SABLE delivers a GPU sparse batched power flow solver with block-diagonal embedding that achieves up to 253x standalone speedup and 206x training throughput for AC optimal power flow learning models.
Power system robust decentralized dynamic state estimation based on multiple hypothesis testing
6 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 6representative citing papers
Proposes a decentralized contraction framework that certifies large-signal stability, exponential convergence, and explicit transient bounds for heterogeneous grid-forming inverters.
Decision-focused learning trains an LSTM PV forecaster on battery optimization objectives and cuts average electricity costs 3.6% versus standard two-stage forecasting despite 19.9% RMSE versus 8.2%.
A decentralized DSE method using statistical linearization and matrix-exponential discretization enables stable and accurate state estimation in stiff inverter-dominated power systems at coarse sampling rates.
A quantum-assisted agentic DAI framework formulates microgrid dispatch as QUBO problems solved by solver portfolios with agentic selection and belief-shaped storage valuation, achieving exact optimum in a 24-hour simulation with 97.83% renewable utilization.
Review of AI applications in power-converter-rich systems across design, control, operations, and governance, highlighting deployment gaps.
citing papers explorer
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SABLE: GPU-Based Power Flow Accelerator for Sparsity-Aware Batched Learning
SABLE delivers a GPU sparse batched power flow solver with block-diagonal embedding that achieves up to 253x standalone speedup and 206x training throughput for AC optimal power flow learning models.
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A Unified Framework for Contraction Stability Analysis of Heterogeneous Grid-Forming Inverters
Proposes a decentralized contraction framework that certifies large-signal stability, exponential convergence, and explicit transient bounds for heterogeneous grid-forming inverters.
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Decision-focused learning for optimal PV-Battery scheduling
Decision-focused learning trains an LSTM PV forecaster on battery optimization objectives and cuts average electricity costs 3.6% versus standard two-stage forecasting despite 19.9% RMSE versus 8.2%.
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Stiffness-Aware Decentralized Dynamic State Estimation for Inverter-Dominated Power Systems
A decentralized DSE method using statistical linearization and matrix-exponential discretization enables stable and accurate state estimation in stiff inverter-dominated power systems at coarse sampling rates.
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A Quantum-Assisted Agentic Distributed Artificial Intelligence Framework for Deadline-Bounded Orchestration of Hybrid Renewable Microgrids
A quantum-assisted agentic DAI framework formulates microgrid dispatch as QUBO problems solved by solver portfolios with agentic selection and belief-shaped storage valuation, achieving exact optimum in a 24-hour simulation with 97.83% renewable utilization.
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Artificial Intelligence for Power-Converter-Rich Electrical Systems: A Review
Review of AI applications in power-converter-rich systems across design, control, operations, and governance, highlighting deployment gaps.