Application of Deep Learning Algorithms for Scenario Analysis of Renewable Energy-Integrated Power Systems: A Critical Review Article Swipe
Shima Rahmani
,
Nima Amjady
,
Rakibuzzaman Shah
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/electronics14112150
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/electronics14112150
As the global shift towards renewable energy sources accelerates, the challenge of effectively modeling the inherent uncertainty associated with these energy units becomes increasingly significant. Sustainable energy sources, like solar and wind power sources, are highly variable and difficult to predict, making their integration into power systems complex. Beyond renewable energy, other critical sources of uncertainty also influence power systems’ operations, including fluctuations in electricity prices and variations in load demand. To address these uncertainties, stochastic programming has become a widely adopted approach. Preparation of the required scenarios for a stochastic programming framework typically includes two main components: scenario generation and reduction. Scenario generation involves creating a diverse set of possible future outcomes based on various uncertainties considered, while scenario reduction focuses on refining these scenarios to a manageable number without losing any essential piece of information. In this paper, we explore the innovative methods used for scenario generation and scenario reduction, with a special emphasis on deep learning approaches. Additionally, we provide future research recommendation, identify areas for further development, and discuss the challenges associated with these deep learning methods.
Related Topics
Concepts
Renewable energy
Computer science
Reduction (mathematics)
Wind power
Variable renewable energy
Electricity generation
Electric power system
Scenario analysis
Industrial engineering
Electricity
Solar power
Stochastic programming
Risk analysis (engineering)
Systems engineering
Mathematical optimization
Power (physics)
Engineering
Quantum mechanics
Geometry
Statistics
Physics
Medicine
Mathematics
Electrical engineering
Metadata
- Type
- review
- Language
- en
- Landing Page
- https://doi.org/10.3390/electronics14112150
- https://www.mdpi.com/2079-9292/14/11/2150/pdf?version=1748168627
- OA Status
- gold
- References
- 53
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4410722253
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4410722253Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/electronics14112150Digital Object Identifier
- Title
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Application of Deep Learning Algorithms for Scenario Analysis of Renewable Energy-Integrated Power Systems: A Critical ReviewWork title
- Type
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reviewOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-05-25Full publication date if available
- Authors
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Shima Rahmani, Nima Amjady, Rakibuzzaman ShahList of authors in order
- Landing page
-
https://doi.org/10.3390/electronics14112150Publisher landing page
- PDF URL
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https://www.mdpi.com/2079-9292/14/11/2150/pdf?version=1748168627Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2079-9292/14/11/2150/pdf?version=1748168627Direct OA link when available
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Renewable energy, Computer science, Reduction (mathematics), Wind power, Variable renewable energy, Electricity generation, Electric power system, Scenario analysis, Industrial engineering, Electricity, Solar power, Stochastic programming, Risk analysis (engineering), Systems engineering, Mathematical optimization, Power (physics), Engineering, Quantum mechanics, Geometry, Statistics, Physics, Medicine, Mathematics, Electrical engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| publication_date | 2025-05-25 |
| publication_year | 2025 |
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