中图分类号：TP391；TM734 文献标志码：A DOI: 10.16157/j.issn.0258-7998.257141 中文引用格式： 刘永成，杜晟磊，孙亚璐，等. 智能配电网源网荷储有功功率快速调节方法研究[J]. 电子技术应用，2026，52(8)：87-91. 英文引用格式： Liu YongCheng，Du Shenglei，Sun Yalu，et al. Research on the fast active power vegulation method for source-grid-load-storage in smart distribution network[J]. Application of Electronic Technique，2026，52(8)：87-91.
Research on the fast active power vegulation method for source-grid-load-storage in smart distribution network
Liu YongCheng，Du Shenglei，Sun Yalu，Li Ding，Jin Qin
Institute of Economic Technology State Grid Gansu Electric Power Company
Abstract： The source network load storage active power regulation is mainly achieved by the real-time collection of wind power and load data, combined with the fixed threshold value to adjust the power of each link of the source-grid-load-storage. The absence of refined detail partitioning for wind-PV output scenarios prevents the regulation strategy from adapting to variable natural conditions, resulting in poor regulation stability. To address this issue, a fast active power regulation method for source-grid-load-storage in smart distribution networks is proposed. The DBSCAN density clustering algorithm is employed to perform scenario clustering on historical wind-PV output data. By mining output patterns across different time periods, seasons, and climate conditions, the continuous wind-PV data are discretized into a set of typical scenarios. The state transition probability matrix is defined based on the scenario clustering results, and the Markov algorithm is adopted to characterize the dynamic evolution of wind-PV output and load demand, thereby enabling accurate short-term power deficiency prediction. The consensus algorithm is applied to model the source, grid, load, and storage as interconnected agents with communication capabilities. Taking the power demand prediction result as the overall power target, the agents iteratively update the power allocation values through local information exchange. In the experiments, the regulation stability of the proposed method is examined. The final test results indicate that when the proposed method is applied for power regulation, the peak-valley difference variation rates of the algorithm are all below 13%, demonstrating a relatively ideal regulation performance.
Key words : smart distribution network；source-grid-load-storage；active power；fast regulation；consistency algorithm
