ZHENJIANG, China, June 3, 2024 /PRNewswire/ -- Recently, the "Reference Station Assisted Selection Function" module, led by State Grid Zhenjiang Power Supply Company and developed by the State Grid Jiangsu Electric Power Research Institute, has been launched and implemented in the observable and measurable system for distributed photovoltaics in Jiangsu. Since then, the estimation accuracy for 26,000 low-voltage distributed photovoltaic users in one city and three counties in Zhenjiang has exceeded the standard value of 95%.
By the end of April, the installed capacity of low-voltage distributed photovoltaics in Zhenjiang exceeded 1 million kilowatts. In recent years, with the rapid growth in the total installed capacity of low-voltage distributed photovoltaics, to effectively address the adverse impacts of large-scale integration of distributed photovoltaics on power system balance and equipment overload, State Grid Jiangsu Electric Power established a provincial-level observable and measurable system for distributed photovoltaics in 2022, achieving minute-level real-time observation of distributed photovoltaic output at the county level and above. However, this significantly relies on dispatchers' selection of photovoltaic power stations at medium voltage and above, resulting in low efficiency and inaccuracies due to blind selection of photovoltaic reference stations and deviations in empirical judgments.
To address these issues, State Grid Zhenjiang Power Supply Company has made full use of data mining technology and entrusted the Electric Power Research Institute of State Grid Jiangsu Electric Power Co., Ltd. to launch and implement the "Reference Station Assisted Selection Function Module" by the end of April this year. This module integrates various operational data of photovoltaic power stations and embeds the latest algorithms of the provincial-level observable and measurable system for distributed photovoltaics. It can retrieve historical photovoltaic output analysis and real-time output observation results based on dispatchers' actual needs. Through in-depth analysis of vast amounts of historical data, the system intelligently evaluates and calculates the matching degree of medium- and low-voltage photovoltaic power stations, providing dispatchers with powerful decision support to adjust photovoltaic reference stations more accurately and efficiently, achieving large-scale optimized management.
