研究微电网的经济负载调度问题,基于改进的鱼优化算法
1College of Electrical and Computer Science, Jilin Jianzhu University, Changchun 130119, China.
Biomimetics (Basel, Switzerland)
|May 24, 2024
概括
一个改进的优化算法 (IPOA) 通过减少热电厂的煤炭消耗来提高经济负载调度. 这种新的方法优化了燃料成本,提高了发电效率.
科学领域:
- 工程 工程师 工程师 工程师
- 计算机科学 计算机科学
- 优化算法 优化算法
背景情况:
- 经济负载调度 (ELD) 问题对于优化发电厂运行和最大限度地降低燃料成本至关重要.
- 传统的优化算法在有效解决复杂,高维度的ELD问题方面面临着挑战.
- 人口多样性和逃避局部最佳值是ELD的元启发性优化中的关键问题.
研究的目的:
- 引入一个改进的优化算法 (IPOA) 来解决经济负载调度问题.
- 为了增强优化算法的种群多样性和全球搜索能力.
- 为了提高收速度和逃离局部优化的能力,优化电力系统.
主要方法:
- 从交叉优化算法中整合垂直交叉操作员以增加人口多样性.
- 引入一个最佳个体来指导人口搜索,并纳入逃离局部最佳的干扰因素.
- 采用一个维度变化策略来改进最佳的个体和加速融合.
主要成果:
- 与标准的鱼优化算法 (POA) 相比,IPOA显示了煤炭消耗的显著降低.
- 在10,40和80维的热电站单元上进行测试,IPOA实现了分别减少0.0292%,2.7273%和3.6739%的煤炭消耗.
- IPOA有效地降低了发电厂的整体燃料成本.
结论:
- IPOA是一个非常有效的优化算法,用于解决经济负载调度问题.
- 对的优化算法的改进显著提高了其在效率和解决方案质量方面的性能.
- IPOA为减少电力行业的运营成本和环境影响提供了一个有希望的方法.
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