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Open access Oct 2026

Physics-Informed Graph-Based Safe RL with Constraint-Specific Critics for Community Battery Coordination in Distribution Network

High penetrations of distributed energy resources (DERs) and fast-varying electric vehicle (EV) charging intensify voltage and line-limit violations in distribution networks (DNs), making safe, low-cost operation increasingly challenging. This paper proposes a physics-informed safe deep reinforcement learning (DRL) fra...

Cheng-Hao Huang, Yue Yang, Hao Wang · 0 citations
#machine learning Preprint Oct 2026

Behind-the-Meter Photovoltaic Visibility from Net Smart Meters: Deployment Regimes and Operational Insights

Distribution operators need visibility of behind-the-meter photovoltaic (PV) generation, yet most residential customers are observed only through net smart meters. Deep-learning-based PV disaggregation can restore this visibility, but its operational value depends on whether the model serves known customers, new custom...

Jack Zheng, Hao Wang · 0 citations
#machine learning Open access Sep 2026

Uncovering residential PV--EV co-adoption from smart-meter data: Load archetypes and detection for demand-side planning

The increasing adoption of electric vehicles (EVs) and rooftop photovoltaic (PV) systems is reshaping residential electricity demand and creating new challenges for demand-side management (DSM), tariff design, and low-voltage network planning. Much of the existing literature examines EV charging or PV generation in iso...

Jack Zheng, Hao Wang · 0 citations

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