多目标群算法,以优化电动汽车在配电网中的集成
1Department of Mathematics, Central Tehran Branch, Islamic Azad University, Tehran, Iran. masumeazadi.uni@gmail.com.
Scientific reports
|July 13, 2025
概括
电动汽车 (EV) 整合挑战电网. 一个新的优化框架和多目标群算法 (MOMSA) 最小化了成本,损失和电压偏差,提高了电网稳定性和效率.
科学领域:
- 电气工程 电气工程
- 优化理论 优化理论
- 可再生能源系统可再生能源系统
背景情况:
- 电动汽车 (EV) 的高透率使配电网受到压力,导致能源损失,电压不稳定以及运营成本增加.
- 现有的优化方法往往无法全面解决这些多目标挑战,特别是可再生能源的变化.
研究的目的:
- 开发一个混合整数多目标优化框架,同时最大限度地降低成本,能源损失,负载减散和电压偏差,在电网高EV透率.
- 引入和验证一个新的多目标群算法 (MOMSA),以在复杂的,非凸起的网格场景中进行高效的优化.
主要方法:
- 一个多目标的优化框架,整合了电动汽车的充电/放电,可再生能源,需求侧的灵活性,以及在负载水龙头变换器 (OLTC) 和静电压调节器 (SVR) 的协调控制.
- 实现了新的多目标群算法 (MOMSA) 来解决优化问题.
- 在33个公共汽车配送网络上进行的模拟,将MOMSA与传统算法 (HOA,PSO,GA) 进行比较.
主要成果:
- 与非电动汽车集成方案相比,MOMSA实现了19.2%的成本降低.
- 在降低总成本,能源损失和提高电压稳定性方面,MOMSA的性能比传统算法高出7.4-15.7%.
- 敏感性分析证实了该模型在不同电价,可再生能源间歇性和不协调的电动汽车充电的情况下的稳定性.
结论:
- 拟议的基于MOMSA的框架有效地提高了电网可靠性,经济效率和电动汽车丰富环境中的可持续性.
- 该研究解决了优化复杂配电网的关键差距,并高度整合电动汽车和可再生能源.
- 在导航非凸的解决方案空间中,MOMSA表现出卓越的性能,用于多目标电网优化.
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