基于粒子群优化和模拟退火混合算法的热电厂经济调度

Muhammad Rivaldi Harjian, O. Penangsang, N. K. Aryani
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引用次数: 0

摘要

本文讨论了蒸汽发电厂的功率选择优化,这是产生在每台发电机上的目的是获得一个经济的费用,特别是在龙目岛电力系统,通过关注单个发电机的负荷功率。本文提出了一种混合粒子群优化和模拟退火(PSO-SA)算法来解决经济调度问题。经济调度是一种在系统价格和负荷下对发电机组负荷进行最优分配的方法。经济调度用于规划电力系统中所有可用发电机组的输出,以使燃料成本保持在最低水平并满足系统限制。本研究使用PLN公司的真实数据和粒子群优化算法(Particle Swarm optimization, PSO)的仿真,将仿真与PSO- sa算法进行比较。从本研究可以得出,在Jeranjang和Sambelia蒸汽电厂使用PSO- sa方法比PSO方法提供更好的溶液性能,总成本为${\$}$ 173.860,69 24小时,而如果使用PSO方法,则为${\$}$ 174.006,39 24小时。仿真结果表明,与实际数据系统相比,PSO- sa方法和PSO方法在PT. PLN领域取得了更好的效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Economic Dispatch Steam Power Plant Jeranjang and Sambelia Using Hybrid Algorithm Particle Swarm Optimization and Simulated Annealing
This paper discusses optimization in steam power plants for power selection which is generated on each generator with the aim of obtaining a fee that is economical, especially in the Lombok Electricity System, by paying attention to load power on individual generators. This paper proposes a hybrid particle swarm optimization and simulated annealing (PSO-SA) to solve economic Dispatch. Economic Dispatch is a method of dividing the load on power system generating units optimally at system prices and loads. Economic Dispatch is used to plan the outputs of all available generation units in the power system so that fuel costs are kept to a minimum and system restrictions are met. This study uses real data from PLN company and simulation from the algorithm Particle Swarm optimization (PSO) to compare the simulation with the PSO-SA algorithm. From this study can be concluded at Jeranjang and Sambelia steam power plants using the PSO-SA method provides better solution performance compared to the PSO method, with a total cost is ${\$}$ 173.860,69 for 24 hours, whereas if you use the PSO method, it is ${\$}$ 174.006,39 for 24 hours. The simulation results show that the method PSO-SA and PSO methods obtained better results when compared to the real data system in the field of PT. PLN.
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