经济-环境-技术最优的电力流解决方案使用一种新的自适应性野算法与随机风能和太阳能
Pavel Trojovský1, Eva Trojovská2, Ebrahim Akbari2
1Department of Mathematics, Faculty of Science, University of Hradec Králové, Rokitanského 62, 500 03, Hradec Králové, Czech Republic. pavel.trojovsky@uhk.cz.
Scientific reports
|February 20, 2024
概括
本研究介绍了一种增强的自适应性野算法 (SAWGA),用于优化使用可再生能源在电网中的电力流. SAWGA有效地降低了成本并改善了融合,优于传统方法.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 优化算法 优化算法
背景情况:
- 最佳功率流 (OPF) 问题很复杂,特别是在可再生能源的整合方面.
- 传统的算法由于高度复杂性和局部最佳值而与OPF扎.
- 现有的方法在优化经济-环境-技术目标方面面临挑战.
研究的目的:
- 引入一个增强的自适应性野算法 (SAWGA) 来解决OPF问题.
- 解决集成太阳能光伏 (PV) 和风能 (WT) 单元的电力系统中的不确定性.
- 提高OPF解决方案的成本降低和融合速度.
主要方法:
- 通过将四个优化器纳入经典的野算法来开发SAWGA.
- 应用SAWGA来优化IEEE 30公交和118公交电网上的OPF模型.
- 与PV和WT单元一起使用常规热单元的模拟系统.
主要成果:
- 与传统WGA和其他算法相比,SAWGA在优化OPF方面表现出卓越的性能.
- 实现了燃料消耗总体成本的显著降低.
- 展示了更快,更高效的趋同,以达到最佳解决方案.
结论:
- SAWGA有效地管理了OPF的挑战,并优化了各种目标功能.
- 该算法表现出强大的能力,能够找到全球或接近全球的最佳设置.
- SAWGA为总成本降低和电力系统快速融合提供了一种卓越的方法.
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