University of Tabriz Tabriz Journal of Electrical Engineering 2008-7799 47 3 2017 11 22 Probabilistic Assessment of Voltage in Active Distribution Networks Considering Correlation between Photovoltaic Distributed Generations Probabilistic Assessment of Voltage in Active Distribution Networks Considering Correlation between Photovoltaic Distributed Generations 1161 1169 5981 FA A. H. Faraji Department of Electrical Engineering, Shahid Beheshti University, Tehran, Iran M. Tourandaz Kenari Department of Electrical Engineering, Shahid Beheshti University, Tehran, Iran M. S. Sepasian Department of Electrical Engineering, Shahid Beheshti University, Tehran, Iran M. Setayesh Nazar Department of Electrical Engineering, Shahid Beheshti University, Tehran, Iran Journal Article 2016 07 19 Due to the development of photovoltaic distributed generation (DG) in distribution networks, these DGs can impact on the operation, planning and reliability of system. In this context, this paper has used a probabilistic load flow algorithm to assess the effects of photovoltaic generation uncertainties on the distribution networks. This method is based on the cumulants. It does not need complex computations like convolution method or high computational burden like Monte Carlo simulation. Proposed methodology can evaluate the correlation between input stochastic variables. In this paper, correlation between near wind units is analyzed. To estimate the probabilistic density functions of bus voltages, one of the most precise methods (maximum entropy) is used. Finally, suggested method is applied on the IEEE 33-bus distribution test system and the results are examined. The results analysis demonstrates the accuracy and effectiveness of the proposed method. Due to the development of photovoltaic distributed generation (DG) in distribution networks, these DGs can impact on the operation, planning and reliability of system. In this context, this paper has used a probabilistic load flow algorithm to assess the effects of photovoltaic generation uncertainties on the distribution networks. This method is based on the cumulants. It does not need complex computations like convolution method or high computational burden like Monte Carlo simulation. Proposed methodology can evaluate the correlation between input stochastic variables. In this paper, correlation between near wind units is analyzed. To estimate the probabilistic density functions of bus voltages, one of the most precise methods (maximum entropy) is used. Finally, suggested method is applied on the IEEE 33-bus distribution test system and the results are examined. The results analysis demonstrates the accuracy and effectiveness of the proposed method. https://tjee.tabrizu.ac.ir/article_5981_e97482053a0f8f8903712df8a348329c.pdf