Frequency constrained unit commitment

Abstract

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 the production cost, while respecting technical and security constraints. In addition to the system load, a specific 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 allows one to obtain UC solutions that naturally provide an acceptable transient response. However, the increasing penetration of variable generation sources, such as wind and solar, can lead to UC solutions that no longer ensure system security. Thus, enhanced security constraints have been proposed to consider the power system dynamics when optimising the dayahead generation schedule. Some published works are focused on the formulation of these constraints in a mixed-integer linear programming structure to apply classic optimisation techniques. Nevertheless, power system dynamics is a non-linear problem, and, to the authors' 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 B Carmen Cardozo 123 C. Cardozo et al.of primary reserve and energy co-optimisation 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 a test system and to quantify the risk of under frequency load shedding. It is shown that the system's need for frequency regulation depends on the operating point and that enforcing a fixed reserve, inertia or maximal frequency slope constraint is not cost/effective to limit the dynamic risk. If used, these values should be set on an hourly basis by iteration between the optimisation and the dynamic simulator.

Optimal Energy Management in Autonomous Power Systems With Probabilistic Security Constraints and Adaptive Frequency Control

“…The challenge of leveraging optimal system operation and security in APSs was also addressed recently, being alternatives presented to the deterministic evaluation of the worstcase APD or the N-1 criterion. References [13], [21]- [24], for instance, considered the effect of anticipated net load variations and applied dynamic constraints for sizing frequency containment reserves and required inertia. These works did not evaluate however the impact that re-adjusting the droop of online units, instead of switching on and off units with fixed droops, would have on frequency stability and OPEX.…”

Optimal Energy Management in Autonomous Power Systems with Probabilistic Security Constraints and Adaptive Frequency Control

“…The challenge of leveraging optimal system operation and security in APSs was also addressed recently, being alternatives presented to the deterministic evaluation of the worstcase APD or the N-1 criterion. References [13], [21]- [24], for instance, considered the effect of anticipated net load variations and applied dynamic constraints for sizing frequency containment reserves and required inertia. These works did not evaluate however the impact that re-adjusting the droop of online units, instead of switching on and off units with fixed droops, would have on frequency stability and OPEX.…”

Large-scale unit commitment under uncertainty: an updated literature survey

“…Obvious downsides of such an approach is that one cannot ensure satisfaction of the original constraint, nor control suboptimality. Directly accounting for frequency related constraints in UC models can be done through a very simplified version of the differential equation system governing the loss of frequency following a contingency (e.g., [499]); we refer to [95,98] for a thorough account of different approaches and extensive tests. A more precise approach can also be designed, albeit under some theoretically hard to verify assumptions [97]; the resulting UC models can be solved by Benders-like scheme [96] exploiting the convexifying effect of Lagrangian relaxations [296].…”