Research on Multi-source Data Assimilation Technology for New Energy Power Generation Prediction

In the high proportion of new energy power system, wind power, photovoltaic and other power generation methods are more affected by geographical and meteorological conditions, there is a large volatility and meteorological elements on the power system influence is becoming more and more prominent. Considering the influence of the sea-air-wave coupling process, this paper establishes a double-nested numerical weather prediction system based on multi-source real-time ocean-atmosphere observations and refined numerical prediction models by studying the construction and operation techniques of the offshore wind resource forecasting model to enhance the forecast accuracy of atmospheric elements, and realizes the refined forecast of wind resource forecasting by using the micro-meteorological scale model. Meanwhile, the ground-based solar irradiance technology based on the atmospheric radiation transport model is studied, and a gridded in situ forecast model of light resources is constructed to realize the refined forecast of solar irradiance. Based on more accurate meteorological forecast data, it provides strong support for new energy power generation prediction, improves the accuracy of new energy power generation prediction, promotes the consumption of new energy, and guarantees the safe and stable operation of the distribution network.