Risk Quantification Based Intraday Flexibility Resource Adjustment Strategy

This paper proposes a risk quantification based intraday scheduling theory for active distribution networks (ADNs) to solve the increasing volatility of renewable energy and the difficulties of distribution network safety. The article employs the Latin Hypercube Sampling (LHS) algorithm to sample the data, and the historical data is sampled to construct a full cycle probability trend sequence, in order to screen out weak nodes/branches of ADN. Thus, in the intraday stage, the safety risks experienced in each period were quantified based on predicted data, and the periods with smooth source load fluctuations and low risk probabilities were identified. The model solution step size was dynamically adjusted to shorten the calculation time. Further accelerate the online solution process of the model and adapt to the fast solution of large-scale complex risk scheduling. Finally, simulation was conducted on the IEEE33 node distribution system to obtain a flexible resource adjustment scheme.