|
|
Introducing stochasticity into the energy system model Times-CZ - a reflection of a war-related extreme environment
Otruba, Šimon ; Rečka, Lukáš (advisor) ; Janda, Karel (referee)
This thesis introduces stochastic elements into the TIMES-CZ energy system model focusing on the impact of extreme events such as pandemic or recent war in Ukraine. The objective is to improve the model's precision in the face of these market uncertainties. Natural gas prices and European Union Allowance (EUA) prices, after a selection process, are represented as random variables allowing for probabilistic forecasting. These variables are derived from an analysis that combines model-based forecasts, which also include external predictions. The results of this comprehensive analysis are then integrated into the TIMES-CZ model. The correctness of these results is validated using sensitivity analysis, which evaluates the impact of results with uncertain parameters on the model's output. The findings highlight the importance of including uncertainty in energy systems modelling and could have implications for energy planning and decision-making in uncertain contexts. Keywords TIMES-CZ Model, Stochasticity, Energy System Modelling, Uncertainty Analysis, Sensitivity Analysis JEL Classification C12, C33, G21, L25, M31 Title Introducing stochasticity into the energy system model Times-CZ - a reflection of a war- related extreme environment
|
|
|
Impact of increased temporal detail in long-term dynamic energy system model with an increasing share of volatile renewable energy sources
Švec, Josef ; Rečka, Lukáš (advisor) ; Janda, Karel (referee)
Long-term energy systems often simplify temporal detail resolution. However, this simplification is not appropriate for systems with an increasing share of intermittent renewable energy sources (IRES). The low temporal resolution causes a systemic bias in the model's outputs. The model overpredicts the share of the IRES on the total generation. This entails an underprediction of the total system costs, underprediction of the amount of greenhouse gas emissions and a bias in the energy mix. This could lead to an ill-informed energy policy and eventually cause a failure in the decarbonization of the energy sector. This thesis increases the temporal resolution of the TIMES-CZ model from 12 seasonal times slices and 3 daynite time slices to 24 daynite time slices and compares 8 scenarios with 1, 4 and 12 seasonal time slices. This thesis compares three representative days selection methods - simple heuristics, hierarchical clustering centroid selection method and historical day closest to the centroid selection method. The electricity generation of IRES and the total production in base year (2012) is the most accurately approximated by the 12 seasonal time slices selected by the hierarchical clustering centroid method.
|
guest ::
