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Impact of partitioning on the performance of decomposition methods for AC Optimal Power Flow

机译:分配对交流最优功率流量分解方法性能的影响

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The optimization problems in power systems become larger and larger due to the increased number of variables from distributed generation and flexible loads. Hence, there has been growing interest in decomposition methods that facilitate distributed decision making. However, limited effort has been spent on the actual implementation of decomposition methods including determining how to partition the problem and what information to exchange among subproblems, which may greatly impact the efficiency of decomposition methods. In this paper, we evaluate the effects of partitioning on the convergence speed of decomposition methods for solving the AC Optimal Power Flow problem. In addition, we propose a speed-up method for the Optimality Condition Decomposition by adding a correction term to refine the search direction. Simulations on the IEEE-30 system show that the convergence speed of the decomposition method can be significantly improved by using a proper partition of the system and the correction term.
机译:由于来自分布式发电和柔性负载的变量增加,电力系统的优化问题变得越来越大。因此,对分解方法的兴趣越来越感兴趣,便于分布式决策。然而,有限的努力已经花费了分解方法的实际实施,包括确定如何分配问题以及在子问题之间交换的信息,这可能会影响分解方法的效率。在本文中,我们评估了分配对解决AC最优功率问题的分解方法的收敛速度的影响。此外,我们提出了一种通过添加校正项来优化搜索方向来提出最优状态分解的速度方法。 IEEE-30系统的仿真表明,通过使用系统和校正项的适当分区,可以显着提高分解方法的收敛速度。

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