Stochastic Transmission Capacity Expansion Planning With Special Scenario Selection for Integrating $N-1$ Contingency Analysis

In this paper, we propose an iterative framework for solving the stochastic transmission capacity expansion planning (TCEP) optimization problem with N-1 contingency analysis. A three-level filtering algorithm is designed that uses developed important scenario identification index (ISII) and similar scenario elimination (SSE) technique to decrease the number of reliability constraints in stochastic TCEP in a systematic and trackable way. This filter decreases structural constraints in our TCEP mixed-integer programming formulation in each iteration (up to 99.8% in the first iteration), and the iterative framework adds reliability constraints into the optimization problem gradually in order to decrease the total simulation time. A lower bound is calculated to quantify the quality of results. To verify capabilities of the proposed method, it is applied to a reduced ERCOT system with 317 buses, 427 binary variables, and ten scenarios. The numerical result shows that the proposed method can solve large-scale stochastic TCEP optimization problems faster, and its relative performance improves with increasing number of scenarios and the system size.