一种新的混合多操作员进化算法,用于动态分布式发电优化和最佳料再配置
Aamir Ali1, Abdul Sattar Saand2, Shoaib Ali2
1Department of Electrical Engineering, Quaid-E-Awam University of Engineering Science and Technology, Nawabshah, 67450, Sindh, Pakistan. aamirali.bhatti@quest.edu.pk.
Scientific reports
|September 24, 2025
概括
本研究介绍了一种混合进化算法,将遗传算法,差异进化和粒子群优化相结合,用于分布式生成集成和网络重新配置. 该方法显著降低了功率损失和电压偏差,同时提高了配电网络的负载能力.
科学领域:
- 电气工程 电气工程
- 电力系统工程 电力系统工程
- 优化技术 优化技术
背景情况:
- 分布式发电 (DG) 集成和网络重新配置对于现代配电网络至关重要.
- 之前的研究还没有彻底研究这些策略的综合影响.
- 技术目标,如降低功率损失,最大限度地减少电压偏差和提高电压稳定性,是有效的网络规划和运营的关键.
研究的目的:
- 调查变化的太阳辐射和负载需求对配电网络的影响.
- 解决大型GD集成和网络重新配置的复杂混合整数非线性问题.
- 开发一种创新的混合进化算法,以优化这些联合挑战.
主要方法:
- 一个新的混合进化算法,结合了遗传算法 (GA),差异进化 (DE) 和粒子群优化 (PSO).
- 整合具有代表性的约束处理技术,以平衡勘探和开发.
- 在各种场景下对IEEE 33和69总线系统进行测试,包括改变太阳辐射和负载需求.
主要成果:
- 拟议的混合多运营商EA实现了对大规模问题的近全球最佳解决方案.
- 证明了超过86%的功率损耗减少.
- 通过集成GD,专注于电压稳定性指数,实现了超过90%的电压偏差改善和超过700%的负载能力增加.
结论:
- 混合EA有效地解决了复杂的,大规模的分销网络优化问题.
- 将总局集成到网络重新配置中,优先考虑电压稳定性,显著提高了网络性能.
- 开发的方法提供了一个强大的方法来改进发电系统的规划和运行.
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