在热电系统的优先级敏感调度下,使用GA-APO杂交的加权成本排放调度优化.
Chodagam Srinivas1, M Rama Prasad Reddy2, Vineet Kumar1
1Department of Electrical and Electronics Engineering, Madanapalle Institute of Technology & Science, Madanapalle, Andhra Pradesh, India.
Scientific reports
|March 5, 2026
概括
本研究介绍了用于发电厂调度的混合基因算法-北极优化 (GA-APO) 技术. 这种新的方法有效地平衡了发电中的经济成本和环境排放.
科学领域:
- 电气工程 电气工程
- 优化算法 优化算法
- 环境科学 环境科学
背景情况:
- 现代电力公用事业公司面临复杂的调度挑战,将经济燃料成本与环境影响相结合.
- 传统的单一目标优化工具与热单元的非线性燃料和排放特性作斗争.
研究的目的:
- 为经济-排放调度问题制定一个强大的两阶段优化策略.
- 创建一个灵活的加权配方,以平衡成本节约和减排.
主要方法:
- 一种混合方法,将广泛搜索的遗传算法 (GA) 与局部改进的北极优化 (APO) 结合起来.
- 测试拟议的GA-APO方法在一个三发电机热电厂的24小时时间表上.
主要成果:
- 在各种条件下,GA-APO方法在运行限制内表现令人满意.
- 与GA单独相比,实现了高达1.88%的运营成本 (成本优先) 降低.
- 减少了约0.21%的排放消耗 (排放优先),每兆瓦时的成本降低了0.68% (平衡).
结论:
- 混合GA-APO策略为经济-排放调度问题提供了有效的解决方案.
- 权重配方允许适应性优化,以实现成本,减排或妥协.
- 与传统GA相比,经过验证的有效性,提供了显著的经济和环境效益.
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