@article{RuhnauStieweMuesseletal., author = {Ruhnau, Oliver and Stiewe, Clemens and Muessel, Jarusch and Hirth, Lion}, title = {Natural gas savings in Germany during the 2022 energy crisis}, series = {Nature Energy}, journal = {Nature Energy}, doi = {10.48462/opus4-4944}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:b1570-opus4-49445}, abstract = {Russia curbed its natural gas supply to Europe in 2021 and 2022, creating a grave energy crisis. This paper empirically estimates the crisis response of natural gas consumers in Germany—for decades the largest export market for Russian gas. Using a multiple regression model, we estimate the response of small consumers, industry, and power stations separately, controlling for the non-linear temperature-heating relationship, seasonality, and trends. We find significant and substantial gas savings for all consumer groups, but with differences in timing and size. For instance, industry started reducing consumption as early as September 2021, while small consumers saved substantially only since March 2022. Across all sectors, gas consumption during the second half of 2022 was 23\% below the temperature-adjusted baseline. We discuss the drivers behind these savings and draw conclusions on their role in coping with the crisis.}, language = {en} } @techreport{RuhnauMuessel, type = {Working Paper}, author = {Ruhnau, Oliver and Muessel, Jarusch}, title = {Update and extension of the When2Heat dataset}, abstract = {The "When2Heat" dataset comprises synthetic national time series for heat demand and heat pumps' coefficient of performance (COP) in hourly resolution. Heat demands for space and water heating are computed by combining gas standard load profiles with spatial temperature reanalysis data and population geodata. With this update, we extend the dataset to 28 European countries and the period from 2008 to 2019, including new, state-of-the-art data sources. For the geographical extension, we propose a novel approach, shifting established German heat demand curves based on country-specific heating thresholds to account for regional differences in thermal insulation and user behavior. Using the example of Italy, we illustrate the effect of shifting heat demand curves. The dataset, scripts, and input parameters are publicly available under an open-source license on the Open Power System Data platform.}, language = {en} } @techreport{RuhnauStieweMuesseletal., type = {Working Paper}, author = {Ruhnau, Oliver and Stiewe, Clemens and Muessel, Jarusch and Hirth, Lion}, title = {Gas demand in times of crisis. The response of German households and industry to the 2021/22 energy crisis}, pages = {8}, abstract = {Europe is in the midst of the most severe energy crisis in a generation, at the core of which is the continuously plummeting supply of Russian natural gas. With alternative supply options being limited, natural gas prices have surged. This paper empirically estimates the response of natural gas demand to the price increase, using data from Germany—the so far largest consumer of Russian natural gas. We identify the crisis response of small and large consumers separately, controlling for temperature, gas-fired power generation, and economic activity. For small consumers, including mostly households, we find a substantial demand reduction of 6\% from March onwards—most likely due to political and ethical considerations after the start of Russia's invasion of Ukraine. For industrial consumers, demand reductions started much earlier in August 2021, when wholesale prices for natural gas started to surge, with an average reduction of 11\%. We conclude that voluntary industrial demand response has played a significant role in coping with the energy crisis so far.}, language = {en} } @techreport{MuesselRuhnauMadlener, type = {Working Paper}, author = {Muessel, Jarusch and Ruhnau, Oliver and Madlener, Reinhard}, title = {Simulating charging behavior of electric vehicles: review and comparison with empirical data}, series = {19th International Conference on the European Energy Market (EEM), Lappeenranta, Finland, 2023}, journal = {19th International Conference on the European Energy Market (EEM), Lappeenranta, Finland, 2023}, doi = {10.1109/EEM58374.2023.10161947}, pages = {1 -- 7}, abstract = {Electric vehicles (EVs) are an important option to decarbonize the passenger transport sector and, therefore, critical to be adequately represented in energy system models. One of the main challenges is to model the volatility associated with charging EVs. We provide an overview of existing modeling approaches for this. We especially compare methods for simulating charging profiles and discuss their advantages and disadvantages, depending on the application. On that basis, we pick one simulation approach and generate time series for a case study of Germany in 2030. We assess the results and compare them with a large empirical dataset on EV charging in the UK. We derive recommendations for the future modeling of EVs.}, language = {en} } @article{MuesselRuhnauMadlener, author = {Muessel, Jarusch and Ruhnau, Oliver and Madlener, Reinhard}, title = {Accurate and scalable representation of electric vehicles in energy system models: A virtual storage-based aggregation approach}, series = {iScience}, volume = {26}, journal = {iScience}, number = {10}, doi = {10.1016/j.isci.2023.107816}, abstract = {The growing number of electric vehicles (EVs) will challenge the power system, but EVs may also support system balancing via smart charging. Modeling EVs' system-level impact while respecting computational constraints requires the aggregation of individual profiles. We show that studies typically rely on too few profiles to accurately model EVs' system-level impact and that a na{\"i}ve aggregation of individual profiles leads to an overestimation of the fleet's flexibility potential. To overcome this problem, we introduce a scalable and accurate aggregation approach based on the idea of modeling deviations from an uncontrolled charging strategy as virtual energy storage. We apply this to a German case study and estimate an average flexibility potential of 6.2 kWh/EV, only 10\% of the result of a na{\"i}ve aggregation. We conclude that our approach allows for a more realistic representation of EVs in energy system models and suggest applying it to other flexible assets.}, language = {en} }