Hybrid Quantum-Classical Dispatching for High-Renewable Power Systems:A Noise-Resilient Variational Approach with Real-World Validation
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Quantum Physics arXiv:2511.14802 (quant-ph) [Submitted on 17 Nov 2025] Title:Hybrid Quantum-Classical Dispatching for High-Renewable Power Systems:A Noise-Resilient Variational Approach with Real-World Validation Authors:Fu Zhang, Yuming Zhao View a PDF of the paper titled Hybrid Quantum-Classical Dispatching for High-Renewable Power Systems:A Noise-Resilient Variational Approach with Real-World Validation, by Fu Zhang and 1 other authors View PDF Abstract:This study introduces a hybrid quantum-classical dispatching framework designed for power systems with high renewable penetration. The proposed method integrates a variational quantum algorithm with classical optimization to provide noise-resilient performance under realistic hardware constraints. Extensive numerical tests and a real-world case study demonstrate significant improvements in cost reduction, dispatch reliability, and robustness to device noise. The approach highlights the potential of near-term quantum computing to address critical challenges in renewable energy integration. The results bridge the gap between quantum algorithms and practical energy system operations, offering a pathway for sustainable and efficient power system management. Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2511.14802 [quant-ph] (or arXiv:2511.14802v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2511.14802 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Fu Zhang [view email] [v1] Mon, 17 Nov 2025 09:45:34 UTC (631 KB) Full-text links: Access Paper: View a PDF of the paper titled Hybrid Quantum-Classical Dispatching for High-Renewable Power Systems:A Noise-Resilient Variational Approach with Real-World Validation, by Fu Zhang and 1 other authorsView PDF view license Current browse context: quant-ph new | recent | 2025-11 References & Citations INSPIRE HEP NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) Links to Code Toggle Papers with Code (What is Papers with Code?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)