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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.

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.

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Mitigation efforts to avoid dangerous effects of climate change are leading to a shift in electricity production towards low carbon and intermittent renewable energy sources. This shift, along with the increasing use of electric heating and transportation, introduces more variable generation and demand in the power system that are both influenced by weather patterns. To assess the adequacy of future power systems in Europe and identify potential problems, power system models are needed to simulate their operation under many weather scenarios. The unit commitment problem (UC) is a well-known optimization problem that is often used for simulating power system operation. Solving these UC problems is challenging since it is a hard optimization problem, and the size of the power system models makes solving it even harder. This results in incredibly high computation times which can be addressed by either increasing the efficiency of the algorithms or performing justified simplifications to the UC problem. This thesis improves power system modeling based on the UC by investigating the impact of modeling decisions, making algorithmic enhancements, and investigating the relationship between power system adequacy and weather regimes of Europe by performing power system modeling based on the UC.