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 paper proposes a novel probabilistic assessment method to assess the available transfer capability (ATC). Usually, a large number of ATC evaluations is needed to obtain accurate results using time-consuming Monte Carlo simulations (MCS). To alleviate the computation burden of probabilistic ATC, a statistically-equivalent surrogate model for the ATC solution is constructed by introducing 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 in an efficient way. With the proposed method, the probability of 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.