A learning-augmented approach for AC optimal power flow

Rahman, Jubeyer; Feng, Cong; Zhang, Jie

doi:10.1016/j.ijepes.2021.106908

Cited by 25 publications

(4 citation statements)

References 17 publications

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“…The features are then fed into an ANN and the random forest model. An augmented-learning framework that combined ML models and physics-based network equations was introduced in [160]. First, the ML models are used to predict the bus voltage magnitudes and angles.…”

Section: Machine Learning/ Deep Learning Opfmentioning

confidence: 99%

Low and Medium Voltage Distribution Network Planning with Distributed Energy Resources: A Survey

Pham,

Shah,

Dao

et al. 2024

Preprint

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The penetration of distributed energy resources (DERs) such as photovoltaic systems, energy storage systems, and electric vehicles is increasing in the distribution system. The distinct characteristics of these resources, e.g., volatility and intermittency, introduce complexity in operation and planning of the distribution system. This paper first summarized the physical characteristics and morphological evaluation of the current and future distribution networks. Then, the impact of these changes on system operation and planning is outlined. Next, the tools, methods, and techniques for energy forecasting, optimal planning, and distribution system state estimation are reviewed and discussed, along with the challenges. As the main contributions, this research systematically organized the published works and assessed the relevant milestones regarding distribution system planning with DERs and emerging technologies. Finally, the key research directions in this domain are outlined.

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Section: Machine Learning/ Deep Learning Opfmentioning

confidence: 99%

Low and Medium Voltage Distribution Network Planning with Distributed Energy Resources: A Survey

Pham,

Shah,

Dao

et al. 2024

Preprint

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“…Numerical results showed that BPNN outperformed conventional algorithms in terms of speed. Apart from ANNs, there are various ML algorithms indirectly used for solving the OPF problem, such as support vector machine (SVM) [34,35], random forest [36,37], K-means [38,39], and others.…”

Section: Introductionmentioning

confidence: 99%

Advancements and Future Directions in the Application of Machine Learning to AC Optimal Power Flow: A Critical Review

Jiang,

Wang,

et al. 2024

Energies

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Optimal power flow (OPF) is a crucial tool in the operation and planning of modern power systems. However, as power system optimization shifts towards larger-scale frameworks, and with the growing integration of distributed generations, the computational time and memory requirements of solving the alternating current (AC) OPF problems can increase exponentially with system size, posing computational challenges. In recent years, machine learning (ML) has demonstrated notable advantages in efficient computation and has been extensively applied to tackle OPF challenges. This paper presents five commonly employed OPF transformation techniques that leverage ML, offering a critical overview of the latest applications of advanced ML in solving OPF problems. The future directions in the application of machine learning to AC OPF are also discussed.

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“…This survey [30] explored ML and DL methodologies in energy systems. There are several methods applied for ML and DL, such as DNN [12], convolution neural network (CNN) [27,31,32], reinforcement learning (RF) [33], Gaussian process (GP) [34], graph neural network (GNN) [31,35,36], Lagrangian-based approaches [37], compact learning and principal component analysis (PCA) [38], meta-learning [39], and the learning-aided OPF approach [40]. A hybrid or combined model reunites physics-based and statistical methods or two or more individual methods [41][42][43].…”

Section: Introductionmentioning

confidence: 99%

Combined MIMO Deep Learning Method for ACOPF with High Wind Power Integration

Unlu,

Peña

2024

Energies

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The higher penetration of renewable energy sources in current and future power grids requires effective optimization models to solve economic dispatch (ED) and optimal power flow (OPF) problems. Data-driven optimization models have shown promising results compared to classical algorithms because they can address complex and computationally demanding problems and obtain the most cost-effective solution for dispatching generators. This study compares the forecast performance of selected data-driven models using the modified IEEE 39 benchmark system with high penetration of wind power generation. The active and reactive power load data of each bus are generated using Monte Carlo simulations, and synthetic wind power data are generated by utilizing a physical wind turbine model and wind speed samples withdrawn from a Weibull distribution. The objective is to design and evaluate an enhanced deep learning approach for the nonlinear, nonconvex alternating current optimal power flow (ACOPF) problem. The study attempts to establish relationships between loads, generators, and bus outcomes, utilizing a multiple-input, multiple-output (MIMO) workflow. Specifically, the study compares the forecast error reduction of convolutional neural networks (CNNs), deep feed-forward neural networks (DFFNNs), combined/hybrid CNN-DFFNN models, and the transfer learning (TL) approach. The results indicate that the proposed combined model outperforms the CNN, hybrid CNN-DFFNN, and TL models by a small margin and the DFFNN by a large margin.

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A learning-augmented approach for AC optimal power flow

Cited by 25 publications

References 17 publications

Low and Medium Voltage Distribution Network Planning with Distributed Energy Resources: A Survey

Low and Medium Voltage Distribution Network Planning with Distributed Energy Resources: A Survey

Advancements and Future Directions in the Application of Machine Learning to AC Optimal Power Flow: A Critical Review

Combined MIMO Deep Learning Method for ACOPF with High Wind Power Integration

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