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首页> 外文会议>IEEE PES Transmission and Distribution Conference and Exposition >Congestion management in deregulated power system by optimal choice and allocation of FACTS controllers using multi-objective genetic algorithm
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Congestion management in deregulated power system by optimal choice and allocation of FACTS controllers using multi-objective genetic algorithm

机译：基于多目标遗传算法的FACTS控制器的最优选择和分配，在解除管制的电力系统中进行拥塞管理

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Congestion management is one of the technical challenges in power system deregulation. This paper presents single objective and multi-objective optimization approaches for optimal choice, location and size of Static Var Compensators (SVC) and Thyristor Controlled Series Capacitors (TCSC) in deregulated power system to improve branch loading (minimize congestion), improve voltage stability and reduce line losses. Though FACTS controllers offer many advantages, their installation cost is very high. Hence Independent System Operator (ISO) has to locate them optimally to satisfy a desired objective. Genetic Algorithms (GA) are best suitable for solution of combinatorial optimization and multi-objective optimization problems. This paper presents optimal location of FACTS controllers considering branch loading (BL), voltage stability (VS) and loss minimization (LM) as objectives at once using GA. It is observed that the locations that are most favorable with respect to one objective are not suitable locations with respect to other two objectives. Later these competing objectives are optimized simultaneously considering two and three objectives at a time using multi-objective Strength Pareto Evolutionary Algorithms (SPEA). The developed algorithms are tested on IEEE 30 bus system. Various cases like i) uniform line loading ii) line outage iii) bilateral and multilateral transactions between source and sink nodes have been considered to create congestion in the system. The developed algorithms show effective locations for all the cases considered for both single and multi-objective optimization studies.
机译：拥塞管理是电力系统放松管制的技术挑战之一。本文提出了单目标和多目标优化方法，以在去稳压电源系统中优化选择，静态无功补偿器（SVC）和晶闸管控制串联电容器（TCSC）的位置和大小，以改善分支负载（最大程度地减少拥塞），提高电压稳定性和减少线损。尽管FACTS控制器具有许多优点，但其安装成本非常高。因此，独立系统运营商（ISO）必须最佳地定位它们，以满足所需的目标。遗传算法（GA）最适合解决组合优化和多目标优化问题。本文提出了使用GA同时考虑支路负载（BL），电压稳定性（VS）和损耗最小化（LM）为目标的FACTS控制器的最佳位置。可以看出，相对于一个目标最有利的位置不是相对于另两个目标而言的合适位置。之后，使用多目标强度帕累托进化算法（SPEA）同时考虑两个和三个目标，同时优化这些竞争目标。所开发的算法已在IEEE 30总线系统上进行了测试。已经考虑了各种情况，例如i）统一线路负载ii）线路中断iii）源节点和宿节点之间的双边和多边交易，从而在系统中造成拥塞。对于单目标和多目标优化研究所考虑的所有情况，已开发的算法均显示出有效的位置。

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