Optimization of Voltage Model Predictive Control in Distribution Network Based on Intelligent Soft Switching

Nowadays, the operation of new energy power systems is becoming increasingly complex. This article constructs a state space or equivalent circuit model of the DN(Distribution Network), characterizes the voltage transfer and change patterns between nodes, and also collects real-time data, monitors and measures voltage, identifies and calibrates model parameters. On this basis, model predictive control algorithms can be used to predict and adjust voltage. In the test of average voltage and average absolute error under different control schemes, the average voltage of control scheme 1 (traditional control) is 10.2 V, and the average absolute error is 0.1 V; The average voltage value of control scheme 4 (intelligent soft switch control) is 10.4 V, with an average absolute error of 0.05 V. Through the research of this project, it is expected to reduce operating costs while ensuring the stability, reliability, and efficiency of the power grid, laying a solid foundation for building a smart grid.