In December 2020, affected by the cold wave and extreme low temperature, the output of regional large-scale wind power clusters fluctuated significantly. The maximum fluctuation of wind power in the whole grid accounted for 37% of the total installed capacity, which seriously threatened the safe and stable operation of the grid. In order to explore the generation mechanism of this large-scale wind power cluster output fluctuation process, this paper uses conventional terrain data and 1°× 1°reanalysis data ( provided by NCEP/NCAR) to analyze the weather situation, meteorological element characteristics and relevant physical quantity fields of this process. The results show that in the early stage of the process, low heat pressure and inverted trough develop on the ground, and the ground temperature rises significantly. Later, the mid latitude ladder trough guides the northern cold air to the south, and the cold high on the ground develops to the intensity of cold wave. The narrow pipe effect of terrain accelerates the movement of cold air, and the range of surface cooling increases significantly. In addition, there is a significant correlation between the change process of surface temperature and relative humidity before the event and this large-scale wind power abnormal operation event. Before the cold wave, the near surface temperature rises, the relative humidity increases, and the atmosphere is in a state of easy saturation. This provides favorable background conditions for the rise and condensation of water vapor in the cold wave weather, leading to a large number of wind turbine operating condition alarms in hilly areas, and significant changes in the output of the whole grid wind power cluster in a short time.
KEYWORDS: Photovoltaics, Solar radiation models, Data modeling, Atmospheric modeling, Solar radiation, Solar cells, Signal attenuation, Meteorology, Error analysis, Neural networks
Prediction technology can overcome the shortcomings of random and intermittent of photovoltaic power, which is of importance for the large-scale PV integration and grid scheduling. Firstly, the mathematical description of output wave momentum of photovoltaic power stations is obtained based on satellite data, and then the cluster analysis of photovoltaic power stations in provincial power grid is carried out, and the spatial correlation characteristics of photovoltaic power stations are summarized. Then, the regional photovoltaic power forecasting model based on K-means clustering and long short-term memory model is proposed.
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