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Compact Optimality Verification for Optimization Proxies
Recent years have witnessed increasing interest in optimization proxies, i. e., machine learning models that approximate the input-output mapping of parametric optimization problems and return near-optimal feasible solutions.
Scalable Exact Verification of Optimization Proxies for Large-Scale Optimal Power Flow
Optimal Power Flow (OPF) is a valuable tool for power system operators, but it is a difficult problem to solve for large systems.
Dual Lagrangian Learning for Conic Optimization
This paper presents Dual Lagrangian Learning (DLL), a principled learning methodology for dual conic optimization proxies.
Dual Interior-Point Optimization Learning
This paper introduces Dual Interior Point Learning (DIPL) and Dual Supergradient Learning (DSL) to learn dual feasible solutions to parametric linear programs with bounded variables, which are pervasive across many industries.
Learning Optimal Power Flow Value Functions with Input-Convex Neural Networks
The Optimal Power Flow (OPF) problem is integral to the functioning of power systems, aiming to optimize generation dispatch while adhering to technical and operational constraints.
Dual Conic Proxies for AC Optimal Power Flow
In recent years, there has been significant interest in the development of machine learning-based optimization proxies for AC Optimal Power Flow (AC-OPF).
Strong Mixed-Integer Formulations for Transmission Expansion Planning with FACTS Devices
Transmission Network Expansion Planning (TNEP) problems find the most economical way of expanding a given grid given long-term growth in generation capacity and demand patterns.
Asset Bundling for Wind Power Forecasting
This approach effectively introduces an auxiliary learning task (predicting the bundle-level time series) to help the main learning tasks.
End-to-End Feasible Optimization Proxies for Large-Scale Economic Dispatch
The paper proposes a novel End-to-End Learning and Repair (E2ELR) architecture for training optimization proxies for economic dispatch problems.
Confidence-Aware Graph Neural Networks for Learning Reliability Assessment Commitments
Reliability Assessment Commitment (RAC) Optimization is increasingly important in grid operations due to larger shares of renewable generations in the generation mix and increased prediction errors.
Just-In-Time Learning for Operational Risk Assessment in Power Grids
In a grid with a significant share of renewable generation, operators will need additional tools to evaluate the operational risk due to the increased volatility in load and generation.
Bucketized Active Sampling for Learning ACOPF
This paper considers optimization proxies for Optimal Power Flow (OPF), i. e., machine-learning models that approximate the input/output relationship of OPF.
Learning Regionally Decentralized AC Optimal Power Flows with ADMM
One potential future for the next generation of smart grids is the use of decentralized optimization algorithms and secured communications for coordinating renewable generation (e. g., wind/solar), dispatchable devices (e. g., coal/gas/nuclear generations), demand response, battery & storage facilities, and topology optimization.
Risk-Aware Control and Optimization for High-Renewable Power Grids
The transition of the electrical power grid from fossil fuels to renewable sources of energy raises fundamental challenges to the market-clearing algorithms that drive its operations.
Uncertainty Quantification
Vocal Bursts Intensity Prediction
Learning Optimization Proxies for Large-Scale Security-Constrained Economic Dispatch
Motivated by a principled analysis of the market-clearing optimizations of MISO, the paper proposes a novel ML pipeline that addresses the main challenges of learning SCED solutions, i. e., the variability in load, renewable output and production costs, as well as the combinatorial structure of commitment decisions.
Data-Driven Time Series Reconstruction for Modern Power Systems Research
A critical aspect of power systems research is the availability of suitable data, access to which is limited by privacy concerns and the sensitive nature of energy infrastructure.
Learning chordal extensions
As a first building block of the learning framework, we propose an on-policy imitation learning scheme that mimics the elimination ordering provided by the (classical) minimum degree rule.
