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Multi-objective probabilistic reactive power and voltage control with wind site correlations

机译:具有风场关联的多目标概率无功功率和电压控制

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This paper proposes a multi-objective probabilistic reactive power and voltage control in distribution networks using wind turbines, hydro turbines, fuel cells, static compensators and load tap changing transforms. The objective functions are total electrical energy costs, the electrical energy losses, total emissions produced, and voltage deviations during the next day. Since the wind sources and load demand have intermittent characteristics, a probabilistic load flow based on 2m + 1 point estimated method is used to investigate the objective functions. The correlation in wind speed is considered as the distances between WTs are not large in distribution systems. Furthermore, a multi-objective modified bee swarm optimization is proposed to solve the optimization problem by defining a set of non-dominated points as the solutions. A fuzzy based clustering is used to control the size of the repository and a niching method is utilized to choose the best solution during the optimization process. Performance of the proposed algorithm is tested on a 69-bus distribution feeder. The results confirm the necessity of modeling the reactive power and voltage control problem in a stochastic framework. Also, the effects of wind site correlations on different objective functions are discussed completely.
机译:本文提出了一种使用风力涡轮机,水轮机,燃料电池,静态补偿器和负荷分接变换的配电网多目标概率无功功率和电压控制。目标函数是总电能成本,电能损耗,产生的总排放量以及第二天的电压偏差。由于风源和负荷需求具有间歇性特征,因此基于2m +1点估计方法的概率潮流用于研究目标函数。风速的相关性被认为是在分配系统中WT之间的距离不大。此外,提出了一种多目标改进蜂群优化算法,通过定义一组非支配点作为解来解决优化问题。基于模糊的聚类用于控制存储库的大小,利基计算方法用于在优化过程中选择最佳解决方案。该算法的性能在69总线配电馈线上进行了测试。结果证实了在随机框架中对无功功率和电压控制问题进行建模的必要性。此外,还完整讨论了风场相关性对不同目标函数的影响。

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