o shift the computational burden from real-time to offline in delay-critical power systems applications, recent works entertain the idea of using a deep neural network (DNN) to predict the solutions of the AC optimal power flow (AC-OPF) once presented load demands. As network topologies may change, training this DNN in a sample-efficient manner becomes a necessity. To improve data efficiency, this work utilizes the fact OPF data are not simple training labels, but constitute the solutions of a parametric optimization problem. We thus advocate training a sensitivity-informed DNN (SI-DNN) to match not only the OPF optimizers, but also their partial derivatives with respect to the OPF parameters (loads). It is shown that the required Jacobian matrices do exist under mild conditions, and can be readily computed from the related primal/dual solutions. The proposed SI-DNN is compatible with a broad range of OPF solvers, including a non-convex quadratically constrained quadratic program (QCQP), its semidefinite program (SDP) relaxation, and MATPOWER; while SI-DNN can be seamlessly integrated in other learning-to-OPF schemes. Numerical tests on three benchmark power systems corroborate the advanced generalization and constraint satisfaction capabilities for the OPF solutions predicted by an SI-DNN over a conventionally trained DNN, especially in low-data setups.
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An Analysis of the Reliability of AC Optimal Power Flow Deep Learning Proxies
Optimal Power Flow (OPF) is a challenging problem
in power systems, and recent research has explored the use of
Deep Neural Networks (DNNs) to approximate OPF solutions
with reduced computational times. While these approaches show
promising accuracy and efficiency, there is a lack of analysis of
their robustness. This paper addresses this gap by investigating
the factors that lead to both successful and suboptimal predictions
in DNN-based OPF solvers. It identifies power system features and
DNN characteristics that contribute to higher prediction errors
and offers insights on mitigating these challenges when designing
deep learning models for OPF.
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- Award ID(s):
- 2041835
- PAR ID:
- 10482464
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE PES Conference On Innovative Smart Grid Technologies Latin America
- ISSN:
- 2643-8798
- ISBN:
- 979-8-3503-3696-2
- Page Range / eLocation ID:
- 170 to 174
- Format(s):
- Medium: X
- Location:
- San Juan, PR, USA
- Sponsoring Org:
- National Science Foundation
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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.more » « less
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