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<button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.00034">arXiv:2502.00034</a> <span> [<a href="https://arxiv.org/pdf/2502.00034">pdf</a>, <a href="https://arxiv.org/format/2502.00034">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Towards Efficient Multi-Objective Optimisation for Real-World Power Grid Topology Control </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Manyari%2C+Y+E">Yassine El Manyari</a>, <a href="/search/cs?searchtype=author&query=Fuxjager%2C+A+R">Anton R. Fuxjager</a>, <a href="/search/cs?searchtype=author&query=Zahlner%2C+S">Stefan Zahlner</a>, <a href="/search/cs?searchtype=author&query=Van+Dijk%2C+J">Joost Van Dijk</a>, <a href="/search/cs?searchtype=author&query=Castagna%2C+A">Alberto Castagna</a>, <a href="/search/cs?searchtype=author&query=Barbieri%2C+D">Davide Barbieri</a>, <a href="/search/cs?searchtype=author&query=Viebahn%2C+J">Jan Viebahn</a>, <a href="/search/cs?searchtype=author&query=Wasserer%2C+M">Marcel Wasserer</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.00034v1-abstract-short" style="display: inline;"> Power grid operators face increasing difficulties in the control room as the increase in energy demand and the shift to renewable energy introduce new complexities in managing congestion and maintaining a stable supply. Effective grid topology control requires advanced tools capable of handling multi-objective trade-offs. While Reinforcement Learning (RL) offers a promising framework for tackling… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.00034v1-abstract-full').style.display = 'inline'; document.getElementById('2502.00034v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.00034v1-abstract-full" style="display: none;"> Power grid operators face increasing difficulties in the control room as the increase in energy demand and the shift to renewable energy introduce new complexities in managing congestion and maintaining a stable supply. Effective grid topology control requires advanced tools capable of handling multi-objective trade-offs. While Reinforcement Learning (RL) offers a promising framework for tackling such challenges, existing Multi-Objective Reinforcement Learning (MORL) approaches fail to scale to the large state and action spaces inherent in real-world grid operations. Here we present a two-phase, efficient and scalable Multi-Objective Optimisation (MOO) method designed for grid topology control, combining an efficient RL learning phase with a rapid planning phase to generate day-ahead plans for unseen scenarios. We validate our approach using historical data from TenneT, a European Transmission System Operator (TSO), demonstrating minimal deployment time, generating day-ahead plans within 4-7 minutes with strong performance. These results underline the potential of our scalable method to support real-world power grid management, offering a practical, computationally efficient, and time-effective tool for operational planning. Based on current congestion costs and inefficiencies in grid operations, adopting our approach by TSOs could potentially save millions of euros annually, providing a compelling economic incentive for its integration in the control room. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.00034v1-abstract-full').style.display = 'none'; document.getElementById('2502.00034v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 24 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2302.07654">arXiv:2302.07654</a> <span> [<a href="https://arxiv.org/pdf/2302.07654">pdf</a>, <a href="https://arxiv.org/format/2302.07654">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Reinforcement Learning Based Power Grid Day-Ahead Planning and AI-Assisted Control </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Fuxj%C3%A4ger%2C+A+R">Anton R. Fuxj盲ger</a>, <a href="/search/cs?searchtype=author&query=Kozak%2C+K">Kristian Kozak</a>, <a href="/search/cs?searchtype=author&query=Dorfer%2C+M">Matthias Dorfer</a>, <a href="/search/cs?searchtype=author&query=Blies%2C+P+M">Patrick M. Blies</a>, <a href="/search/cs?searchtype=author&query=Wasserer%2C+M">Marcel Wasserer</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2302.07654v1-abstract-short" style="display: inline;"> The ongoing transition to renewable energy is increasing the share of fluctuating power sources like wind and solar, raising power grid volatility and making grid operation increasingly complex and costly. In our prior work, we have introduced a congestion management approach consisting of a redispatching optimizer combined with a machine learning-based topology optimization agent. Compared to a t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.07654v1-abstract-full').style.display = 'inline'; document.getElementById('2302.07654v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2302.07654v1-abstract-full" style="display: none;"> The ongoing transition to renewable energy is increasing the share of fluctuating power sources like wind and solar, raising power grid volatility and making grid operation increasingly complex and costly. In our prior work, we have introduced a congestion management approach consisting of a redispatching optimizer combined with a machine learning-based topology optimization agent. Compared to a typical redispatching-only agent, it was able to keep a simulated grid in operation longer while at the same time reducing operational cost. Our approach also ranked 1st in the L2RPN 2022 competition initiated by RTE, Europe's largest grid operator. The aim of this paper is to bring this promising technology closer to the real world of power grid operation. We deploy RL-based agents in two settings resembling established workflows, AI-assisted day-ahead planning and realtime control, in an attempt to show the benefits and caveats of this new technology. We then analyse congestion, redispatching and switching profiles, and elementary sensitivity analysis providing a glimpse of operation robustness. While there is still a long way to a real control room, we believe that this paper and the associated prototypes help to narrow the gap and pave the way for a safe deployment of RL agents in tomorrow's power grids. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.07654v1-abstract-full').style.display = 'none'; document.getElementById('2302.07654v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 February, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2211.05612">arXiv:2211.05612</a> <span> [<a href="https://arxiv.org/pdf/2211.05612">pdf</a>, <a href="https://arxiv.org/format/2211.05612">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Power Grid Congestion Management via Topology Optimization with AlphaZero </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Dorfer%2C+M">Matthias Dorfer</a>, <a href="/search/cs?searchtype=author&query=Fuxj%C3%A4ger%2C+A+R">Anton R. Fuxj盲ger</a>, <a href="/search/cs?searchtype=author&query=Kozak%2C+K">Kristian Kozak</a>, <a href="/search/cs?searchtype=author&query=Blies%2C+P+M">Patrick M. Blies</a>, <a href="/search/cs?searchtype=author&query=Wasserer%2C+M">Marcel Wasserer</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2211.05612v1-abstract-short" style="display: inline;"> The energy sector is facing rapid changes in the transition towards clean renewable sources. However, the growing share of volatile, fluctuating renewable generation such as wind or solar energy has already led to an increase in power grid congestion and network security concerns. Grid operators mitigate these by modifying either generation or demand (redispatching, curtailment, flexible loads). U… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.05612v1-abstract-full').style.display = 'inline'; document.getElementById('2211.05612v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2211.05612v1-abstract-full" style="display: none;"> The energy sector is facing rapid changes in the transition towards clean renewable sources. However, the growing share of volatile, fluctuating renewable generation such as wind or solar energy has already led to an increase in power grid congestion and network security concerns. Grid operators mitigate these by modifying either generation or demand (redispatching, curtailment, flexible loads). Unfortunately, redispatching of fossil generators leads to excessive grid operation costs and higher emissions, which is in direct opposition to the decarbonization of the energy sector. In this paper, we propose an AlphaZero-based grid topology optimization agent as a non-costly, carbon-free congestion management alternative. Our experimental evaluation confirms the potential of topology optimization for power grid operation, achieves a reduction of the average amount of required redispatching by 60%, and shows the interoperability with traditional congestion management methods. Our approach also ranked 1st in the WCCI 2022 Learning to Run a Power Network (L2RPN) competition. Based on our findings, we identify and discuss open research problems as well as technical challenges for a productive system on a real power grid. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.05612v1-abstract-full').style.display = 'none'; document.getElementById('2211.05612v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 10 November, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2106.02328">arXiv:2106.02328</a> <span> [<a href="https://arxiv.org/pdf/2106.02328">pdf</a>, <a href="https://arxiv.org/format/2106.02328">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Neural and Evolutionary Computing">cs.NE</span> </div> </div> <p class="title is-5 mathjax"> Temporally coherent video anonymization through GAN inpainting </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Balaji%2C+T">Thangapavithraa Balaji</a>, <a href="/search/cs?searchtype=author&query=Blies%2C+P">Patrick Blies</a>, <a href="/search/cs?searchtype=author&query=G%C3%B6ri%2C+G">Georg G枚ri</a>, <a href="/search/cs?searchtype=author&query=Mitsch%2C+R">Raphael Mitsch</a>, <a href="/search/cs?searchtype=author&query=Wasserer%2C+M">Marcel Wasserer</a>, <a href="/search/cs?searchtype=author&query=Sch%C3%B6n%2C+T">Torsten Sch枚n</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2106.02328v1-abstract-short" style="display: inline;"> This work tackles the problem of temporally coherent face anonymization in natural video streams.We propose JaGAN, a two-stage system starting with detecting and masking out faces with black image patches in all individual frames of the video. The second stage leverages a privacy-preserving Video Generative Adversarial Network designed to inpaint the missing image patches with artificially generat… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2106.02328v1-abstract-full').style.display = 'inline'; document.getElementById('2106.02328v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2106.02328v1-abstract-full" style="display: none;"> This work tackles the problem of temporally coherent face anonymization in natural video streams.We propose JaGAN, a two-stage system starting with detecting and masking out faces with black image patches in all individual frames of the video. The second stage leverages a privacy-preserving Video Generative Adversarial Network designed to inpaint the missing image patches with artificially generated faces. Our initial experiments reveal that image based generative models are not capable of inpainting patches showing temporal coherent appearance across neighboring video frames. To address this issue we introduce a newly curated video collection, which is made publicly available for the research community along with this paper. We also introduce the Identity Invariance Score IdI as a means to quantify temporal coherency between neighboring frames. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2106.02328v1-abstract-full').style.display = 'none'; document.getElementById('2106.02328v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 June, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Preprint of our FG2021 submission</span> </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> 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