在混合能源系统的动态单元承诺和经济排放调度中采用集成的二进制元启发式方法
S Syama1, J Ramprabhakar2, R Anand3
1Department of Electrical and Electronics Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru, India. s_syama@blr.amrita.edu.
Scientific reports
|October 13, 2024
概括
这项研究引入了一种新的混合算法,即Crow Search Improved Binary Grey Wolf Optimization (CS-BIGWO),用于优化发电调度. CS-BIGWO算法有效地降低了使用可再生能源的混合能源系统中的燃料成本和排放.
科学领域:
- 电气工程 电气工程
- 优化算法 优化算法
- 可再生能源系统可再生能源系统
背景情况:
- 由于化石燃料耗尽和全球变暖,对零排放能源的需求日益增加.
- 将间歇性可再生能源 (RES) 整合到现有电网中的挑战.
- 传统的单位承诺 (UC) 和综合经济排放调度 (CEED) 方法的限制.
研究的目的:
- 为解决复杂的UC-CEED问题开发一种高效的混合元启发算法.
- 在集成可再生能源的电力系统中,尽量降低燃料成本和有害排放.
- 改进传统和可再生能源发电机组的调度.
主要方法:
- 提出了一个新的混合算法:Crow Search 改进的二进制灰狼优化 (CS-BIGWO).
- 集成的CS-BIGWO与增强的lambda代用于UC-CEED问题解决.
- 利用了优化算法优化极端学习机器 (LCWOA-ELM) 进行前一天的可再生能源预测.
主要成果:
- 在标准数学函数上,CS-BIGWO算法表现出卓越的性能.
- 在IEEE-39总线系统上进行测试,拟议的方法实现了燃料成本和排放量的降低.
- 实现了0.1021%的燃料成本和0.7995%的排放降低 (案例1:没有 RES) 和0.12896%的燃料成本和0.772%的排放降低 (案例2:有 RES).
结论:
- 拟议的CS-BIGWO与增强的lambda代集成,有效地解决了混合能源系统的UC-CEED问题.
- 该方法在降低运营成本和环境影响方面被证明优于现有方法.
- 验证了先进的元启发算法的潜力,以优化可再生能源集成的电力系统运行.
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