Since the probabilistic pwer flow is vulnerable to the scale of the power grid
the conventional probabilistic power flow algorithm based on cumulant method is improved. When the amount of random variables is small
the computational complexity is reduced by calculating the partial sensitivity matrix. When the amount of random variables is big
the computational scale of power grid is reduced by the network equivalent and decomposition methods. These modification can improve the computational speed of cumulant method of the probabilistic power flow. The accuracy and practicality are both testified by the IEEE systems and several actual large-scale power grid cases.
Probabilistic Power Flow Method Based on Arbitrary Probability Distribution Modeling and Improved Polynomial Chaos Expansion
Holomorphic Embedding Probabilistic Power Flow Calculation Method for AC/DC System Based on Polynomial Chaos Expansion
Bayesian Theory Based Calculation of Probabilistic Power Flow Considering Correlation Between Multi-dimensional Wind Speed
Related Author
LI Guoqing
JIANG Tao
ZHAO Shukang
LI Xue
LU Juanjuan
SHAN Xin
WANG Yi
LI Kemeng
Related Institution
Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education (Northeast Electric Power University)
NARI Group Corporation(State Grid Electric Power Research Institute)
NARI Technology Co., Ltd.
School of Electrical Engineering, Southeast University
School of Electronic Science and Engineering, Southeast University