...
首页> 外文期刊>Advances in Electrical and Electronic Engineering >Congestion Management Based on Optimal Rescheduling of Generators and Load Demands Using Swarm Intelligent Techniques
【24h】

Congestion Management Based on Optimal Rescheduling of Generators and Load Demands Using Swarm Intelligent Techniques

机译:基于Swarm智能技术的发电机优化调度和负荷需求的拥塞管理。

获取原文
   

获取外文期刊封面封底 >>

       

摘要

This paper presents the Congestion Management (CM) methodologies and how they get modified in the new competitive framework of electricity power markets. When the load on the system is increased or when some contingency occurs in the system, some of the lines may become overloaded. Thus, the loadability of the system should be increased by generating and dispatching the power optimally for the secure operation of power system. In this paper, the CM problem is solved by using the optimal rescheduling of generating units and load demands, and the Swarm intelligent techniques are used to handle this problem. Here, the CM problem is solved by using the Particle Swarm Optimization (PSO), Fitness Distance Ratio PSO (FDR-PSO) and Fuzzy Adaptive-PSO (FA-PSO). First, the generating units are selected based on sensitivity to the over-loaded transmission line, and then these generators are rescheduled to remove the congestion in the transmission line. This paper also utilizes the demand response offers to solve the CM problem. The effectiveness of the proposed CM methodology is examined on the IEEE 30 bus and Indian 75 bus test systems.
机译:本文介绍了拥塞管理(CM)方法,以及如何在新的竞争性电力市场框架中对它们进行修改。当系统上的负载增加或系统中发生意外事件时,某些线路可能会过载。因此,应该通过为电力系统的安全运行最佳地产生和分配电力来增加系统的负载能力。在本文中,通过使用发电机组和负荷需求的最佳调度来解决CM问题,并使用Swarm智能技术来解决此问题。在这里,通过使用粒子群优化(PSO),适应距离比PSO(FDR-PSO)和模糊自适应PSO(FA-PSO)解决了CM问题。首先,根据对过载的传输线的敏感性选择发电机组,然后重新安排这些发电机的时间,以消除传输线中的拥塞。本文还利用需求响应提供的解决CM问题。提议的CM方法的有效性在IEEE 30总线和Indian 75总线测试系统上进行了检验。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号