DeepOPF-V: Solving AC-OPF Problems Efficiently

Huang, Wanjun; Pan, Xiaojun; Chen, Minghua; Low, Steven H.

doi:10.1109/tpwrs.2021.3114092

Cited by 52 publications

(15 citation statements)

References 17 publications

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“…The IEEE 300‐bus system [42], composed of 411 transmission lines, 69 generators, and 480 “ N− 1” contingencies, is tested. For transmission lines, reactance values are from [43], and ratings are three times those from [43]. Hourly load percentages from Elia Grid [37] in 2020 are used to generate 8784 hourly load instances.…”

Section: Numerical Resultsmentioning

confidence: 99%

Interpretable data‐driven contingency classification for real‐time corrective security‐constrained economic dispatch

Yu,

Gao,

et al. 2023

IET Renewable Power Gen

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High penetrations of renewable energy are crucial for low‐carbon power systems. However, the higher volatility of renewable power generation pushes real‐time operations closer to equipment limits. It is thus important to utilize flexibilities in the system through corrective security‐constrained economic dispatch (SCED) that allows generators to take corrective adjustments after contingencies. The corrective SCED problem, containing a large number of contingencies, and corresponding post‐contingency decisions and constraints, is very large in scale and difficult to solve using purely model‐based methods within the strict time limits of real‐time markets. To accelerate the solution process, this paper develops a novel interpretable data‐driven contingency classification method. Historical data and their potentially useful patterns are utilized in interpretable data‐driven decision tree classifiers. To directly consider continuous features, such as net load values, and to consider imbalanced datasets without much additional complexity, Improved Strong Optimal Classification Trees (ISOCTs) are developed with new branching threshold constraints and category weights in the objective function. ISOCTs are then embedded into a hybrid model‐based and data‐driven framework to guarantee the accuracy of the real‐time active contingency set and the resulting security of dispatch decisions. Numerical testing results demonstrate the classification accuracy, computational efficiency, and interpretability of the proposed approach.

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Section: Numerical Resultsmentioning

confidence: 99%

Interpretable data‐driven contingency classification for real‐time corrective security‐constrained economic dispatch

Yu,

Gao,

et al. 2023

IET Renewable Power Gen

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“…Self-Supervised Learning (SSL) has emerged as an alternative to SL that does not require labeled data [23]- [25]. Namely, training OPF proxies in a self-supervised fashion does not require the solving of any OPF instance offline, thereby removing the need for (costly) data generation.…”

Section: Introductionmentioning

confidence: 99%

“…Namely, training OPF proxies in a self-supervised fashion does not require the solving of any OPF instance offline, thereby removing the need for (costly) data generation. In [23], the authors train proxies for ACOPF where the training loss consists of the objective value of the predicted solution, plus a penalty term for constraint violations. A similar approach is used in [24] in conjunction with Generative Adversarial Networks (GANs).…”

Section: Introductionmentioning

confidence: 99%

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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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“…The primary di↵erence between the GP-POPF (Section 3.2) and the a ne policy is the kernel used for learning. Additionally, it should be acknowledged that the task of an OPF proxy can also be interpreted as directly learning a deterministic OPF solution, as seen in end-to-end OPF learning methods[206][207][208][209]. However, we argue that the OPF proxy proposed must must be coupled with a feasible solution recovery mechanism, to adjust the predicted OPF solution.…”

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confidence: 96%

Analytical approximations and decision-making techniques for power systems under uncertainty

Pareek¹

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The integration of distributed energy resources and increasing adoption of electric vehicles continue to drive uncertainty in power systems to an unprecedented level.In view of reduced applicability of traditional analysis and decision-making methods, this dissertation aims to address the need for new approaches, by attempting to solve three di↵erent sets of problems.This dissertation first proposes an analytical power flow approximation and develops a closed-form power flow framework. The thesis proposes a novel framework using Gaussian process regression to learn node voltage as a closed-form function of e↵ective bus load or injection vector. The proposed approximation is valid over a subspace of load, where the 'subspace' is used to describe a hypercube within which uncertain loads/injections lie. The approximation framework explains the system's behavior under uncertainty via Gaussian process hyper-parameter based interpretability. The proposed method achieves low mean absolute error of order 10 5 (per unit) in voltage magnitude and 10 4 (rad.) in angle when tested on 33-Bus and 56-Bus systems. Also, the proposed framework is used to develop an optimal steady-state voltage control framework using a linear voltage-power relationship. Simulation results on the 69-Bus system have shown the independence of approximation error with system loading and uncertainty levels. Therefore, control mechanisms developed based on the proposed analytical approximation are suitable under uncertainty. The framework also addresses the challenges posed by independent entity integration (e.g., peer-to-peer energy trading), as independent operational decisions of prosumers a↵ect the entire network. The proposed probabilistic feasibility set construction method-for privacy-preserving feasibility assessment-is tractable and handles non-parametric injection uncertainties by applying the worst-case performance analysis theorem over the proposed closed-form power flow approximation. The developed method's ability to capture the variations in probabilistic feasibility set for storage connected at a node in a 33-Bus network is shown via numerical simulations under di↵erent levels of total renewable penetration and uncertainty.xviiContents Statement of Originality iii

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DeepOPF-V: Solving AC-OPF Problems Efficiently

Cited by 52 publications

References 17 publications

Interpretable data‐driven contingency classification for real‐time corrective security‐constrained economic dispatch

Interpretable data‐driven contingency classification for real‐time corrective security‐constrained economic dispatch

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

Analytical approximations and decision-making techniques for power systems under uncertainty

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