A Deterministic Approach for Probabilistic TTC Evaluation of Power Systems Including Wind Farm Based on Data Clustering

Transfer capability of an electric transmission network indicates the maximum real power that can be exchanged between two areas in a reliable manner. This index is used in power system planning, operation and marketing. Transfer capability depends on the system state and is not a fixed value. Utilization of energy resources with stochastic nature adds new probabilistic dimension to the power system and makes total transfer capability (TTC) study more complex. Wind is one of the important alternative renewable energy resources for electric energy production but its speed is stochastic in nature. Increasing penetration of these resources is a motivation for research to study different aspects of this issue. This paper proposes a method for probabilistic evaluation of TTC in the presence of wind farms. It proposes a hybrid method based on data clustering and contingency enumeration. Contingency enumeration is done using two contingency lists (CLs). Single and double contingencies are established the first CL and the second CL considers a number of contingencies from the first CL. Monte Carlo simulation (MCS) is also used to assess probabilistic variation of TTC and to verify the proposed method. The obtained probabilistic TTC results have acceptable relative error compered to MCS results. Especially when the probabilities of contingencies are low the second approach works very well. The efficiency of the proposed method is investigated by conducting numerical studies on the IEEE Reliability Test System.