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Probabilistic load flow for photovoltaic distributed generation using the Cornish-Fisher expansion

机译：使用Cornish-Fisher扩展的光伏分布式发电的概率潮流

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摘要

This paper shows that in order to solve a probabilistic load flow in radial distribution networks, it is necessary to apply effective techniques that take into account their technical constraints. Among these constraints, voltage regulation is one of the principal problems to be addressed in photovoltaic distributed generation. Probabilistic load flows can be solved by analytical techniques as well as the Monte Carlo method. Our research study applied an analytical method that combined the cumulant method with the Cornish-Fisher expansion to solve this problem. The Monte Carlo method is used to compare the results of analytical method proposed. To evaluate the performance of photovoltaic distributed generation, this paper describes a probabilistic model that takes into account the random nature of solar irradiance. Therefore, load and photovoltaic distributed generation are modelled as independent/dependent random variables. The results obtained show that the technique proposed gave a better performance than the Monte Carlo method. This technique provided satisfactory solutions with a smaller number of iterations. Therefore, convergence was rapidly attained and computational cost was lower than that required for the Monte Carlo method. Besides, the results revealed how the Cornish-Fisher expansion had a better performance than the Gram-Charlier expansion, when input random variables were non-Gaussian.
机译：本文表明，为了解决径向分布网络中的概率潮流，有必要应用考虑其技术约束的有效技术。在这些限制中，电压调节是光伏分布式发电中要解决的主要问题之一。概率潮流可以通过分析技术以及蒙特卡洛方法求解。我们的研究应用了一种分析方法，将累积量方法与Cornish-Fisher展开相结合来解决此问题。蒙特卡罗方法用于比较所提出的分析方法的结果。为了评估光伏分布式发电的性能，本文介绍了一种概率模型，该模型考虑了太阳辐照度的随机性。因此，将负荷和光伏分布式发电建模为独立/相关的随机变量。获得的结果表明，所提出的技术比蒙特卡洛方法具有更好的性能。该技术以较少的迭代次数提供了令人满意的解决方案。因此，可以快速实现收敛，并且计算成本低于蒙特卡洛方法所需的成本。此外，结果显示当输入随机变量为非高斯时，Cornish-Fisher展开比Gram-Charlier展开具有更好的性能。

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