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  <doc>
    <id>3091</id>
    <completedYear/>
    <publishedYear>2019</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>189</issue>
    <volume>6</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2019-10-31</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Time series of heat demand and heat pump efficiency for energy system modeling</title>
    <abstract language="eng">With electric heat pumps substituting for fossil-fueled alternatives, the temporal variability of their power consumption becomes increasingly important to the electricity system. To easily include this variability in energy system analyses, this paper introduces the “When2Heat” dataset comprising synthetic national time series of both the heat demand and the coefficient of performance (COP) of heat pumps. It covers 16 European countries, includes the years 2008 to 2018, and features an hourly resolution. Demand profiles for space and water heating are computed by combining gas standard load profiles with spatial temperature and wind speed reanalysis data as well as population geodata. COP time series for different heat sources – air, ground, and groundwater – and different heat sinks – floor heating, radiators, and water heating – are calculated based on COP and heating curves using reanalysis temperature data. The dataset, as well as the scripts and input parameters, are publicly available under an open source license on the Open Power System Data platform.</abstract>
    <parentTitle language="eng">Nature Scientific Data</parentTitle>
    <identifier type="doi">10.1038/s41597-019-0199-y</identifier>
    <note>Open Access publication</note>
    <licence>Metadaten / metadata</licence>
    <author>Oliver Ruhnau</author>
    <submitter>Simone Dudziak</submitter>
    <author>Lion Hirth</author>
    <author>Aaron Praktiknjo</author>
    <collection role="HertieResearch" number="">Centre for Sustainability</collection>
    <thesisPublisher>Hertie School</thesisPublisher>
  </doc>
  <doc>
    <id>3096</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>92</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2019-11-11</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Heating with Wind: Economics of heat pumps and variable renewables</title>
    <abstract language="eng">With the growth of wind and solar energy in electricity supply, the electrification of space and water heating is becoming a promising decarbonization option. In turn, such electrification may help the power system integration of variable renewables, for two reasons: thermal storage could provide low-cost flexibility, and heat demand is seasonally correlated with wind power. However, temporal fluctuations in heat demand may also imply new challenges for the power system. This study assesses the economic characteristics of electric heat pumps and wind energy and studies their interaction on wholesale electricity markets. Using a numerical electricity market model, we estimate the economic value of wind energy and the economic cost of powering heat pumps. We find that, just as expanding wind energy depresses its €/MWhel value, adopting heat pumps increases their €/MWhel cost. This rise can be mitigated by synergistic effects with wind power, “system-friendly” heat pump technology, and thermal storage. Furthermore, heat pumps raise the wind market value, but this effect vanishes if accounting for the additional wind energy needed to serve the heat pump load. Thermal storage facilitates the system integration of wind power but competes with other flexibility options. For an efficient adoption of heat pumps and thermal storage, we argue that retail tariffs for heat pump customers should reflect their underlying economic cost.</abstract>
    <parentTitle language="eng">Energy Economics</parentTitle>
    <identifier type="doi">10.1016/j.eneco.2020.104967</identifier>
    <licence>Metadaten / metadata</licence>
    <author>Oliver Ruhnau</author>
    <submitter>Simone Dudziak</submitter>
    <author>Lion Hirth</author>
    <author>Aaron Praktiknjo</author>
    <collection role="HertieResearch" number="">Centre for Sustainability</collection>
    <thesisPublisher>Hertie School</thesisPublisher>
  </doc>
  <doc>
    <id>4015</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2021-07-20</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Why electricity market models yield different results: Carbon pricing in a model-comparison experiment</title>
    <abstract language="eng">The European electricity industry, the dominant sector of the world’s largest cap-and-trade scheme, is one of the most-studied examples of carbon pricing. In particular, numerical models are often used to study the uncertain future development of carbon prices and emissions. While parameter uncertainty is often addressed through sensitivity analyses, the potential uncertainty of the models themselves remains unclear from existing single-model studies. Here, we investigate such model-related uncertainty by running a structured model comparison experiment, in which we exposed five numerical power sector models to aligned input parameters—finding stark model differences. At a carbon price of 27 EUR/t in 2030, the models estimate that European power sector emissions will decrease by 36–57% when compared to 2016. Most of this variation can be explained by the extent to which models consider the market-driven decommissioning of coal- and lignite-fired power plants. Higher carbon prices of 57 and 87 EUR/t yield a stronger decrease in carbon emissions, by 45–75% and 52–80%, respectively. The lower end of these ranges can be attributed to the short-term fuel switch captured by dispatch-only models. The higher reductions correspond to models that additionally consider market-based investment in renewables. By further studying cross-model variation in the remaining emissions at high carbon prices, we identify the representation of combined heat and power as another crucial driver of differences across model results.</abstract>
    <identifier type="doi">10.1016/j.rser.2021.111701</identifier>
    <note>Preprint version available here:&#13;
https://www.econstor.eu/handle/10419/234468</note>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Metadaten / metadata</licence>
    <author>Oliver Ruhnau</author>
    <submitter>Bernadette Boddin</submitter>
    <author>Michael Bucksteeg</author>
    <author>David Ritter</author>
    <author>Richard Schmitz</author>
    <author>Diana Böttger</author>
    <author>Matthias Koch</author>
    <author>Arne Pöstges</author>
    <author>Michael Wiedmann</author>
    <author>Lion Hirth</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Carbon pricing, EU Emission Trading System (EU ETS), electricity decarbonization, power sector, renewable energy, fuel switch, combined heat and power, electricity market modeling, model comparison, model-related uncertainty</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Centre for Sustainability</value>
    </subject>
    <collection role="HertieResearch" number="">Centre for Sustainability</collection>
    <collection role="AY" number=""/>
    <thesisPublisher>Hertie School</thesisPublisher>
  </doc>
  <doc>
    <id>4016</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>article</type>
    <publisherName>Journal of Cleaner Production Vo. 363</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2021-07-20</completedDate>
    <publishedDate>2022-08-20</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Blue hydrogen and industrial base products: The future of fossil fuel exporters in a net-zero world</title>
    <abstract language="eng">Is there a place for today’s fossil fuel exporters in a low-carbon future? This study explores trade channels between energy exporters and importers using a novel electricity-hydrogen-steel energy systems model calibrated to Norway, a major natural gas producer, and Germany, a major energy consumer. Under tight emission constraints, Norway can supply Germany with electricity, (blue) hydrogen, or natural gas with re-import of captured CO2. Alternatively, it can use hydrogen to produce steel through direct reduction and supply it to the world market, an export route not available to other energy carriers due to high transport costs. Although results show that natural gas imports with CO2 capture in Germany is the least-cost solution, avoiding local CO2 handling via imports of blue hydrogen (direct or embodied in steel) involves only moderately higher costs. A robust hydrogen demand would allow Norway to profitably export all its natural gas production as blue hydrogen. However, diversification into local steel production, as one example of easy-to-export industrial base products, offers an effective hedge against the possibility of lower European blue hydrogen demand. Thus, it is recommended that hydrocarbon exporters like Norway consider a strategic energy export transition to a diversified mix of blue hydrogen and climate-neutral industrial base products.</abstract>
    <identifier type="doi">10.1016/j.jclepro.2022.132347</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Metadaten / metadata</licence>
    <author>Schalk Cloete</author>
    <submitter>Bernadette Boddin</submitter>
    <author>Oliver Ruhnau</author>
    <author>Jan Hendrik Cloete</author>
    <author>Lion Hirth</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Hydrogen economy, Energy-intensive industry, Decarbonization, CO2 capture and storage, Variable renewable energy</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Centre for Sustainability</value>
    </subject>
    <collection role="HertieResearch" number="">Centre for Sustainability</collection>
    <collection role="AY" number=""/>
    <thesisPublisher>Hertie School</thesisPublisher>
  </doc>
  <doc>
    <id>4027</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2021-08-09</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Electricity balancing as a market equilibrium: An instrument-based estimation of supply and demand for imbalance energy</title>
    <abstract language="eng">Frequency stability requires equalizing supply and demand for electricity at short time scales. Such electricity balancing is often understood as a sequential process in which random shocks, such as weather events, cause imbalances that system operators close by activating balancing reserves. By contrast, we study electricity balancing as a market where the equilibrium price (imbalance price) and quantity (system imbalance) are determined by supply and demand. System operators supply imbalance energy by activating reserves; market parties that, deliberately or not, deviate from schedules create a demand for imbalance energy. The incentives for deliberate strategic deviations emerge from wholesale market prices and the imbalance price. We empirically estimate the demand curve of imbalance energy, which describes how sensitive market parties are to imbalance prices. To overcome the classical endogeneity problem of price and quantity, we deploy instruments derived from a novel theoretical framework. Using data from Germany, we find a decline in the demand for imbalance energy by 2.2 MW for each increase in the imbalance price by EUR 1 per MWh. This significant price response is remarkable because the German regulator prohibits strategic deviations. We also estimate cross-market equilibriums between intraday and imbalance markets, finding that a shock to the imbalance price triggers a subsequent adjustment of the intraday price.</abstract>
    <identifier type="doi">10.1016/j.eneco.2021.105455</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Metadaten / metadata</licence>
    <author>Anselm Eicke</author>
    <submitter>Bernadette Boddin</submitter>
    <author>Oliver Ruhnau</author>
    <author>Lion Hirth</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Electricity balancing, Intraday electricity market, Imbalance energy</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Centre for Sustainability</value>
    </subject>
    <collection role="HertieResearch" number="">Centre for Sustainability</collection>
    <collection role="AY" number=""/>
    <thesisPublisher>Hertie School</thesisPublisher>
  </doc>
  <doc>
    <id>3635</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>169</pageFirst>
    <pageLast>188</pageLast>
    <pageNumber/>
    <edition/>
    <issue>1</issue>
    <volume>46</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2020-10-22</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">On capital utilization in the hydrogen economy: The quest to minimize idle capacity in renewables-rich energy systems</title>
    <abstract language="eng">The hydrogen economy is currently experiencing a surge in attention, partly due to the possibility of absorbing wind and solar energy production peaks through electrolysis. A fundamental challenge with this approach is low utilization rates of various parts of the integrated electricity-hydrogen system. To assess the importance of capacity utilization, this paper introduces a novel stylized numerical energy system model incorporating the major elements of electricity and hydrogen generation, transmission and storage, including both "green" hydrogen from electrolysis and "blue" hydrogen from natural gas reforming with CO2 capture and storage (CCS). Balancing renewables with electrolysis results in low utilization of electrolyzers, hydrogen pipelines and storage infrastructure, or electricity transmission networks, depending on whether electrolyzers are co-located with wind farms or demand centers. Blue hydrogen scenarios face similar constraints. High renewable shares impose low utilization rates of CO2 capture, transport and storage infrastructure for conventional CCS, and of hydrogen transmission and storage infrastructure for a novel process (gas switching reforming) that enables flexible power and hydrogen production. In conclusion, both green and blue hydrogen can facilitate the integration of wind and solar energy, but the cost related to low capacity utilization erodes much of the expected economic benefit.</abstract>
    <parentTitle language="deu">International Journal of Hydrogen Energy</parentTitle>
    <identifier type="doi">10.1016/j.ijhydene.2020.09.197</identifier>
    <licence>Metadaten / metadata</licence>
    <author>Schalk Cloete</author>
    <submitter>Louisa Finke</submitter>
    <author>Oliver Ruhnau</author>
    <author>Lion Hirth</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Hydrogen economy</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Energy system model</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Decarbonization</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>CO2 capture and storage</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Variable renewable energy</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>Centre for Sustainability</value>
    </subject>
    <collection role="HertieResearch" number="">Centre for Sustainability</collection>
    <thesisPublisher>Hertie School</thesisPublisher>
    <thesisGrantor>Hertie School</thesisGrantor>
  </doc>
  <doc>
    <id>5769</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>37</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>workingpaper</type>
    <publisherName>arXiv</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2025-03-21</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Identifying Elasticities in Autocorrelated Time Series Using Causal Graphs</title>
    <abstract language="eng">The price elasticity of demand can be estimated from observational data using instrumental variables (IV). However, naive IV estimators may be inconsistent in settings with autocorrelated time series. We argue that causal time graphs can simplify IV identification and help select consistent estimators. To do so, we propose to first model the equilibrium condition by an unobserved confounder, deriving a directed acyclic graph (DAG) while maintaining the assumption of a simultaneous determination of prices and quantities. We then exploit recent advances in graphical inference to derive valid IV estimators, including estimators that achieve consistency by simultaneously estimating nuisance effects. We further argue that observing significant differences between the estimates of presumably valid estimators can help to reject false model assumptions, thereby improving our understanding of underlying economic dynamics. We apply this approach to the German electricity market, estimating the price elasticity of demand on simulated and real-world data. The findings underscore the importance of accounting for structural autocorrelation in IV-based analysis.</abstract>
    <identifier type="doi">10.48550/arXiv.2409.15530</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <licence>Metadaten / metadata</licence>
    <author>Silvana Tiedemann</author>
    <submitter>Alex Karras</submitter>
    <author>Jorge Sanchez Canales</author>
    <author>Felix Schur</author>
    <author>Raffaele Sgarlato</author>
    <author>Lion Hirth</author>
    <author>Oliver Ruhnau</author>
    <author>Jonas Peters</author>
    <collection role="HertieResearch" number="">Publications PhD Researchers</collection>
    <collection role="HertieResearch" number="">Centre for Sustainability</collection>
    <collection role="AY-24-25" number=""/>
    <thesisPublisher>Hertie School</thesisPublisher>
  </doc>
  <doc>
    <id>5469</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>135</volume>
    <type>article</type>
    <publisherName>Elsevier BV</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2024-06-03</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">How aggregate electricity demand responds to hourly wholesale price fluctuations</title>
    <abstract language="eng">Electricity needs to be consumed at the very moment of production, leading wholesale prices to fluctuate widely at (sub-)hourly time scales. This article investigates the response of aggregate electricity demand to such price variations. Using wind energy as an instrument, we estimate a significant and robust short-term price elasticity of about −0.05 in Germany and attribute this to industrial consumers. As the share of consumption that is exposed to real-time prices (currently less than 25%) expands, we expect the aggregated price elasticity to grow.</abstract>
    <parentTitle language="eng">Energy Economics</parentTitle>
    <identifier type="issn">0140-9883</identifier>
    <identifier type="doi">10.1016/j.eneco.2024.107652</identifier>
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    <author>Lion Hirth</author>
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    <title language="eng">Natural gas savings in Germany during the 2022 energy crisis</title>
    <abstract language="eng">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.</abstract>
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    <title language="eng">The (very) short-term price elasticity of German electricity demand</title>
    <abstract language="eng">Electricity is a peculiar economic good, the most important reason being that it needs to be supplied at the very moment of consumption. As a result, wholesale electricity prices fluctuate widely at hourly or sub-hourly time scales, regularly reaching multiples of their average, and even turn negative. This paper examines whether the demand for electricity responds to such price variations in the very short term. To solve the classical identification problem when estimating a demand curve, we use weather-driven wind energy generation as an instrument. Our robustness checks confirm that wind energy is indeed a strong and valid instrument. Using data from Germany, we estimate that a 1 €/MWh increase in the wholesale electricity price causes the aggregate electricity demand to decline by 67–80 MW or 0.12–0.14%, contradicting the conventional wisdom that electricity demand is highly price-inelastic. These estimates are statistically significant and robust across model specifications, estimators, and sensitivity analyses. At average price and demand, our estimates correspond to a price elasticity of demand of about –0.05. Comparing situations with high and low wind energy (5–95th percentile), we estimate that prices vary by 26 €/MWh, and the corresponding demand response to wholesale electricity prices is about 2 GW, or 2.6% of peak load. Our analysis suggests that the demand response in Germany can be attributed primarily to industrial consumers.</abstract>
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    <author>Lion Hirth</author>
    <submitter>Devika Dua</submitter>
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      <value>Electricity markets</value>
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      <value>Demand response</value>
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      <value>Instrumental variables</value>
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