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Cumulant Based Stochastic Reactive Power Planning Method for Distribution Systems With Wind Generators

机译:风力发电机配电系统中基于累积量的随机无功规划方法

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Proliferation of wind generators (WGs) requires a change in distribution system planning techniques as WGs have intermittent and uncertain output. This paper proposes a Cumulant based stochastic optimal reactive power planning method for distribution systems with high penetration of WGs. Uncertainties in the output of WGs and load forecasts are modeled using probability density functions (PDFs). With a stochastic framework, an optimization challenge is formulated to minimize the total costs of new capacitors and the total annual energy loss. The optimization problem is then solved by using the Logarithmic Barrier Interior Point Method (LBIPM). At the optimal solution, LBIPM provides a linear relationship between the cumulants of independent variables (load and wind power) and the cumulants of the dependent system parameters. The Cumulant method offers a generous advantage in speed, while maintaining acceptable accuracy, as compared to the computationally cumbersome traditional Monte Carlo simulation (MCS) method. The method is tested on 7-bus, 33-bus, and 129-bus systems. The results are reported and discussed. The performance and accuracy are assessed by comparing the results with those from MCS method.
机译:风力发电机(WG)的扩散需要改变配电系统的规划技术,因为WG具有间歇性和不确定性的输出。本文提出了一种基于累积量的随机最优无功规划方法,用于WGs渗透率较高的配电系统。使用概率密度函数(PDF)对工作组输出和负荷预测的不确定性进行建模。在随机框架下,提出了优化挑战,以最大程度地减少新电容器的总成本和每年的总能量损耗。然后,通过使用对数势垒内点法(LBIPM)解决优化问题。在最佳解决方案中,LBIPM在独立变量(负载和风力)的累积量与相关系统参数的累积量之间提供线性关系。与计算上繁琐的传统蒙特卡洛模拟(MCS)方法相比,累积量方法在速度上具有可观的优势,同时保持了可接受的精度。该方法已在7总线,33总线和129总线系统上进行了测试。报告结果并进行讨论。通过将结果与MCS方法的结果进行比较来评估性能和准确性。

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