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<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">JERA</journal-id><journal-title-group><journal-title>Journal of Electronic Research and Application</journal-title></journal-title-group><issn>2208-3502</issn><eissn>2208-3510</eissn><publisher><publisher-name>Bio-Byword Scientific Publishing Pty. Ltd.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.26689/jera.v10i3.14637</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>Physics Informed Hybrid Quantum-Classical Dispatching for LargeScale Renewable Power Systems: A Noise-Resilient Framework</title><url>https://artdesignp.com/journal/JERA/10/3/10.26689/jera.v10i3.14637</url><author>ZhangFu,ZhaoYuming</author><pub-date pub-type="publication-year"><year>2026</year></pub-date><volume>10</volume><issue>3</issue><history><date date-type="pub"><published-time>2026-04-22</published-time></date></history><abstract>Rising renewable penetration introduces severe non-convexity in power dispatching, straining classical optimization. While variational quantum algorithms (VQAs) on NISQ devices offer combinatorial potential, “black-box” approaches struggle with scalability and grid constraints. We propose the physics-informed hybrid quantum-classical dispatching (PI-HQCD) framework to address these limitations. PI-HQCD maps power flow and storage constraints directly into a topology-aware Hamiltonian, shrinking the search space. A noise-adaptive regularization technique bounds the objective’s Lipschitz constant, ensuring convergence under measurement noise. Experiments on IEEE 39-bus and 118-bus systems show PI-HQCD outperforms stochastic dual dynamic programming (SDDP) in cost and renewable utilization. Theoretical analysis confirms our topology-aligned ansatz achieves &amp;nbsp;gradient variance scaling, mitigating barren plateaus. 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