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Day-Ahead Probabilistic Load Flow Analysis Considering Wind Power Forecast Error Correlation
Power Automation Department, China Electric Power Research Institute, Beijing, China
Power Automation Department, China Electric Power Research Institute, Beijing, China
Power Automation Department, China Electric Power Research Institute, Beijing, China
Power Automation Department, China Electric Power Research Institute, Beijing, China
Power Automation Department, China Electric Power Research Institute, Beijing, China
National Electric Power Dispatch and Control Center, State Grid Corporation of China, Beijing, China 3Electrical and Electronic Engineering College, Zhejiang University, Hangzhou, China
Electrical and Electronic Engineering College, Zhejiang University, Hangzhou, China
- 1 Power Automation Department, China Electric Power Research Institute, Beijing, China
- 2 Power Automation Department, China Electric Power Research Institute, Beijing, China
- 3 Power Automation Department, China Electric Power Research Institute, Beijing, China
- 4 Power Automation Department, China Electric Power Research Institute, Beijing, China
- 5 Power Automation Department, China Electric Power Research Institute, Beijing, China
- 6 National Electric Power Dispatch and Control Center, State Grid Corporation of China, Beijing, China 3Electrical and Electronic Engineering College, Zhejiang University, Hangzhou, China
- 7 Electrical and Electronic Engineering College, Zhejiang University, Hangzhou, China
Energy and Power Engineering·Volume 09 (2017)·Pages 292–299·Published 6 April 2017·DOI10.4236/epe.2017.94B034
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Abstract
Short-term power flow analysis has a significant influence on day-ahead generation schedule. This paper proposes a time series model and prediction error distribution model of wind power output. With the consideration of wind speed and wind power output forecast error’s correlation, the probabilistic distributions of transmission line flows during tomorrow’s 96 time intervals are obtained using cumulants combined Gram-Charlier expansion method. The probability density function and cumulative distribution function of transmission lines on each time interval could provide scheduling planners with more accurate and comprehensive information. Simulation in IEEE 39-bus system demonstrates effectiveness of the proposed model and algorithm.
KeywordsWind PowerTime Series ModelForecast Error DistributionForecast Error CorrelationProbabilistic Load FlowGram-Charlier Expansion
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