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

Altan Unlu () and Malaquias Peña
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Altan Unlu: Department of Electrical & Computer Engineering, University of Connecticut, Storrs, CT 06268, USA
Malaquias Peña: Department of Civil & Environmental Engineering, University of Connecticut, Storrs, CT 06268, USA

Energies, 2024, vol. 17, issue 4, 1-28

Abstract: 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and synthetic wind power data are generated by utilizing a physical wind turbine model and wind speed samples withdrawn from a Weibull distribution.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.

Keywords: data-driven optimal power flow (OPF); renewable energy integration; deep learning; combined/hybrid deep learning; convolution neural network (CNN); deep feed-forward neural network (DFFNN); transfer learning (TL); multiple-input, multiple-output workflow (MIMO) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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