基于Coyote优化算法的综合风电经济调度

U. Güvenc, Enes Kaymaz
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引用次数: 24

摘要

电力系统中使用的化石燃料由于释放温室气体而造成空气污染和全球变暖。如今,由于对生态环境的关注和燃料价格的上涨,可再生能源尤其是风能在发电中得到了更广泛的应用。为此,本文提出了经济调度综合风电方案。然而,风力发电是随机的,因为风速在本质上是不确定的。因此,采用威布尔概率密度函数(PDF)和不完全伽马函数(IGF)对风力进行估计和建模。为了有效地解决该问题,将COA算法应用于该问题,并在由火电机组和风力发电机组组成的多种电力系统上进行了测试。仿真结果与其他启发式算法如遗传算法和粒子群算法进行了比较。可以清楚地看到,COA比GA和PSO产生更好的结果。
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 Integrated Wind Power Using Coyote Optimization Algorithm
Fossil fuels used in power system cause air pollution and global warming because of releasing greenhouse gases. Nowadays, renewable energy especially wind power has more widespread in power generation due to ecological concerns and increasing fuel prices. Therefore, it is presented the Economic Dispatch integrated wind power approach in this paper. However, wind power is stochastic because wind speed is uncertain in nature. Therefore Weibull Probability Density Function (PDF) and Incomplete Gamma Function (IGF) are used to estimating and modelling wind power. To solve the problem effectively, Coyote Optimization Algorithm (COA) is implemented to the problem and it was tested on various power system consisting thermal generator and wind power generator. Simulation results generated by COA are compared with other heuristic algorithm such as GA and PSO. It can be clearly seen that COA produces better results than GA and PSO.
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