Towards Understanding the Unreasonable Effectiveness of Learning AC-OPF
Solutions
- URL: http://arxiv.org/abs/2111.11168v1
- Date: Mon, 22 Nov 2021 13:04:31 GMT
- Title: Towards Understanding the Unreasonable Effectiveness of Learning AC-OPF
Solutions
- Authors: My H. Dinh, Ferdinando Fioretto, Mostafa Mohammadian, Kyri Baker
- Abstract summary: Optimal Power Flow (OPF) is a fundamental problem in power systems.
Recent research has proposed the use of Deep Neural Networks (DNNs) to find OPF approximations at vastly reduced runtimes.
This paper provides a step forward to address this knowledge gap.
- Score: 31.388212637482365
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Optimal Power Flow (OPF) is a fundamental problem in power systems. It is
computationally challenging and a recent line of research has proposed the use
of Deep Neural Networks (DNNs) to find OPF approximations at vastly reduced
runtimes when compared to those obtained by classical optimization methods.
While these works show encouraging results in terms of accuracy and runtime,
little is known on why these models can predict OPF solutions accurately, as
well as about their robustness. This paper provides a step forward to address
this knowledge gap. The paper connects the volatility of the outputs of the
generators to the ability of a learning model to approximate them, it sheds
light on the characteristics affecting the DNN models to learn good predictors,
and it proposes a new model that exploits the observations made by this paper
to produce accurate and robust OPF predictions.
Related papers
- Towards Long-Term predictions of Turbulence using Neural Operators [68.8204255655161]
It aims to develop reduced-order/surrogate models for turbulent flow simulations using Machine Learning.
Different model structures are analyzed, with U-NET structures performing better than the standard FNO in accuracy and stability.
arXiv Detail & Related papers (2023-07-25T14:09:53Z) - Optimal Power Flow Based on Physical-Model-Integrated Neural Network
with Worth-Learning Data Generation [1.370633147306388]
We propose an OPF solver based on a physical-model-integrated neural network (NN) with worth-learning data generation.
We show that the proposed method leads to an over 50% reduction of constraint violations and optimality loss compared to conventional NN solvers.
arXiv Detail & Related papers (2023-01-10T03:06:08Z) - Faster Adaptive Federated Learning [84.38913517122619]
Federated learning has attracted increasing attention with the emergence of distributed data.
In this paper, we propose an efficient adaptive algorithm (i.e., FAFED) based on momentum-based variance reduced technique in cross-silo FL.
arXiv Detail & Related papers (2022-12-02T05:07:50Z) - Data-Driven Chance Constrained AC-OPF using Hybrid Sparse Gaussian
Processes [57.70237375696411]
The paper proposes a fast data-driven setup that uses the sparse and hybrid Gaussian processes (GP) framework to model the power flow equations with input uncertainty.
We advocate the efficiency of the proposed approach by a numerical study over multiple IEEE test cases showing up to two times faster and more accurate solutions.
arXiv Detail & Related papers (2022-08-30T09:27:59Z) - Reinforcement Learning for Battery Energy Storage Dispatch augmented
with Model-based Optimizer [0.0]
This paper proposes a novel approach to combine the physics-based models with learning-based algorithms to solve distribution-level OPF problems.
The effectiveness of the proposed approach is demonstrated using IEEE 34-bus and 123-bus distribution feeders with numerous distribution-level battery storage systems.
arXiv Detail & Related papers (2021-09-02T14:48:25Z) - Learning to Solve the AC-OPF using Sensitivity-Informed Deep Neural
Networks [52.32646357164739]
We propose a deep neural network (DNN) to solve the solutions of the optimal power flow (ACOPF)
The proposed SIDNN is compatible with a broad range of OPF schemes.
It can be seamlessly integrated in other learning-to-OPF schemes.
arXiv Detail & Related papers (2021-03-27T00:45:23Z) - A Meta-Learning Approach to the Optimal Power Flow Problem Under
Topology Reconfigurations [69.73803123972297]
We propose a DNN-based OPF predictor that is trained using a meta-learning (MTL) approach.
The developed OPF-predictor is validated through simulations using benchmark IEEE bus systems.
arXiv Detail & Related papers (2020-12-21T17:39:51Z) - High-Fidelity Machine Learning Approximations of Large-Scale Optimal
Power Flow [49.2540510330407]
AC-OPF is a key building block in many power system applications.
Motivated by increased penetration of renewable sources, this paper explores deep learning to deliver efficient approximations to the AC-OPF.
arXiv Detail & Related papers (2020-06-29T20:22:16Z) - Data-driven Optimal Power Flow: A Physics-Informed Machine Learning
Approach [6.5382276424254995]
This paper proposes a data-driven approach for optimal power flow (OPF) based on the stacked extreme learning machine (SELM) framework.
A data-driven OPF regression framework is developed that decomposes the OPF model features into three stages.
Numerical results carried out on IEEE and Polish benchmark systems demonstrate that the proposed method outperforms other alternatives.
arXiv Detail & Related papers (2020-05-31T15:41:24Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.