Ultra-short-term Interval Prediction of Wind Power Based on Graph Neural Network and Improved Bootstrap Technique

Liao, Wenlong; Wang, Shouxiang; Bak‐Jensen, Birgitte; Pillai, Jayakrishnan Radhakrishna; Yang, Zhe; Liu, Kuangpu

doi:10.35833/mpce.2022.000632

Cited by 29 publications

(10 citation statements)

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“…For future work, the single objective function may be extended into multiple ones. Besides, renewable energy sources, active power dispatching, energy storage devices, and topology changes may also be considered [30], [31].…”

Section: Discussionmentioning

confidence: 99%

Data-driven Reactive Power Optimization of Distribution Networks via Graph Attention Networks

Liao,

Yang,

Liu

et al. 2024

Journal of Modern Power Systems and Clean Energy

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Reactive power optimization of distribution networks is traditionally addressed by physical model based methods, which often lead to locally optimal solutions and require heavy online inference time consumption. To improve the quality of the solution and reduce the inference time burden, this paper proposes a new graph attention networks based method to directly map the complex nonlinear relationship between graphs (topology and power loads) and reactive power scheduling schemes of distribution networks, from a data-driven perspective. The graph attention network is tailored specifically to this problem and incorporates several innovative features such as a self-loop in the adjacency matrix, a customized loss function, and the use of max-pooling layers. Additionally, a rulebased strategy is proposed to adjust infeasible solutions that violate constraints. Simulation results on multiple distribution networks demonstrate that the proposed method outperforms other machine learning based methods in terms of the solution quality and robustness to varying load conditions. Moreover, its online inference time is significantly faster than traditional physical model based methods, particularly for large-scale distribution networks.

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Section: Discussionmentioning

confidence: 99%

Data-driven Reactive Power Optimization of Distribution Networks via Graph Attention Networks

Liao,

Yang,

Liu

et al. 2024

Journal of Modern Power Systems and Clean Energy

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“…Note that both traditional and modified bootstrap techniques share the same hypothesis that the data distributions in the training, validation, and test sets are similar [25]. Only in this case, the prediction errors of the validation and test sets can be small, and the prediction errors of the test set can be accurately estimated by those of the validation set.…”

Section: Active Powermentioning

confidence: 99%

“…Specifically, the time-series predictions of power loads in the 2 nd bus, wind power in the 10 th bus, and PV power in the 22 nd bus are considered as simple examples. First, a point prediction model named gated recurrent unit is employed to predict power loads, wind power, and PV power, because of its outstanding ability in time-series prediction [25], [32]. Then, the PDFs of prediction errors of the training, validation, and test sets using the power load, wind power, and PV power datasets are visualized, as shown in Figs.…”

Section: B Feasibility Analysis Of Prediction Error Estimationmentioning

confidence: 99%

Robust Reactive Power Scheduling of Distribution Networks Based on Modified Bootstrap Technique

Liao,

Wang,

Bak-Jensen

et al. 2024

Journal of Modern Power Systems and Clean Energy

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The uncertainties of the power load, wind power, and photovoltaic power lead to errors between point prediction values and real values, which challenges the safe operation of distribution networks. In this paper, a robust reactive power scheduling (RRPS) model based on a modified bootstrap technique is proposed to consider the uncertainties of power loads and renewable energy sources. Firstly, a deterministic reactive power scheduling (DRPS) model and an RRPS model are formulated. Secondly, a modified bootstrap technique is proposed to estimate prediction errors of power loads and renewable energy sources without artificially assuming the probability density function of prediction errors. To represent all possible scenarios, point prediction values and prediction errors are combined to construct two worst-case scenarios in the RRPS model. Finally, the RRPS model is solved to find a scheduling scheme, which ensures the security of distribution networks for all possible scenarios in theory. Simulation results show that the worstcase scenarios constructed by the modified bootstrap technique outperform popular baselines. Besides, the RRPS model based on the modified bootstrap technique balances economics and security well.

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“…With GNNs, the features of the power system network can be extracted and utilized to improve overall system performance [72]. GNNs can be applied to solar and wind power prediction tasks by leveraging the spatial and temporal dependencies present in weather data and power generation patterns [124], [125], [126], [127].…”

Section: B Academic Applicationsmentioning

confidence: 99%

From Graph Theory to Graph Neural Networks (GNNs): The Opportunities of GNNs in Power Electronics

Li,

Xue,

Zargari

et al. 2023

IEEE Access

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Graph theory within power electronics, developed over a 50-year span, is continually evolving, necessitating ongoing research endeavors. Facing with the never-been-seen explosion of graphstructured data, the state-of-the-art deep learning technique-Graph Neural Networks (GNNs), becomes the leading trend in machine learning within just recent five years and demonstrated surprisingly broad and prominent benefits covering from new drug discovery to better IC design. However, its promising applications in Power Electronics are still rarely discussed and its full potential remains unexplored. Addressing this gap, this review paper is the first to outline GNNs' general workflow in power electronics, laying the groundwork and examining current GNN methodologies within the field. To bridge the gap in the sparse GNN literature within this domain, we also provide extended discussions on leveraging insights from GNN-aided circuit design to enrich power electronics research. Our work includes in-depth GNNbased case studies that demonstrate promising applications from converters to system-level power electronics, showcasing GNNs' unique benefits and untapped possibilities (e.g., accurate component design, voltage predictions on IEEE-13 bus and 118 bus systems). Additionally, we provide a comprehensive survey of GNNs' latest and successful applications, emphasizing their impact on energy-centric sectors, such as transportation electrification, smart grids. Considering the interdisciplinary nature of power electronics in modern energy systems, our review highlights the potential of GNNs emerge as a promising tool to decode the intricate behavior and dynamics of power electronics systems, and we hope such synergies between advanced AI methodologies like GNNs with the ever-evolving graph theory can lead to more powerful tools, novel methodologies, and advancements in the power electronics community.

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Ultra-short-term Interval Prediction of Wind Power Based on Graph Neural Network and Improved Bootstrap Technique

Cited by 29 publications

References 0 publications

Data-driven Reactive Power Optimization of Distribution Networks via Graph Attention Networks

Data-driven Reactive Power Optimization of Distribution Networks via Graph Attention Networks

Robust Reactive Power Scheduling of Distribution Networks Based on Modified Bootstrap Technique

From Graph Theory to Graph Neural Networks (GNNs): The Opportunities of GNNs in Power Electronics

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