Probabilistic load flow calculation with quasi-Monte Carlo and multiple linear regression |
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| Affiliation: | 1. School of Electrical Engineering, Wuhan University, Wuhan 430072, China;2. State Grid Gansu Electric Power Research Institute, Lanzhou 730070, China;3. State Grid Fuzhou Electric Power Supply Company, Fuzhou 350009, China;1. Department of Electric Power Engineering, Norwegian University of Science and Technology, Trondheim, Norway;2. Department of Automatic Control, Lund University, Lund, Sweden;3. Electric Power Systems Department, KTH Royal Institute of Technology, Stockholm, Sweden;1. Electric Engineering College, Shanghai University of Electric Power, China;2. Department of Electrical Engineering, The Hong Kong Polytechnic University, Hong Kong;3. Faculty of Engineering, University of Strathclyde, Glasgow, Scotland, UK;1. School of Electrical Engineering, Southwest Jiaotong University, Chengdu, Sichuan 610031, People’s Republic of China;2. School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou, Gansu 730070, People’s Republic of China |
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| Abstract: | In this paper, quasi-Monte Carlo combined with multiple linear regression (QMC-MLR) is proposed to solve probabilistic load flow (PLF) calculation. A distinguishing feature of the paper is that PLF is approached by a low-dimensional problem with the concept of the effective dimension, and thus QMC based on low-discrepancy sequences is used to improve the sampling efficiency of the Monte Carlo simulation (MCS). Moreover, according to the relationship between linear correlation and linear regression, the MLR-based correlation control technique is developed to arrange the orders of samples in order to introduce prescribed dependences between variables. The proposed method is tested with the IEEE 118-bus system. Simulation results indicate that the MLR-based technique is robust and efficient in handling correlated non-normal variables and the proposed method shows better performances in PLF calculation compared with other MCS techniques, including simple random sampling (SRS), Latin hypercube sampling (LHS) and Latin supercube sampling (LSS). |
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| Keywords: | Correlation Multiple linear regression Non-normal distribution Probabilistic load flow Quasi-Monte Carlo Wind power |
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