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Power system expansion models are a widely used tool for planning powersystems, especially considering the integration of large shares of renewableresources. The backbone of these models is an optimization problem, whichdepends on a number of economic and technical parameters. Although theseparameters contain significant uncertainties, the sensitivity of power systemmodels to these uncertainties is barely investigated. In this work, we introduce a novel method to quantify the sensitivity ofpower system models to different model parameters based on measuring theadditional cost arising from misallocating generation capacities. The value ofthis method is proven by three prominent test cases: the definition of capitalcost, different weather periods and different spatial and temporal resolutions.We find that the model is most sensitive to the temporal resolution. Fur-thermore, we explain why the spatial resolution is of minor importance andwhy the underlying weather data should be chosen carefully.
The Vietnamese Power system is expected to expand considerably in upcoming decades. However, pathways towards higher shares of renewables ought to be investigated. In this work, we investigate a highly renewable Vietnamese power system by jointly opt
Deep decarbonization of the electricity sector can be provided by a high penetration of renewable sources such as wind, solar PV and hydro power. Flexibility from hydro and storage complements the high temporal variability of wind and solar, and tran
A fully renewable European power system comes with a variety of problems. Most of them are linked to the intermittent nature of renewable generation from the sources of wind and photovoltaics. A possible solution to balance European generation and co
We consider state-aggregation schemes for Markov chains from an information-theoretic perspective. Specifically, we consider aggregating the states of a Markov chain such that the mutual information of the aggregated states separated by T time steps
In October of 2020, China announced that it aims to start reducing its carbon dioxide (CO2) emissions before 2030 and achieve carbon neutrality before 20601. The surprise announcement came in the midst of the COVID-19 pandemic which caused a transien