修改的人工蜂鸟算法的应用在考虑可再生能源的电力网络中的最佳电力流量和发电能力
Marwa M Emam1, Essam H Houssein1, Mohamed A Tolba2
1Faculty of Computers and Information, Minia University, Minia, Egypt.
Scientific reports
|December 5, 2023
概括
经过修改的人工蜂鸟算法 (mAHA) 通过将成本和损失降至最低来优化电力流动,即使与可再生能源集成. 这种方法提高了电网的稳定性和效率.
科学领域:
- 电气工程 电气工程
- 优化算法 优化算法
- 电力系统分析 分析 分析
背景情况:
- 现代电力系统面临着增加负载的挑战,导致功率损失,电压不稳定和过载.
- 传统的最佳功率流 (OPF) 模型往往忽略了网络排放,这是可再生能源整合上升的关键因素.
- 整合可再生能源需要仔细规划,以避免对电网性能产生负面影响.
研究的目的:
- 开发和评估一个修改的人工蜂鸟算法 (mAHA) 来解决最佳功率流 (OPF) 问题.
- 为了最大限度地降低燃料成本,实际功率损失,排放成本和电网中的电压偏差,考虑可再生能源.
- 评估mAHA在加强电力系统规划和运营方面的有效性,包括分布式发电机 (DG) 的安置.
主要方法:
- 提出了一种新的优化算法,即修改的人工蜂鸟算法 (mAHA),将本地逃生运算符 (LEO) 和基于对立的学习 (OBL) 集成到基本的人工蜂鸟算法 (AHA) 中.
- 在修改后的IEEE-30总线和IEEE-118总线系统上测试了mAHA算法,以解决OPF问题.
- 使用CEC'2020测试套件对其他七种全球优化算法进行了性能比较.
主要成果:
- 与其他经过测试的元启发式算法相比,mAHA算法在最小化整体成本函数方面表现出卓越的性能.
- 模拟结果证实了算法在各种场景下为OPF问题提供融合解决方案的能力,包括可再生能源集成.
- 提出的方法有效地解决了运行约束,同时优化了成本,损失和电压偏差等多个目标.
结论:
- 修改后的人工蜂鸟算法 (mAHA) 是一个有效和高效的优化器,可以解决复杂的最佳功率流量问题.
- mAHA为电力系统规划提供了一个强大的解决方案,可以将成本和排放量最小化,同时提高电网稳定性.
- 该算法的LEO和OBL集成提高了其搜索效率和克服现有优化技术局限性的能力.
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