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In order to ensure security of supply in a future energy system with a high share of volatile electricity generation, flexibility technologies are needed. Industrial demand-side management ranks as one of the most efficient flexibility options. This paper analyses the effect of the integration of industrial demand-side management through the flexibilisation of aluminium electrolysis and other flexibilities of the electricity system and adjacent sectors. The additional flexibility options include electricity storage, heat storage in district heating networks, controlled charging of electric vehicles, and buffer storage in hydrogen electrolysis. The utilisation of the flexibilities is modelled in different settings with an increasing share of renewable energies, applying a dispatch model. This paper compares which contributions the different flexibilities can make to emission reduction, avoidance of curtailment, and reduction of fuel and CO2 costs, and which circumstances contribute to a decrease or increase of overall emissions with additional flexibilities. The analysis stresses the rising importance of flexibilities in an energy system based on increasing shares of renewable electricity generation, and shows that flexibilities are generally suited to reduce carbon emissions. It is presented that the relative contribution towards the reduction of curtailment and costs of flexibilisation of aluminium electrolysis are high, whereby the absolute effect is small compared to the other options due to the limited number of available processes.
Model-based scenario analyses of future energy systems often come to deviating results and conclusions when different models are used. This may be caused by heterogeneous input data and by inherent differences in model formulations. The representation of technologies for the conversion, storage, use, and transport of energy is usually stylized in comprehensive system models in order to limit the size of the mathematical problem, and may substantially differ between models. This paper presents a systematic comparison of nine power sector models with sector coupling. We analyze the impact of differences in the representation of technologies, optimization approaches, and further model features on model outcomes. The comparison uses fully harmonized input data and highly simplified system configurations to isolate and quantify model-specific effects. We identify structural differences in terms of the optimization approach between the models. Furthermore, we find substantial differences in technology modeling primarily for battery electric vehicles, reservoir hydro power, power transmission, and demand response. These depend largely on the specific focus of the models. In model analyses where these technologies are a relevant factor, it is therefore important to be aware of potential effects of the chosen modeling approach. For the detailed analysis of the effect of individual differences in technology modeling and model features, the chosen approach of highly simplified test cases is suitable, as it allows to isolate the effects of model-specific differences on results. However, it strongly limits the model's degrees of freedom, which reduces its suitability for the evaluation of fundamentally different modeling approaches.