Frequency Constrained Unit Commitment
Résumé
The Unit Commitment (UC) problem deals with the short-term schedule of the electrical generation to meet the power demand. The main objective is to minimize production cost, while respecting technical and security constraints. In addition to the system load, a speci c amount of spare
capacity is committed to cope with uncertainties, such as forecasting errors and unit outages; this is called reserve and it has been traditionally specified following a static reliability criterion. In a system with a conventional generation mix, this security constraint allow achieving UC solutions that naturally provide an acceptable transient response. However, the increasing penetration of Variable Generation (VG) sources, such as wind and solar, can lead to UC solutions that no longer ensure system security. Thus, new UC models have been proposed to consider the power system dynamics when optimizing the day-ahead generation schedule. Some published works are focused on the formulation of these constraints
in a Mixed-Integer Linear Programming (MILP) structure to apply classic optimization techniques. Nevertheless, power system dynamics is a non-linear problem, and, to the author's knowledge, the limits of these linear approximations have not been discussed in literature. This work examines the ability of different UC models to produce secure schedules when facing unit outages, through the implementation
of a set of primary reserve & energy co-optimization models. These models are built based on linear approximations of dynamic constraints that are available in recent literature. Then, dynamic simulations are performed for every conceivable outage to observe the transient response of the system and to quantify the risk of Under Frequency Load Curtailment (UFLC). Depending on the energy mix and the dynamic parameters of the available production park, the system dynamic response can be improved at reasonable cost through slight changes in schedule and dispatch using adapted UC models. Further work will include net demand forecasting errors to determine the expected activation of UFLC.