In power system studies the unit commitment problem (UC) is solved to support market decisions and assess system adequacy. Simplifications are made to solve the UC faster, but they are made without considering the consequences on solution quality. In this study we thoroughly investigated the impacts of simplifications on solution quality and computation time on a benchmark set consisting of almost all the available instances in the literature. We found that omitting the minimum up- and downtime and simplifying the startup cost resulted in a significant quality loss without reducing the computation time. Omitting reserve requirements, ramping limits and transmission limits reduced the computation time, but degraded the solution significantly. However, the linear relaxation resulted in less quality loss with a significant speed-up and resulted in no difference when unserved energy was minimized. Finally, we found that the average and maximum capacity factor difference is large for all model variants.@en

In power system modelling the unit commitment problem is used to simulate the wholesale electricity market. A solution to the unit commitment problem is a least-cost schedule that contains information regarding the capacity factors of each generator, the total CO2 emissions, and unserved energy per hour. However, since there might be a large variety of (sub)-optimal solutions, these characteristics might be arbitrary and conclusions about them may be presumptuous.In this article, we illustrate this by running multiple experiments on a future European power system. Each scenario was run multiple times by adding additional terms to the objective function such as the minimization and maximization of generator capacity factors, carbon emissions, and loss of load hours. The results showed that schedules can be equivalent in terms of cost, but that relative capacity factors, emissions, and loss of load hours could differ by large factors.@en

The single-unit commitment problem (1UC) is the problem of finding a cost optimal schedule for a single generator given a time series of electricity prices subject to generation limits, minimum up- and downtime and ramping limits. In this paper we present two efficient dynamic programming algorithms. For each time step we keep track of a set of functions that represent the cost of optimal schedules until that time step. We show that we can combine a subset of these functions by only considering their minimum. We can construct this minimum either implicitly or explicitly. Experiments show both methods scale linear in the amount of time steps and result in a significant speedup compared to the state-of-the-art for piece-wise linear as well as quadratic generation cost. Therefore using these methods could lead to significant improvements for solving large scale unit commitment problems with Lagrangian relaxation or related methods that use 1UC as subproblem.@en