A new approach to security-constrained generation scheduling of large-scale power systems with a piecewise linear ramping model

This paper presents a new and efficient approach to determine security-constrained generation scheduling (SCGS) in large-scale power systems, taking into account dispatch, network, and security constraints in pre and post-contingency states. A novel ramp rate limit is also modeled as a piecewise linear function in the SCGS problem to reflect more practical characteristics of the generating units. Benders decomposition is applied to this constrained solution process to obtain an optimal SCGS problem based on mixed-integer nonlinear programming (MINLP). The formulation can be embedded in two stages. First, a MIP is formulated in the master problem to solve a unit commitment (UC) problem. This stage determines appropriate on/off states of the units. The second stage, the subproblem, is formulated as a NLP to solve a security-constrained economic dispatch (SCED) problem. This stage is used to determine the feasibility of the master problem solution. It provides information to formulate the benders cuts that connect both problems. The proposed approach is tested in the IEEE 118-bus system to show its effectiveness. The simulation results are more realistic and feasible, whilst assuring an acceptable computation time.

► We develop a piecewise linear ramping process model for solving security-constrained generation scheduling (SCGS) problem.
► A mixed-integer nonlinear programming (MINLP) is used to solve the SCGS problem in large-scale power system.
► MINLP-based SCGS problem is formulated as a benders decomposition scheme for the computational efficiency.
► Proposed approach provides a trade-off system security and economic efficiency, and computation time.
► The simulation results leads to realistic and feasible signals to the power producer and independent system operator.