About Characteristics of wind and photovoltaic power generation
After establishing a wind and solar power output correlation model based on the Copula function and Markov chain, this paper uses the Monte Carlo method to simulate the generation of wind power output and photovoltaic output sequences that meet the spatiotemporal correlation relationship.
After establishing a wind and solar power output correlation model based on the Copula function and Markov chain, this paper uses the Monte Carlo method to simulate the generation of wind power output and photovoltaic output sequences that meet the spatiotemporal correlation relationship.
By analyzing and utilizing the wind and PV power prediction results, we can optimize the matching calculation of the wind and solar complementary power generation system to obtain a better power generation effect.
Here we develop a rule-of-thumb statistical learning model for wind and solar power prediction and generate a year-long dataset of hourly prediction errors of 30 provinces in China.
Meanwhile, the fluctuation characteristics of wind power and photovoltaic power under different output levels are considered. Finally, the Monte Carlo method is used to iteratively simulate a large number of wind and photovoltaic output sequences, and clustering generates typical scenes of wind and photovoltaic output.
Wind speed and solar radiation are the most important factors for generating wind and solar power, respectively. The Pearson correlation coefficient (PCC) is a measure of linear correlation.
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