XUZHOU, China, Aug. 15, 2024 /PRNewswire/ — Recently, State Grid Xuzhou Power Supply Company, in collaboration with the local meteorological station, has successfully developed and implemented a photovoltaic power forecasting technology. This innovation not only addresses the issue of exceeding reverse load limits on the lines but also enhances the efficiency of photovoltaic power consumption.
It is reported that the Xuzhou area has over 3.6 million kilowatts of installed distributed photovoltaic capacity, ranking first in Jiangsu Province. The randomness, intermittency, and volatility of photovoltaic power generation present challenges to the electrical grid’s load balancing and regulation. Therefore, the accuracy of photovoltaic power forecasting is particularly crucial for flexible grid flow scheduling.
Since July last year, State Grid Xuzhou Power Supply Company has been developing photovoltaic power prediction technology based on geographic and meteorological conditions. This technology incorporates climatic factors such as the warm temperate monsoon climate and the transitional nature from north to south, along with effects of aerosol radiation, cloud radiation, terrain slope, and shadow. These factors collectively enhance the accuracy of photovoltaic irradiance predictions.
Moreover, by dividing Xuzhou into 115 virtual grid stations, a “meteorological eye” for photovoltaic power station forecasting has been formed, linking the entire region’s photovoltaic stations with grid points. The technology platform then uses historical power generation curves and other data for analysis, continually optimizing and adjusting the photovoltaic forecasting strategy. This allows for photovoltaic power forecasts of more than 10 days in advance, with 15-minute intervals, aiding grid operators in flexibly adjusting grid flow distribution.
Since the implementation of this new technology, the accuracy of the company’s photovoltaic power forecasting has improved by approximately 2.85%. This enhancement has enabled photovoltaic stations to increase green energy consumption by 400,000 kilowatt-hours per month through optimized maintenance planning and other measures.
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