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首页> 外文期刊>Science, Measurement & Technology, IET >Coordinated aggregated-based particle swarm optimisation algorithm for congestion management in restructured power market by placement and sizing of unified power flow controller
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Coordinated aggregated-based particle swarm optimisation algorithm for congestion management in restructured power market by placement and sizing of unified power flow controller

机译：基于统一潮流控制器的布局和规模调整的基于聚集的粒子群优化算法在重组电力市场中的拥塞管理

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摘要

This study presents a particle swarm optimisation (PSO)-based algorithm to perform congestion management by proper placement and sizing of one unified power flow controller (UPFC) device in a market-based power systems. The algorithm uses quadratic smooth curves for generators' costs. A typical load duration curve (LDC) is used to improve the accuracy of the model by incorporating the impacts of load variation on the optimisation problem. The proposed approach makes use of the PSO algorithm to allocate the near-optimal GenCos as well as the optimal location and size of UPFC whereas the Newton?? Raphson solution minimises the mismatch of the power flow equations. Simulation results (without/with the line flow constraints, before and after the compensation) are used to analyse the impact of UPFC on the congestion levels of the reliability test system (RTS) 24-bus test system. Simulation results by the proposed PSO algorithm are also compared with solutions obtained by the conventional sequential quadratic programming (SQP) approach.
机译：这项研究提出了一种基于粒子群优化（PSO）的算法，可以通过在基于市场的电力系统中正确放置和调整一个统一潮流控制器（UPFC）设备的大小来执行拥塞管理。该算法使用二次平滑曲线来计算发电机的成本。通过合并负载变化对优化问题的影响，典型的负载持续时间曲线（LDC）用于提高模型的准确性。提出的方法利用PSO算法分配接近最优的GenCos以及UPFC的最佳位置和大小，而Newton？ Raphson解决方案最大程度地减小了潮流方程的不匹配。仿真结果（在补偿前后，在没有/有线路流量约束的情况下）用于分析UPFC对可靠性测试系统（RTS）24总线测试系统的拥塞水平的影响。提出的PSO算法的仿真结果也与传统的顺序二次规划（SQP）方法获得的解决方案进行了比较。

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