Voltage security constrained reactive power planning considering the costs and performance of VAR devices

N. Yorino, M. Eghbal, E. E. El-Araby, Y. Zoka
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引用次数: 9

Abstract

This paper deals with optimal allocation of fast and slow VAR devices under different load levels. These devices are utilized to maintain system security in normal and contingency states, where corrective and preventive controls are implemented for the contingency cases. Load shedding and fast VAR devices are used in the corrective state in order to quickly restore system stability even though they are expensive, while cheap slow VAR devices can be used in the preventive state to obtain the desired security level. The main objective of this paper is to make a trade-off between economy and security by determining the optimal combination of fast and slow controls (load shedding, new slow and fast VAR devices). To meet the desired security limits, a variety of constraints have to be considered during the investigated transitions states. The proposed RPP problem is a combinatorial optimization problem, which cannot be solved easily by conventional optimization methods. Swarm optimization methods are reported to be efficient to solve combinatorial optimization problems. This paper discovers the efficiency of Particle Swarm Optimization (PSO) and Evolutionary PSO (EPSO) in solving the proposed RPP problem. The proposed approaches have been successfully tested on IEEE 14 bus system and a comparative study is illustrated.
考虑成本和性能的电压安全约束无功规划
本文研究了不同负荷水平下快速和慢速无功装置的优化配置问题。这些设备用于维持系统在正常和应急状态下的安全性,并针对应急情况实施纠正和预防控制。在纠偏状态下使用减载和快速VAR设备,即使价格昂贵,也可以快速恢复系统的稳定性,而在预防状态下使用便宜的慢速VAR设备,可以获得所需的安全级别。本文的主要目标是通过确定快速和慢速控制的最佳组合(减载,新的慢速和快速VAR设备),在经济和安全之间进行权衡。为了满足期望的安全限制,在所研究的转换状态期间必须考虑各种约束。所提出的RPP问题是一个组合优化问题,传统的优化方法难以解决。群优化方法被认为是解决组合优化问题的有效方法。研究了粒子群算法和进化粒子群算法在求解RPP问题中的有效性。所提出的方法已在IEEE 14总线系统上成功地进行了测试,并进行了比较研究。
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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