Optimal Power Flow Using Graph Neural Networks
@article{Owerko2019OptimalPF, title={Optimal Power Flow Using Graph Neural Networks}, author={Damian Owerko and Fernando Gama and Alejandro Ribeiro}, journal={ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, year={2019}, pages={5930-5934} }
Optimal power flow (OPF) is one of the most important optimization problems in the energy industry. In its simplest form, OPF attempts to find the optimal power that the generators within the grid have to produce to satisfy a given demand. Optimality is measured with respect to the cost that each generator incurs in producing this power. The OPF problem is non-convex due to the sinusoidal nature of electrical generation and thus is difficult to solve. Using small angle approximations leads to a…
68 Citations
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A model architecture on which unsupervised training is performed to learn a general solution of the AC power power formulation that is independent of the specific topologies and supply tasks used for training is presented.
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A new method to reduce the number of constraints in the original OPF problem using a graph neural network (GNN) is presented, an innovative machine learning model that utilizes features from nodes, edges, and network topology to maximize its performance.
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This work uses deep neural networks to learn the dual variables of the ACOPF problem and proposes a Lagrangian-based approach that can reach more globally optimal solutions with significant computational speedup even when the training data consists of mostly suboptimal solutions.
Unsupervised Deep Learning for AC Optimal Power Flow via Lagrangian Duality
- Computer ScienceGLOBECOM 2022 - 2022 IEEE Global Communications Conference
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A deep neural network is developed to output a partial set of decision variables while the remaining variables are recovered by solving AC power flow equations and the fast decoupled power flow solver is adopted to further reduce the computational time.
References
SHOWING 1-10 OF 29 REFERENCES
Toward Distributed Energy Services: Decentralizing Optimal Power Flow With Machine Learning
- EngineeringIEEE Transactions on Smart Grid
- 2020
A data-driven approach to learn control policies for each DER to reconstruct and mimic the solution to a centralized OPF problem from solely locally available information, providing a framework for Distribution System Operators to efficiently plan and operate the contributions of DERs to achieve Distributed Energy Services in distribution networks.
Machine Learning for AC Optimal Power Flow
- Computer ScienceArXiv
- 2019
This work presents two formulations of ACOPF as a machine learning problem: 1) an end-to-end prediction task where the optimal generator settings are predicted, and 2) a constraint predictiontask where the set of active constraints in the optimal solution are predicted.
History of Optimal Power Flow and Formulations
- Engineering
- 2012
The purpose of this paper is to present a literature review of the AC Optimal Power Flow (ACOPF) problem and propose areas where the ACOPF could be improved. The ACOPF is at the heart of Independent…
A power flow method suitable for solving OPF problems using genetic algorithms
- EngineeringThe IEEE Region 8 EUROCON 2003. Computer as a Tool.
- 2003
The standard procedure in which one sets the generator-bus active power output and voltage magnitude has been replaced by an innovative approach where generator- bus voltage magnitude and voltage angle are scheduled.
Regression-based Inverter Control for Decentralized Optimal Power Flow and Voltage Regulation
- EngineeringArXiv
- 2019
A systematic and data-driven approach to determine reactive power inverter output as a function of local measurements in a manner that obtains near optimal results and allows for an efficient volt-VAR optimization (VVO) scheme.
Convex Relaxations of Optimal Power Flow Problems: An Illustrative Example
- PhysicsIEEE Transactions on Circuits and Systems I: Regular Papers
- 2016
This paper demonstrates that physically based conditions cannot universally explain algorithm behavior and uses an example OPF problem with two equivalent formulations to illustrate relaxations from the Lasserre hierarchy for polynomial optimization and a related “mixed semidefinite/second-order cone programming” hierarchy.
Optimal Wireless Resource Allocation With Random Edge Graph Neural Networks
- Computer ScienceIEEE Transactions on Signal Processing
- 2020
This work introduces the random edge graph neural network (REGNN), which performs convolutions over random graphs formed by the fading interference patterns in the wireless network, and presents an unsupervised model-free primal-dual learning algorithm to train the weights of the REGNN.
Lecture Notes on Optimal Power Flow (OPF)
- EngineeringArXiv
- 2018
These lecture notes cover the DC Optimal Power and ACoptimal Power Flow formulations, as well as the Economic Dispatch for Power Systems, and will include OPF formulations based on semidefinite programming, detailed derivation of Locational Marginal Prices, and other topics.
Stability Properties of Graph Neural Networks
- Computer ScienceIEEE Transactions on Signal Processing
- 2020
This work proves that graph convolutions with integral Lipschitz filters, in combination with the frequency mixing effect of the corresponding nonlinearities, yields an architecture that is both stable to small changes in the underlying topology, and discriminative of information located at high frequencies.
MATPOWER: Steady-State Operations, Planning, and Analysis Tools for Power Systems Research and Education
- EngineeringIEEE Transactions on Power Systems
- 2011
The details of the network modeling and problem formulations used by MATPOWER, including its extensible OPF architecture, are presented, which are used internally to implement several extensions to the standard OPF problem, including piece-wise linear cost functions, dispatchable loads, generator capability curves, and branch angle difference limits.