微电网的最佳调度基于两个人群的合作搜索机制
1School of Electrical and Computer Engineering, Jilin Jianzhu University, Changchun 130118, China.
Biomimetics (Basel, Switzerland)
|October 28, 2025
概括
这项研究介绍了一种新的混合智能算法 (IMOHHOGWO),用于微电网的最佳调度. 新方法提高了管理复杂约束和冲突目标的效率和准确性,降低了成本和碳排放.
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:
- 微电网最佳调度面临诸如高维非线性约束,多目标冲突和低解决方案效率等挑战.
- 现有的算法很难有效地平衡全球勘探和当地开发.
研究的目的:
- 提出一个新的多目标哈里斯-灰狼混合智能算法 (IMOHHOGWO),用于微电网的最佳调度.
- 解决现有方法在处理复杂的约束和相互矛盾的目标方面的局限性.
主要方法:
- 引入了适应性能源和非线性收因子,以平衡勘探和开采.
- 整合了哈里斯·霍克优化的随机突袭策略,用于多样化的解决方案,以及灰狼优化器的引导,用于约束调整.
- 采用模拟的回火扰动策略,以提高对复杂约束的适应性和局部搜索准确度.
主要成果:
- 与微电网调度模拟中的其他三种算法相比,IMOHHOGWO算法显示出更高的融合速度和准确性.
- 对多目标测试函数的性能分析显示,IMOHHOGWO的性能优于其他四种算法.
- 经过验证的微电网最佳调度模型的电网运行成本和碳排放的降低.
结论:
- IMOHHOGWO混合智能算法对微电网最佳调度非常有效,提供更高的效率和准确性.
- 该算法成功地管理了高维非线性约束和多目标冲突.
- 为未来的集成微电网最佳调度研究和应用提供可行和高效的参考.
关键词:
灰狼优化器 灰狼优化器哈里斯·霍克优化器 哈里斯·霍克优化器在 IMOHHOGWOWO 中.模拟化 (SA) 策略是一种模拟化 (SA) 策略.碳排放成本是碳排放的成本.多目标优化多目标优化微电网的优化调度更多相关视频
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