一个修改后的白优化器,以实现最佳的功率流,考虑到可再生能源的不确定性
Mohamed Farhat1, Salah Kamel2, Mohamed A Elseify3
1Electrical Power and Machines Engineering Department, Faculty of Engineering, Ain Shams University, Cairo, 11517, Egypt.
Scientific reports
|February 6, 2024
概括
本研究引入了修改后的白优化 (MWSO) 算法,以解决最佳功率流 (OPF) 问题,有效地处理可再生能源和负载需求的不确定性. 该MWSO算法表现出比原始WSO更优异的性能,改善了电网管理.
科学领域:
- 电气工程 电气工程
- 计算智能是一种计算智能.
- 优化算法 优化算法
背景情况:
- 最佳功率流 (OPF) 问题对于高效的电力系统运行至关重要.
- 整合可再生能源给电网带来了巨大的不确定性.
- 现有的优化算法可能会与现代电力系统的复杂性和随机性质作斗争.
研究的目的:
- 开发和评估一种新的修改白优化 (MWSO) 算法来解决OPF问题.
- 通过整合高斯裸骨 (GB) 和准对立式学习 (QOBL) 策略来增强MWSO算法.
- 在OPF框架内解决可再生能源发电和负载需求的不确定性.
主要方法:
- 修改的白优化 (MWSO) 算法,结合GB和QOBL策略.
- 通过集成风能和太阳能光伏发电机组来修改IEEE30巴和IEEE57巴系统.
- 使用韦布尔分布和逻辑正常分布对可再生能源变量的建模.
- 包括对输出功率过高/低估的储备和罚款成本.
- 使用概率密度函数 (PDF) 分析负载需求不可预测性.
- 考虑热发电机坡道速度限制.
主要成果:
- 与原来的WSO相比,MWSO算法显示了更好的汇率和准确性.
- 拟议的方法有效地处理可再生能源的不确定性和修改的IEEE 30 总线和 57 总线系统的负载变化.
- 使用23个基准函数对六种既定技术进行比较分析,证明了MWSO算法的有效性.
- 统计分析证实了MWSO在不确定的条件下解决OPF方面的优势.
结论:
- MWSO算法是解决最佳功率流量问题的强大而有效的工具.
- 该方法成功地整合了可再生能源,并考虑到需求方面的不确定性.
- 这项研究为电力系统优化和智能电网管理领域做出了宝贵的贡献.
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