Transient Stability‐Constrained Unit Commitment (TSCUC)

Abstract

Traditional security-constrained unit commitment (SCUC) only considers static security criteria, which may however not ensure the ability of the system to survive dynamic transition before reaching a viable operating equilibrium subjected to a large disturbance, such as transient stability. This chapter proposes a novel and tractable mathematical model for transient stability-constrained unit commitment (TSCUC) and a practical solution approach, which is applied to efficiently solve the model. For this model, it does not include any explicit differential-algebraic equations (DAEs); thus, the problem size is significantly reduced and can be readily handled. Based on the prevailing SCUC solution algorithm, the approach is derived from a decomposition framework, where the master problem is to solve a basic UC model so that the unit status and the generation output can be determined, and the slave subproblems are to check the feasibility, including both network steady-state security evaluation (NSE) and transient stability assessment (TSA), and generate additional constraints for the master problem to retrieve the security/stability. In order to solve the master problem, the mixed-integer programming (MIP) method is applied here since it outperforms Lagrangian relaxation (LR) in that high-performance commercial solvers can be employed and higher-quality solutions can be usually obtained. For the slave subproblems, the EEAC method is applied to handle the transient stability constraints. According to EEAC, the transient stability can be quantitatively constrained and the stability control can be analytically derived and formulated as linear constraints (called stabilization cuts in this chapter). By doing this, the TSCUC is constructed very similar to a standard SCUC problem. In the meantime, contingencies that have common instability modes can be simultaneously stabilized, namely, one stabilization cut is to stabilize multiple contingencies to reduce the dimension of the problem.