Gonzalo E. Constante
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    • A Quadratically-Constrained Convex Approximation for the AC Optimal Power Flow
    • Physics-informed neural networks with hard linear equality constraints
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    • Daily scheduling of generating units with natural-gas market constraints
    • An effective hybrid decomposition approach to solve the network-constrained stochastic unit commitment problem in large-scale power systems
    • Distributed manufacturing for electrified chemical processes in a microgrid
    • Stochastic Unit Commitment: Model Reduction via Learning
    • Security-constrained unit commitment: A decomposition approach embodying Kron reduction
    • Stochastic scheduling of generating units with weekly energy storage: A hybrid decomposition approach
    • Solving certain complementarity problems in power markets via convex programming
    • AC Network-Constrained Unit Commitment via Relaxation and Decomposition
    • Stealthy monitoring-control attacks to disrupt power system operations
    • AC network-constrained unit commitment via conic relaxation and convex programming
    • Sensitivity-based Vulnerability Assessment of State Estimation
    • Sensitivity-based Vulnerability Assessment of State Estimation
    • Operations and Long-Term Expansion Planning of Natural-Gas and Power Systems: A Market Perspective
    • Learning to tune low-fidelity optimization models
    • Conservation voltage reduction of networked microgrids
    • Data-Driven Probabilistic Power Flow Analysis for a Distribution System With Renewable Energy Sources Using Monte Carlo Simulation
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A Quadratically-Constrained Convex Approximation for the AC Optimal Power Flow

Jan 1, 2025·
Gonzalo Constante-Flores
,
Can Li
· 0 min read
PDF Cite
Type
Conference paper
Last updated on Jan 1, 2025
Physics-informed neural networks with hard linear equality constraints Oct 1, 2024 →

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