About Solar power generation Liu
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6 FAQs about [Solar power generation Liu]
How can a prediction of photovoltaic power generation benefit China?
Prediction of photovoltaic power generation can effectively mitigate the influences of meteorological and other factors on solar power stations, thereby enabling the efficient deployment of solar energy resources in China.
Who is yongqian Liu?
[...] [...] Dr. Yongqian LIU, Professor of Wind Power System, New Energy School, North China Electric Power University, Beijing, China. Currently his main research, teaching, and consultation interests are the theory and technologies on Wind Power Plant， including Design, Intelligent Control, Operation and Maintenance.
Will China's photovoltaic power capacity exceed wind power capacity?
Considering China's policies and future land use projections, Liu et al. believe that China's deployable photovoltaic (PV) power capacity will exceed wind power capacity in the future, reaching 2674 GW . In recent years, China's installed capacity of renewable energy has been increasing steadily.
How to predict the future power generation of PV power station?
Leveraging the NEX-GDDP-CMIP6 data, the study constructed the Vine Copula multi-model ensemble downscaling model. On this basis, the future power generation of PV power station for 2025–2034 was predicted using the future meteorological data provided by the downscaling model. Both models constructed for the PV power station have high accuracy.
Will climate conditions affect PV power generation in the future?
Under the influence of future climate conditions, the average annual power generation of the PV power station are projected to be higher in the future period compared to the average annual power generation in the historical period.
How many MWP is a PV power station in Yunnan Province?
The total installed capacity of a PV power station in Yunnan province (Fig. 1) studied in this paper is 40 MWp, and the power generation data spans from June, 1, 2018, to May, 31, 2021, with a data frequency of 15 min. The power generation data is converted into daily average data for constructing the long-term power generation prediction model.
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