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    <title>EconStor Community:</title>
    <link>https://hdl.handle.net/10419/52611</link>
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    <pubDate>Fri, 01 May 2026 13:42:03 GMT</pubDate>
    <dc:date>2026-05-01T13:42:03Z</dc:date>
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      <title>EconStor Community:</title>
      <url>http://econstor.eu:80/retrieve/6f3d1cb1-9527-403e-817c-44364fd5c9be/logo-uni-bayreuth.png</url>
      <link>https://hdl.handle.net/10419/52611</link>
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      <title>From papers to power plants: A taxonomy of power flow tracing methods in research and practice</title>
      <link>https://hdl.handle.net/10419/338104</link>
      <description>Title: From papers to power plants: A taxonomy of power flow tracing methods in research and practice
Authors: Ströher, Tobias; Strüker, Jens; Konoval, Volodymyr
Abstract: Power flow tracing (PFT) methods algorithmically reconstruct how electricity generators supply specific loads and contribute to network losses, enabling physically grounded attribution of electricity flows across a grid. Despite nearly three decades of research, the field lacks a unified conceptual framework that integrates academic and industry perspectives. In this paper, we address this gap by conducting a multivocal literature review (MLR) covering 52 academic and industry sources published between 2019 and 2025, and developing a taxonomy of PFT methods structured along six dimensions and 20 characteristics: input, output, tracing approach, application area, topology model, and level of analysis. Our analysis reveals that linear-equation-based methods embodying the proportional sharing principle dominate both academic and practitioner contexts, and that emissions attribution and renewable energy certification have emerged as the primary application areas, primarily driven by tightening sustainability reporting requirements. While PFT methodologies themselves exhibit considerable maturity, we find that limited data availability, granularity, and quality represent the central barrier to broader practical adoption. We discuss how digital technologies can support the measuring, reporting, and verification of electricity data to overcome these barriers, and propose a research agenda from a data perspective. Our taxonomy supports policymakers and grid operators in selecting suitable PFT methods for regulatory, technical, and operational contexts.</description>
      <pubDate>Thu, 01 Jan 2026 00:00:00 GMT</pubDate>
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      <dc:date>2026-01-01T00:00:00Z</dc:date>
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      <title>A digital infrastructure for integrating decentralized assets into redispatch. Decentralized Redispatch (DEER): Interfaces for providing flexibility</title>
      <link>https://hdl.handle.net/10419/313653</link>
      <description>Title: A digital infrastructure for integrating decentralized assets into redispatch. Decentralized Redispatch (DEER): Interfaces for providing flexibility
Authors: Körner, Marc-Fabian; Nolting, Lars; Babel, Matthias; Ehaus, Marvin; Heeß, Paula; Lautenschlager, Jonathan; Radtke, Malin; Schick, Leo; Strüker, Jens; Wiedemann, Stefanie; Zwede, Till
Abstract: In response to the challenges posed by an increasingly decentralized energy system characterized by a high penetration of renewable energy sources, grid operators are experiencing heightened pressure to effectively manage grid congestion. Concurrently, both the European Union as well as the German government's ambitious climate targets are fostering the proliferation of small-scale systems like heat pumps, photovoltaic systems with battery storages, and electric cars, thereby enhancing the flexibility potential for redispatch operations. The project "Decentralized Redispatch (DEER): Interfaces for providing flexibility" aims to explore the integration of decentral flexibility into congestion management practices. This White Paper provides an overview on the project's first outcomes and the necessary background technologies and methods. The project's primary focus lies in designing an architecture in the context of a multi-agentsystem that facilities secure and sovereign communication among all stakeholders in such a decentralized redispatch, ensuring data security, data privacy, and verifiability. The DEER project sets out to analyze the potential of leveraging self-sovereign identity management methods, combined with technologies such as zero-knowledge proofs and distributed ledgers, as a robust framework for achieving these objectives.</description>
      <pubDate>Wed, 01 Jan 2025 00:00:00 GMT</pubDate>
      <guid isPermaLink="false">https://hdl.handle.net/10419/313653</guid>
      <dc:date>2025-01-01T00:00:00Z</dc:date>
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      <title>Sustainability in the German economy – A comparison of understanding, measures and reporting between SMEs and large companies</title>
      <link>https://hdl.handle.net/10419/325597</link>
      <description>Title: Sustainability in the German economy – A comparison of understanding, measures and reporting between SMEs and large companies
Authors: Horn, Andreas; Berkmüller, Ruth; Bokelmann, Monika; Gerk, Alexander-Michael; Heldmann, Jan; Knauer, Miriam; Naji, Fatah; Rath, Simon; Sperling, Franziska; Wagner, Johanna
Abstract: Sustainability and non-financial risk management are becoming increasingly im-portant for German companies due to climate change and new regulations. However, it is un-clear how well companies of varying sizes are prepared to handle the increasing pressure to adapt. While large companies are obliged to publish a sustainability report due to regulatory requirements such as the CSRD, there is no direct obligation for SMEs. Nevertheless, due to external pressure from stakeholders and information requirements of large companies, SMEs increasingly have to address the issue of sustainability. This raises the question as to how far large companies and SMEs have progressed in implementing sustainability, how the costs and benefits of this adaptation process can be compared and what differences can be identified be-tween the two company sizes. Our study is based on a June 2023 survey of 120 companies and provides an overview of the current state of sustainability management in German companies. Further, our results show that large companies have a broader understanding of sustainability than SMEs. In addition, most large companies already implement and report on sustainability measures. Overall, we observe that there is a link between understanding sustainability, imple-menting sustainability measures and sustainability reporting.</description>
      <pubDate>Wed, 01 Jan 2025 00:00:00 GMT</pubDate>
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      <dc:date>2025-01-01T00:00:00Z</dc:date>
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      <title>Generative Artificial Intelligence in the energy sector</title>
      <link>https://hdl.handle.net/10419/290410</link>
      <description>Title: Generative Artificial Intelligence in the energy sector
Authors: Böcking, Lars; Michaelis, Anne; Schäfermeier, Bastian; Baier, André; Kühl, Niklas; Körner, Marc-Fabian; Nolting, Lars</description>
      <pubDate>Mon, 01 Jan 2024 00:00:00 GMT</pubDate>
      <guid isPermaLink="false">https://hdl.handle.net/10419/290410</guid>
      <dc:date>2024-01-01T00:00:00Z</dc:date>
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