Open AccessA Prediction Method of Wind Power In-Place Consumption Capacity
Author(s) -
Xiaoguang Chen,
Wenbo Hao,
Wei Guan,
Yuanting Hu,
Jiapeng Cui,
Jin Liu,
Shuang Rong
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1549/5/052024
Subject(s) - wind power , power (physics) , consumption (sociology) , environmental science , power consumption , wind speed , wind power forecasting , markov chain , nameplate capacity , meteorology , computer science , automotive engineering , electric power system , marine engineering , electricity generation , engineering , electrical engineering , geography , physics , quantum mechanics , social science , machine learning , sociology
In Heilongjiang Province, China, the generated wind power is mainly consumed in-place, the uncertainty of wind power variability and electric load lead the difficulty in wind power in-place consumption capacity prediction. In this paper a wind power in-place consumption capacity predition model based on scenario-markov chain simulation (SMCS) is proposed. The wind velocity and wind power output could be accurately predicted for the calculation of wind power in-place consumption capacity by the presented prediction model. The case study results of a real region power system verify effectiveness of the proposed model.