This research explores the integration of start-up and shut-down trajectory constraints into the Tulipa energy system optimisation model, which uses fully-flexible temporal resolution. These constraints aim to more realistically represent the behaviour of large thermal generators during operation. Case studies with varying time resolutions and generator configurations were evaluated to measure impacts on computation time and solution accuracy. Results show a significant increase in computation time with small gains in accuracy, compared to introduction of minimal down-time constraints.

This paper extends the Tulipa energy system optimisation model by incorporating start-up and shut-down capability constraints formulated for Tulipa's fully flexible temporal resolution. The impact of adding these constraints for thermal generators is assessed using a greenfield case study with 7 European countries. Results show that including these constraints increases computation time, but they more realistically represent generator behaviour, which also results in a higher objective function value. Cases where the resolution of assets is not a multiple of the resolution of flows result in uniquely long solving times. The investments, as well as the unit operation trends remain similar on a high level. Batteries are utilised to improve the reduced flexibility, and units with the most flexible start-up/shut-down capabilities become used slightly more often, while the opposite holds for those with the least flexible capabilities. Units also tend to be turned on and off less often. This research contributes to understanding the trade-offs between model complexity and runtime in long-term energy planning.

The transition of the energy grid into a system with higher shares of renewable energy production requires careful investment planning while considering operational characteristics of generators. Generation Expansion Planning (GEP) is used for finding optimal investments, while Unit Commitment (UC) can be used to limit generator operational capabilities, such as via ramping limits, for more accurate modelling. The system may be extended to model custom thermal generator capabilities at the time of their start-up or shut-down, where they may differ from traditional ramping, promoting more precise modelling. However, the inclusion of such detail requires careful managing of model complexity to keep solving time feasible, which can be done with techniques such as Clustered Unit Commitment (CUC), and clustering time blocks while allowing flexible resolution combinations. The latter is known as Fully Flexible Temporal Resolution, and promises managing of model complexity via temporal resolution reductions, while maintaining modelling versatility. The paper targets the unexplored area of including Start-Up and Shut-Down (SU/SD) capability ramping limits alongside a fully flexible temporal resolution in a GEP & CUC model, and contributes by examining the effect of the new capabilities on the run times, investment and operational solutions, and the total system cost of a model with enabled Battery Energy Storage System (BESS) investments. The resulting findings show that the inclusion of the SU/SD capabilities has little effect on the investments and total cost of the model, significantly increases computation time, yet has a noticeable effect on the operational schedule of generators.

Fully flexible temporal resolutions have shown to be a useful tool for improving the tradeoff between the runtime and accuracy of generation expansion planning models. However, no research has been done into the effects of considering short-term operational dynamics of thermal generators in models with such resolutions. Therefore, this paper complements the existing literature by adding start-up and shut-down costs to a large-scale energy system optimisation model with a fully flexible temporal resolution. The results suggest that the addition of start-up and shut-down costs significantly increases the runtime of the model, while providing a small increase in accuracy. Additionally, they show that a compact set of start-up and shut-down constraints outperforms a full set of constraints in terms of model runtime, while having the same accuracy.

In recent literature fully flexible temporal resolutions have been proposed as a new form of temporal clustering in generation expansion planning models, showing promising benefits in terms of the tradeoff between solution accuracy and computation time. However, unit commitment constraints such as minimum up and down times have not yet been considered in combination with these resolutions. This paper introduces minimum up and down time (MU/MD) constraints to fully flexible time resolutions and shows the effects of including them when doing generation expansion planning. This is done by constructing a case study based on the European energy grid and comparing the effects of adding MU/MD constraints to a model with a fully flexible time resolution in the form of the geographically decreasing resolution. The paper shows that the addition of minimum up and down time constraints maintains the benefits of fully flexible time resolutions, but comes with additional computation time and also has little effect on the optimal solution cost for the case study examined.