Physics-Informed Neural Networks for Minimising Worst-Case Violations in DC Optimal Power Flow

Nellikkath, Rahul; Chatzivasileiadis, Spyros

doi:10.1109/smartgridcomm51999.2021.9632308

Cited by 16 publications

(11 citation statements)

References 18 publications

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“…Regularization can greatly enhance the performance of NN models by mitigating data overfitting and improving the training speed. Simlarly for OPF learning, regularization has been introduced to e.g., improve the constraint satisfaction [9], or approach the first-order optimality [6], [10]. Nonetheless, the flow constraint in (1e) is by and large the most critical feasibility condition of ac-OPF, motivating us to develop a new ac-feasibility regularization (FR) approach.…”

Section: Ac-feasibility Regularizationmentioning

confidence: 99%

“…p (12) θ (8) ŝij recalling that f (X 0 ; φ) represents the GNN model of trainable parameter φ according to (6).…”

Section: Ac-feasibility Regularizationmentioning

confidence: 99%

“…network-wide line limit constraints. For example, a feasible domain technique was developed in [5], [9], while KKT conditions for ac/dc-OPF were used for training regularization [6], [10]. While the first approach could affect the OPF solution optimality, the second one tends to add a large number of regularization terms, all of which require the design of weight coefficients (hyper-parameters).…”

Section: Introductionmentioning

confidence: 99%

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Topology-aware Graph Neural Networks for Learning Feasible and Adaptive ac-OPF Solutions

Liu¹,

Wu²,

Zhu³

2022

Preprint

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Solving the optimal power flow (OPF) problem is a fundamental task to ensure the system efficiency and reliability in real-time electricity grid operations. We develop a new topology-informed graph neural network (GNN) approach for predicting the optimal solutions of real-time ac-OPF problem. To incorporate grid topology to the NN model, the proposed GNNfor-OPF framework innovatively exploits the locality property of locational marginal prices and voltage magnitude. Furthermore, we develop a physics-aware (ac-)flow feasibility regularization approach for general OPF learning. The advantages of our proposed designs include reduced model complexity, improved generalizability and feasibility guarantees. By providing the analytical understanding on the graph subspace stability under grid topology contingency, we show the proposed GNN can quickly adapt to varying grid topology by an efficient re-training strategy. Numerical tests on various test systems of different sizes have validated the prediction accuracy, improved flow feasibility, and topology adaptivity capability of our proposed GNN-based learning framework.

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Section: Ac-feasibility Regularizationmentioning

confidence: 99%

“…p (12) θ (8) ŝij recalling that f (X 0 ; φ) represents the GNN model of trainable parameter φ according to (6).…”

Section: Ac-feasibility Regularizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Topology-aware Graph Neural Networks for Learning Feasible and Adaptive ac-OPF Solutions

Liu¹,

Wu²,

Zhu³

2022

Preprint

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“…The main metric of interest is the achieved accuracy across the input domain defined in (21). To give a first impression of the accuracy that we can accomplish with RK-PINNs, we consider three trajectories of δ in Fig.…”

Section: B Evaluating and Interpreting Accuracymentioning

confidence: 99%

“…Physics-informed neural networks have been first introduced in power systems in our previous work [18], and, since then, have extended in applications related to system identification [19], the transient response of interconnected systems [20], and DC optimal power flow [21]; along the same lines, sensitivity-informed NNs have been recently introduced for AC power flow optimization [22] and physics-informed graphical NNs for parameter estimation [23]. All of these works, however, have utilised a continuous formulation of the problem's underlying physics and have thus required simulated training data.…”

Section: Introductionmentioning

confidence: 99%

Learning without Data: Physics-Informed Neural Networks for Fast Time-Domain Simulation

Stiasny¹,

Chevalier²,

Chatzivasileiadis³

2021

Preprint

Self Cite

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In order to drastically reduce the heavy computational burden associated with time-domain simulations, this paper introduces a Physics-Informed Neural Network (PINN) to directly learn the solutions of power system dynamics. In contrast to the limitations of classical model order reduction approaches, commonly used to accelerate time-domain simulations, PINNs can universally approximate any continuous function with an arbitrary degree of accuracy. One of the novelties of this paper is that we avoid the need for any training data. We achieve this by incorporating the governing differential equations and an implicit Runge-Kutta (RK) integration scheme directly into the training process of the PINN; through this approach, PINNs can predict the trajectory of a dynamical power system at any discrete time step. The resulting Runge-Kutta-based physics-informed neural networks (RK-PINNs) can yield up to 100 times faster evaluations of the dynamics compared to standard time-domain simulations. We demonstrate the methodology on a single-machine infinite bus system governed by the swing equation. We show that RK-PINNs can accurately and quickly predict the solution trajectories.

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Learning optimization proxies for large-scale Security-Constrained Economic Dispatch

Chen

Park

Tanneau

et al. 2022

Electric Power Systems Research

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Physics-Informed Neural Networks for Minimising Worst-Case Violations in DC Optimal Power Flow

Cited by 16 publications

References 18 publications

Topology-aware Graph Neural Networks for Learning Feasible and Adaptive ac-OPF Solutions

Topology-aware Graph Neural Networks for Learning Feasible and Adaptive ac-OPF Solutions

Learning without Data: Physics-Informed Neural Networks for Fast Time-Domain Simulation

Learning optimization proxies for large-scale Security-Constrained Economic Dispatch

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