Abstract
One of the key attributes that distinguishes bottom-up energy modelling frameworks is the temporal depiction. In any given bottom-up model, the depiction across two dimensions—viz. model time horizon and intra-annual time resolution—has an implicit meaning for the framework and research questions to be answered. There are also tradeoffs between these two temporal dimensions in model design driven by computational resources, solver algorithm capabilities, data availability and methodological limitations. In the TIMES framework, the option to apply a higher intra-annual time resolution offers the potential to generate additional powerful insights into the electricity sector where fluctuations in supply and demand are significant, even though this feature alone is still less suitable for analyzing fully the dynamics of the sector. Nonetheless, the TIMES integrated system approaches offer additional capabilities which are not available in single-sector modeling approaches. This chapter provides a broad overview of temporal features in the MARKAL/TIMES energy modelling framework. The significance in terms of higher time resolution, along with trade-offs and benefits of an integrated system approach are discussed with a set of scenarios from the Swiss TIMES electricity and energy system models.
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Notes
- 1.
The 288 hourly timeslices of STEM-E are aggregated into eight timeslices (two diurnal timeslices viz. Day and Night in four seasons and the representation of different days of the week is removed).
- 2.
A self-sufficiency constraint is introduced requiring that net electricity trade is roughly in balance over the year, but the timing of electricity trade is left unconstrained.
- 3.
It should be noted that the availability of cheap electricity during the early morning is an assumption applied in the analysis (based on historical electricity import prices), which may change if the demand profiles in electricity trading partners were to vary substantially (for example, as a result of charging of BEVs).
- 4.
The computational time includes both model generation and solution time using CPLEX solver in 8-cores Intel processor with 24 GB RAM.
- 5.
For example, some specific CPLEX/Barrier options for improving the performance of the algorithm includes BarColz, BarEpComp, BarOrder, BarStartAlg, etc. However, the use of CPLEX/Barrier requires large memory, which can be addressed with solver options like MemoryEmphasis, Names, WorkMem to conserve memory (GAMS 2014). Numerical difficulties can occur during the optimisation if large LP problems are ill-defined due to large differences in the magnitude of coefficients in an equation. In such a case, the solver reports the problem as unscaled infeasibility. This can be diagnosed by GAMSCHK option described in Bruce (2013) and accordingly input data has to be adjusted to avoid large numerical differences in coefficients.
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Kannan, R., Turton, H., Panos, E. (2015). Methodological Significance of Temporal Granularity in Energy-Economic Models—Insights from the MARKAL/TIMES Framework. In: Giannakidis, G., Labriet, M., Ó Gallachóir, B., Tosato, G. (eds) Informing Energy and Climate Policies Using Energy Systems Models. Lecture Notes in Energy, vol 30. Springer, Cham. https://doi.org/10.1007/978-3-319-16540-0_11
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