Dynamic partitioning and evaluation of autonomous regions based on stochastic model predictive control under chance constraints

In order to obtain a reliable autonomous region partitioning scheme while taking into account the uncertainty of distributed energy output, this paper proposes a stochastic model predictive control-based dynamic partitioning and evaluation method for autonomous regions under chance constraints. First, the autoregressive integrated moving average (ARIMA) model is applied to forecast wind and PV power. Finally, the SMPC model was used to solve and evaluate the dynamic partitioning correction. Secondly, with the goal of minimizing the risk of load loss in autonomous regions, a chance constraint system for partitioning autonomous regions is established, and stochastic simulation techniques are used to solve the chance constraints. Finally, the optimization model for distribution network partitioning based on stochastic model predictive control (SMPC) is constructed, and the improved active modularity function is used to evaluate and correct the partitioning scheme. Validation is carried out through the modified IEEE33 node arithmetic simulation. The results show that the partitioning method can ensure the effective and reasonable partitioning results at a certain confidence level, and effectively ensure the power supply stability in autonomous regions.