Probabilistic available transfer capability assessment in power systems with wind power integration

Extending current deterministic tools to incorporate significant stochastic wind power is becoming an important as well as challenging task for present-day power system decision-making. This study proposes a novel probabilistic assessment method to assess the available transfer capability (ATC). Usually, repeated ATC evaluations with an exhaustive set of samples are needed to obtain converged results by the Monte Carlo simulation. To alleviate the computation burden, a statistically-equivalent surrogate model for the ATC solution is constructed based on the canonical low-rank approximation (LRA). By implementing LRA for the base case and a set of enumerated contingencies, the uncertainties of wind power generation and load, as well as transmission equipment outages, are addressed efficiently. With the proposed method, the probabilistic ATC is characterised, and the most influential uncertain factors are identified, which helps to determine a suitable ATC level. The effectiveness of the proposed method is validated via case studies with a modified IEEE 118-bus system.