Multi-timescale optimal control strategy for energy storage using LSTM prediction–correction in the active distribution network Article Swipe
Junjian Wu
,
Yiwei Chen
,
Jinhui Zhou
,
Chengtao Jiang
,
Wei Liu
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3389/fenrg.2023.1240764
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3389/fenrg.2023.1240764
The daily output of wind power is inversely proportional to the load demand in most situations, which will lead to an increase in peak-to-valley difference and fluctuation. To solve this problem, this study proposes a long short-term memory prediction–correction-based multi-timescale optimal control strategy for energy storage. First, the proposed strategy performs a long short-term memory (LSTM) prediction on the power of wind power and load. Then, it establishes a predictive planning model to improve the effect of peak shaving and the operating income of energy storage. Finally, it uses the method of online correction of power lines for peak shaving to further optimize the energy storage power according to the error between the residual energy of energy storage and the planned residual energy in the actual peak shaving process. By comparing with traditional strategies, the proposed strategy is found to be significantly better than the constant power strategy and the power difference strategy in the peak shaving effect and operating income.
Related Topics
Concepts
Peaking power plant
Residual
Energy storage
Power (physics)
Computer science
Energy (signal processing)
Wind power
Control theory (sociology)
Peak demand
Term (time)
Mathematical optimization
Control (management)
Reliability engineering
Automotive engineering
Algorithm
Electricity
Electricity generation
Engineering
Mathematics
Artificial intelligence
Statistics
Electrical engineering
Physics
Quantum mechanics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fenrg.2023.1240764
- OA Status
- gold
- Cited By
- 1
- References
- 18
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4386918091
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4386918091Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3389/fenrg.2023.1240764Digital Object Identifier
- Title
-
Multi-timescale optimal control strategy for energy storage using LSTM prediction–correction in the active distribution networkWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-09-21Full publication date if available
- Authors
-
Junjian Wu, Yiwei Chen, Jinhui Zhou, Chengtao Jiang, Wei LiuList of authors in order
- Landing page
-
https://doi.org/10.3389/fenrg.2023.1240764Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.3389/fenrg.2023.1240764Direct OA link when available
- Concepts
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Peaking power plant, Residual, Energy storage, Power (physics), Computer science, Energy (signal processing), Wind power, Control theory (sociology), Peak demand, Term (time), Mathematical optimization, Control (management), Reliability engineering, Automotive engineering, Algorithm, Electricity, Electricity generation, Engineering, Mathematics, Artificial intelligence, Statistics, Electrical engineering, Physics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
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2024: 1Per-year citation counts (last 5 years)
- References (count)
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18Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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