Physics-informed Convolutional Neural Network for Microgrid Economic Dispatch Article Swipe
Xiaoyu Ge
,
Javad Khazaei
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2404.18362
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2404.18362
The variability of renewable energy generation and the unpredictability of electricity demand create a need for real-time economic dispatch (ED) of assets in microgrids. However, solving numerical optimization problems in real-time can be incredibly challenging. This study proposes using a convolutional neural network (CNN) based on deep learning to address these challenges. Compared to traditional methods, CNN is more efficient, delivers more dependable results, and has a shorter response time when dealing with uncertainties. While CNN has shown promising results, it does not extract explainable knowledge from the data. To address this limitation, a physics-inspired CNN model is developed by incorporating constraints of the ED problem into the CNN training to ensure that the model follows physical laws while fitting the data. The proposed method can significantly accelerate real-time economic dispatch of microgrids without compromising the accuracy of numerical optimization techniques. The effectiveness of the proposed data-driven approach for optimal allocation of microgrid resources in real-time is verified through a comprehensive comparison with conventional numerical optimization approaches.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2404.18362
- https://arxiv.org/pdf/2404.18362
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4396820592
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4396820592Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2404.18362Digital Object Identifier
- Title
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Physics-informed Convolutional Neural Network for Microgrid Economic DispatchWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-04-29Full publication date if available
- Authors
-
Xiaoyu Ge, Javad KhazaeiList of authors in order
- Landing page
-
https://arxiv.org/abs/2404.18362Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2404.18362Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2404.18362Direct OA link when available
- Concepts
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Microgrid, Economic dispatch, Convolutional neural network, Artificial neural network, Computer science, Artificial intelligence, Physics, Electric power system, Power (physics), Quantum mechanics, Control (management)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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