Multi-objective optimization method for medium and long-term power supply and demand balance considering the spatiotemporal correlation of source and load Article Swipe
Jiaxi Li
,
Zhuomin Zhou
,
Ming Wen
,
Hongyi Huang
,
Bo Wen
,
Xinyang Zhang
,
Zongchao Yu
,
Hai‐Wei Liang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.esr.2024.101463
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.esr.2024.101463
The medium and long-term supply-demand imbalance of the power system in the context of the new power system is becoming more and more prominent due to the fluctuation and intermittency brought about by the high proportion of new energy sources connected to the grid. In this regard, a multi-objective power supply-demand balance optimization method considering the spatiotemporal correlation of source and load is proposed in this work. First, the autocorrelation and inter-correlation characteristics of source and load are analyzed. On this basis, a multi-dimensional scenario set construction method considering the spatiotemporal correlation of source and load is proposed. Then, the planning capacity of each regional power source and the system operation under each scenario are taken as the optimization variables. Renewable energy electricity curtailment, equivalent annual total cost, and inter-region transmission electricity are taken as the optimization objectives. Various constraints such as power source planning and operation, power balance, inter-region power transmission, and renewable energy power curtailment rate are considered comprehensively. The optimization method for the medium and long-term power supply and demand balance is proposed. Finally, the method is applied to Hunan Province, China to guide power planning. The results show that compared with traditional multi-dimensional correlation scene construction methods, the average probability density functions error of wind turbine output, photovoltaic output, and load constructed in this work decrease by 44.08 %, 73.64 %, and 57.54 %, respectively. It takes into account the regional, temporal, temporal autocorrelation, and inter-correlation of the source and load, and has similar characteristics to historical data. Compared with traditional planning that only considers economy, the optimization plan for power supply and demand balance in this work reduces electricity curtailment and inter-region transmission by 97.04 % and 72.71 %, respectively, balancing renewable energy consumption, economy, and regional independent balancing indicators.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.esr.2024.101463
- OA Status
- gold
- Cited By
- 10
- References
- 39
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4399878111
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4399878111Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.esr.2024.101463Digital Object Identifier
- Title
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Multi-objective optimization method for medium and long-term power supply and demand balance considering the spatiotemporal correlation of source and loadWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-06-21Full publication date if available
- Authors
-
Jiaxi Li, Zhuomin Zhou, Ming Wen, Hongyi Huang, Bo Wen, Xinyang Zhang, Zongchao Yu, Hai‐Wei LiangList of authors in order
- Landing page
-
https://doi.org/10.1016/j.esr.2024.101463Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.esr.2024.101463Direct OA link when available
- Concepts
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Term (time), Balance (ability), Supply and demand, Mathematical optimization, Power (physics), Power Balance, Medium term, Computer science, Economics, Microeconomics, Mathematics, Physics, Physical medicine and rehabilitation, Medicine, Quantum mechanics, MacroeconomicsTop concepts (fields/topics) attached by OpenAlex
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10Total citation count in OpenAlex
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2025: 7, 2024: 3Per-year citation counts (last 5 years)
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39Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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