Probabilistic calculation of total transfer capability (TTC) for power systems with wind farms using evolutionary algorithms

M. R. Aghaebrahimi, R. K. Golkhandan, S. Ahmadnia
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引用次数: 3

Abstract

Total transfer capability (TTC) represents the maximum power transfer between areas of a power system, while considering its constraints. In modern power systems, it is important to determine the TTC between different areas as it has become a very serious concern for grid designers. This index is used in the operation, design and electricity marketing stages of power systems. In recent years, there is much attention towards the use of renewable energy units in power systems, which increases the necessity of applying probabilistic methods. In this paper, the probabilistic calculations of power transfer capability in the presence of wind farms are performed, applying evolutionary algorithms. In addition, K-means clustering algorithm is applied in clustering the data related to the wind farms' output power. Then, the simulation results obtained from applying evolutionary algorithms are compared with each other. IEEE 30-bus system is used as the test network.
基于进化算法的风电场电力系统总传输能力(TTC)概率计算
总传输能力(TTC)表示在考虑约束条件的情况下,电力系统各区域之间的最大功率传输能力。在现代电力系统中,确定不同区域之间的TTC已成为电网设计人员非常关注的问题。该指标用于电力系统的运行、设计和销售阶段。近年来,可再生能源机组在电力系统中的应用受到越来越多的关注,这增加了应用概率方法的必要性。本文采用进化算法对有风电场时的输电能力进行了概率计算。此外,采用K-means聚类算法对风电场输出功率相关数据进行聚类。然后,对应用进化算法得到的仿真结果进行了比较。测试网络采用IEEE 30总线系统。
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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