Probabilistic Available Transfer Capability Evaluation Considering Dynamic Line Rating Based on a Sequential Game-Theoretic Approach

Abstract

Available transfer capability (ATC) plays an essential role in deregulated power network scheduling. This value determines the power that can be transferred between deregulated regions. The ATC value is limited by transmission line constraints and reliability marginal terms. In this article, the impact of dynamic line rating (DLR) instruments, which are generally used to enhance the capacity of transmission lines, on the ATC is investigated. In addition, a robust ATC evaluation model is proposed to find worst cases for determining the reliability marginal terms and considering them in the ATC evaluation. The robust ATC evaluation model includes a master problem, formulated to determine ATC and total transfer capability, as well as two bilevel subproblems, formulated to find the reliability marginal terms. The Benders decomposition algorithm is derived to solve the proposed robust ATC evaluation. An uncertain environment, including wind farm powers, dynamic line capacities, and load demand values, is also considered to cover the stochastic nature of such uncertainty sources. A probabilistic approach is proposed to calculate the expected value of ATC and depict the cumulative distribution function of each variable. The proposed probabilistic model is based on dividing the stochastic set into smaller groups, similar to clustering techniques. However, the significant differences between the proposed probabilistic and cluster-based models are due to detecting the optimal value of a reduced number of stochastic sets and finding the members of each cluster set. In other words, a sequential game-theoretic approach is applied to divide the data into smaller sets. The impact of DLR on the proposed robust ATC evaluation problem and the efficiency of the proposed probabilistic model are evaluated by comprehensive studies based on the IEEE 118-bus test system.