The aim of the thermal unit commitment problem is to decide which of the available generating units in a power system will be used to cover
the demand and reserve requirements at a minimum cost. The solution of the UC problem has to satisfy the operational constraints of the
generating units, with typical planning horizons ranging from one day to a couple of weeks. The state–of–the–art approach to tackle the
UC problem is based on monolithic mixed–integer programming (MLP) formulations, which are solved solved via a particular branch & bound (BB)
scheme. For example, it has been reported by the PJM interconnection that the MIP–BB approach has been able to improve the UC problem's solution
by about 1% when compared against strategies based on Lagrangian relaxations (LR). Every percent of improvement translates into millions of
dollars of savings, thus the UC problem continues to be a topic of active research.
In order to evaluate the performance of different formulations of this problem, we have built a set of 162 daily instances based on code developed by Frangioni RCUC.zip, which was modified to write the output files in AMPL format. All instances include the following parameters: number of thermal generation units (NG), number of hourly periods (NP), unit's id (IDG), initial state (UINI), initial power generation (PINI), rampup limit (GRADS), rampdown limit (GRADB), minimum up time (TMINON), number of hours on at init (TONINI), minimum down time (TMINOFF), number of hours off at init (TOFFINI), cuadratic coefficient of generation cost (A), linear coefficient of generation cost (B), fixed part of generation cost (C), minimum power (PMIN), maximum power (PMAX), shutdown cost (SDCOST), startup power (PSU), suthdown power (PSD), number of startup cost stages (NSUC), startup cost pairs (SUTIME,SUCOST), hourly demand (DEMANDA) and hourly reserve level (RESERVA). Regarding the number of generators involved, instances were divided into three groups:
DOWNLOADS: Here you can download our instances in AMPL format.

Type of instances 
Instances 

Small size 
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Medium size 
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Large size 
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