Optimization of Thermal Power Plant Operations Using Genetic Algorithms

Sapto Nisworo, Arnawan Hasibuan, Syafii Syafii
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Abstract

Accurate scheduling of capacity and operating time for electricity generation is intended to be able to determine the start and end periods of electricity generation operations and produce power output that can meet load requirements. In this research, the goal to be achieved is to know the existence of power plants when to start operating and when to stop operations and to minimize operational costs by dividing the value of the power that will be generated at each power plant. Genetic algorithms are applied to thermal power plant data patterns to design a scheduling plan. The process involves combining the six power generating units to be tested into three different samples. It was found that the total power load and total cost for Sample 1 was 78,109 MW and IDR 200,285, 66.26, Sample 2 was 74,497 MW and IDR 149,774,156.41, and Sample 3 was 78,681 MW and IDR 156,297,893, respectively. 08. This shows that the cost of sample 1 compared to sample 2 decreased by 25.22%, then in sample 2 when compared to sample 3 it increased by 4.17%. The data also shows that a higher number of generations results in lower costs. Therefore, genetic algorithms produce better solutions from one generation to the next.
利用遗传算法优化火力发电厂运行
准确安排发电能力和运行时间的目的是能够确定发电运行的开始和结束时间,并产生能够满足负荷要求的电力输出。在这项研究中,要实现的目标是了解发电厂何时开始运行,何时停止运行,并通过划分各发电厂的发电量价值,最大限度地降低运行成本。遗传算法应用于火力发电厂数据模式,以设计调度计划。这一过程包括将六个待测试的发电单元组合成三个不同的样本。结果发现,样本 1 的总电力负荷和总成本分别为 78,109 兆瓦和 200,285,66.26 印尼盾,样本 2 为 74,497 兆瓦和 149,774,156.41 印尼盾,样本 3 为 78,681 兆瓦和 156,297,893 印尼盾。08.这表明,样本 1 与样本 2 相比,成本下降了 25.22%,而样本 2 与样本 3 相比,成本上升了 4.17%。数据还显示,代数越高,成本越低。因此,遗传算法一代比一代产生更好的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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