了三种混合方案——串联混合方案、种群划分方案和切换策略方案——以有效应对优化挑战。结果表明，第一种和第二种混合方案对解决电动汽车负荷预测模型最有效，鲁棒性至少为90%。相比之下，第三种方案在某些模型中的鲁棒性仅达到30%。在IEEE 9节点网络和IEEE 30节点系统上的仿真研究验证了该模型在整合电动汽车的同时实现环境可持续性目标方面的有效性。研究结果表明，所提出的混合方案在寻找最优解方面解决承载能力模型具有优越性。然而，第三种方案所需的计算时间比其他方案少，其收敛时间至少缩短33.3%。
