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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

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7 results for “AC-OPF”

Data-Driven Chance Constrained AC-OPF using Hybrid Sparse Gaussian Processeseess.SY

Paper proposes a fast data-driven AC-OPF method using sparse hybrid Gaussian processes.

problem Optimizing electricity generation and delivery under generation uncertainty in modern power grids.

method Data-driven approach using sparse hybrid Gaussian processes to model power flow equations.

result Shows up to two times faster and more accurate solutions compared to state-of-the-art methods.

data-driven AC-OPF chance-constrained Gaussian processes sparse

2022-08-30abs ↗pdf ↗

Learning Optimal Solutions for Extremely Fast AC Optimal Power Flowcs.LG

In this paper, we develop an online method that leverages machine learning to obtain feasible solutions to the AC optimal power flow (OPF) problem with negligible optimality gaps on extremely fast timescales (e.g., milliseconds), bypassing solving an AC OPF altogether. This is motivated by the fact that as the power gr…

2019-09-27abs ↗pdf ↗

Data-Driven Stochastic AC-OPF using Gaussian Processescs.LG

Develops a machine learning approach for solving AC-OPF problems.

problem Nonlinear and computationally demanding AC chance-constrained OPF problem.

method Uses Gaussian process regression to approximate AC power flow equations.

result Demonstrates competitive and promising results compared to state-of-the-art approaches.

Gaussian processes machine learning AC-OPF stochastic optimal power flow

2024-02-17abs ↗pdf ↗

Learning to Solve the AC-OPF using Sensitivity-Informed Deep Neural Networksmath.OC

This work uses a SI-DNN to predict AC-OPF solutions efficiently.

problem Efficiently predicting AC-OPF solutions in real-time power systems.

method Sensitivity-Informed Deep Neural Network (SI-DNN) for AC-OPF.

result SI-DNN can predict AC-OPF solutions with better generalization and constraint satisfaction.

AC-OPF Deep Neural Networks Optimal Power Flow Sensitivity Constraint Satisfaction

2021-03-27abs ↗pdf ↗

Data-Driven Stochastic AC-OPF using Gaussian Processesstat.ML

Paper uses Gaussian processes to solve AC-OPF with renewable uncertainty.

problem Optimizing power grids with fluctuating renewable sources.

method Data-driven approach using Gaussian processes.

result Efficiently solves chance-constrained AC-OPF with uncertainty.

Gaussian processes renewable energy AC-OPF chance constraints power grid optimization

2022-07-21abs ↗pdf ↗

DNN-based Policies for Stochastic AC OPFmath.OC

DNN policies improve stochastic AC OPF for power grid optimization.

problem Optimizing power grid operations under uncertainty.

method Deep neural network (DNN) policies for real-time generator dispatch decisions.

result DNN policies enforce feasibility constraints and produce near optimal solutions.

power grid stochastic OPF deep learning generator dispatch optimization

2021-12-04abs ↗pdf ↗

Enriching Neural Network Training Dataset to Improve Worst-Case Performance Guaranteescs.LG

Improves neural network performance by enriching training dataset.

problem Achieving worst-case performance guarantees in neural networks.

method Adapting training dataset during training to reduce worst-case violations.

result Improved worst-case performance guarantees in neural networks.

Neural Networks Training Dataset Worst-Case Performance Power Systems Machine Learning

2023-03-23abs ↗pdf ↗