Learning Optimal Solutions for Extremely Fast AC Optimal Power Flow

Zamzam, Ahmed S.; Baker, Kyri

doi:10.1109/smartgridcomm47815.2020.9303008

Cited by 100 publications

(87 citation statements)

References 23 publications

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“…We now summarize the approaches proposed by existing works that use DNNs for the OPF problem [2], [3]. Fig.…”

Section: B Dnn Approach For the Opf Problemmentioning

confidence: 99%

“…1. Note that solving the AC power flow problem is computationally extremely fast as compared to solving the AC OPF problem, and hence, adds only a small computational overhead on the DNN approach [3].…”

Section: B Dnn Approach For the Opf Problemmentioning

confidence: 99%

“…The OPF solution predicted by the DNN is feasible when it satisfies the active power generation/ nodal voltage limits, which are specified in ( 4), ( 5), (6). In order to ensure the feasibility of DNN proposed solution, we take the following approach proposed in [3], [15]. First, we perform a linear transformation for the active power generation/ nodal voltage magnitudes as follows:…”

Section: Ensuring Feasibilitymentioning

confidence: 99%

“…This approach was shown to provide up to 100 times speed-up as compared to using conventional optimization solvers. A similar approach was used to solve the AC-OPF problem in [3], in which, the inputs to the DNN are the active/reactive power demand at the load buses and the outputs are the active power generations and voltage magnitudes at the generator buses. This framework was shown to achieve 20 times speed-up as compared to conventional OPF solvers.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, a preventive framework to ensure feasibility for the DC OPF problem was proposed in [15] by calibrating the transmission line capacity limits and the slack bus generation limits to compensate for the inherent approximation errors of DNNs. Similar ideas were extended for the AC OPF problem in [3]. The worst-case guarantees with respect to physical constraint violations for the DNN's OPF solution were derived in [16], [17], and the results were used to reduce the worst-case error.…”

Section: Introductionmentioning

confidence: 99%

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A Meta-Learning Approach to the Optimal Power Flow Problem Under Topology Reconfigurations

Chen

Lakshminarayana

Maple

et al. 2022

IEEE Open J. Power Energy

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Recently there has been a surge of interest in adopting deep neural networks (DNNs) for solving the optimal power flow (OPF) problem in power systems. Computing optimal generation dispatch decisions using a trained DNN takes significantly less time when compared to conventional optimization solvers. However, a major drawback of existing work is that the machine learning models are trained for a specific system topology. Hence, the DNN predictions are only useful as long as the system topology remains unchanged. Changes to the system topology (initiated by the system operator) would require retraining the DNN, which incurs significant training overhead and requires an extensive amount of training data (corresponding to the new system topology). To overcome this drawback, we propose a DNN-based OPF predictor that is trained using a meta-learning (MTL) approach. The key idea behind this approach is to find a common initialization vector that enables fast training for any system topology. The developed OPF-predictor is validated through simulations using benchmark IEEE bus systems. The results show that the MTL approach achieves significant training speed-ups and requires only a few gradient steps with a few data samples to achieve high OPF prediction accuracy and outperforms other pretraining techniques.

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“…We now summarize the approaches proposed by existing works that use DNNs for the OPF problem [2], [3]. Fig.…”

Section: B Dnn Approach For the Opf Problemmentioning

confidence: 99%

Section: B Dnn Approach For the Opf Problemmentioning

confidence: 99%

Section: Ensuring Feasibilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Meta-Learning Approach to the Optimal Power Flow Problem Under Topology Reconfigurations

Chen

Lakshminarayana

Maple

et al. 2022

IEEE Open J. Power Energy

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Data‐driven decision‐focused surrogate modeling

Gupta,

Zhang

2024

AIChE Journal

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We introduce the concept of decision‐focused surrogate modeling for solving computationally challenging nonlinear optimization problems in real‐time settings. The proposed data‐driven framework seeks to learn a simpler, for example, convex, surrogate optimization model that is trained to minimize the decision prediction error, which is defined as the difference between the optimal solutions of the original and the surrogate optimization models. The learning problem, formulated as a bilevel program, can be viewed as a data‐driven inverse optimization problem to which we apply a decomposition‐based solution algorithm from previous work. We validate our framework through numerical experiments involving the optimization of common nonlinear chemical processes such as chemical reactors, heat exchanger networks, and material blending systems. We also present a detailed comparison of decision‐focused surrogate modeling with standard data‐driven surrogate modeling methods and demonstrate that our approach is significantly more data‐efficient while producing simple surrogate models with high decision prediction accuracy.

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