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Stage-Wise Optimal Configuration of Energy Storage for Multi-Energy Complementary Systems in Qinghai-Based on a Bilevel Optimization Model.

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Title: Stage-Wise Optimal Configuration of Energy Storage for Multi-Energy Complementary Systems in Qinghai-Based on a Bilevel Optimization Model.
Authors: Tuo, Changjun1 (AUTHOR), Han, Yunlong1,2 (AUTHOR), Yang, Xinlian1,2 (AUTHOR), Ma, Jun2 (AUTHOR), Liu, Chulei2 (AUTHOR), Zhang, Jing1 (AUTHOR), Qin, Ling1 (AUTHOR), Li, Lincang1 (AUTHOR), Xiao, Feng2 (AUTHOR) xiaofeng@ncepu.edu.cn
Source: Energies (19961073). Jun2026, Vol. 19 Issue 11, p2612. 20p.
Subject Terms: *Energy storage, *Mathematical optimization, *K-means clustering, *Long short-term memory, *Hybrid power systems, *Renewable natural resources, *Electric power systems, Planning techniques
Geographic Terms: Qinghai Sheng (China)
Abstract: For power systems with a high penetration of renewable energy, energy storage allocation is important for enhancing system flexibility and supporting renewable energy integration. Existing planning methods cannot simultaneously reflect source-load uncertainty and the stage-wise evolution of system development. To address this issue, this paper proposes a stage-wise energy storage planning framework based on bilevel optimization. The proposed method employs an LSTM model to construct representative wind power, photovoltaic power, and load time series for the subsequent optimization analysis, and applies K-means clustering to extract representative operating scenarios. The Qinghai power system is selected as a case study for validation. The results show that the proposed method can reasonably capture the stage-wise characteristics of storage demand, with deviation rates of 4.6% for storage power and 3.2% for storage capacity. Under low-, medium-, and high-growth scenarios, storage demand increases significantly with renewable development scale. In the high-growth scenario, the required storage capacity increases from 277,836 MWh in 2030 to 926,120 MWh in 2035. Meanwhile, the role of storage shifts from short-term power balancing to peak shaving and inter-temporal energy shifting, while the optimal storage duration remains stable at 3–4 h. The proposed framework provides a basis for long-term energy storage planning in power systems with high renewable penetration. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 194588000
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: Stage-Wise Optimal Configuration of Energy Storage for Multi-Energy Complementary Systems in Qinghai-Based on a Bilevel Optimization Model.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Tuo%2C+Changjun%22">Tuo, Changjun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Han%2C+Yunlong%22">Han, Yunlong</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yang%2C+Xinlian%22">Yang, Xinlian</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ma%2C+Jun%22">Ma, Jun</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Chulei%22">Liu, Chulei</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Jing%22">Zhang, Jing</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Qin%2C+Ling%22">Qin, Ling</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Lincang%22">Li, Lincang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xiao%2C+Feng%22">Xiao, Feng</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> xiaofeng@ncepu.edu.cn</i>
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  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Jun2026, Vol. 19 Issue 11, p2612. 20p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Energy+storage%22">Energy storage</searchLink><br />*<searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br />*<searchLink fieldCode="DE" term="%22K-means+clustering%22">K-means clustering</searchLink><br />*<searchLink fieldCode="DE" term="%22Long+short-term+memory%22">Long short-term memory</searchLink><br />*<searchLink fieldCode="DE" term="%22Hybrid+power+systems%22">Hybrid power systems</searchLink><br />*<searchLink fieldCode="DE" term="%22Renewable+natural+resources%22">Renewable natural resources</searchLink><br />*<searchLink fieldCode="DE" term="%22Electric+power+systems%22">Electric power systems</searchLink><br /><searchLink fieldCode="DE" term="%22Planning+techniques%22">Planning techniques</searchLink>
– Name: SubjectGeographic
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Qinghai+Sheng+%28China%29%22">Qinghai Sheng (China)</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: For power systems with a high penetration of renewable energy, energy storage allocation is important for enhancing system flexibility and supporting renewable energy integration. Existing planning methods cannot simultaneously reflect source-load uncertainty and the stage-wise evolution of system development. To address this issue, this paper proposes a stage-wise energy storage planning framework based on bilevel optimization. The proposed method employs an LSTM model to construct representative wind power, photovoltaic power, and load time series for the subsequent optimization analysis, and applies K-means clustering to extract representative operating scenarios. The Qinghai power system is selected as a case study for validation. The results show that the proposed method can reasonably capture the stage-wise characteristics of storage demand, with deviation rates of 4.6% for storage power and 3.2% for storage capacity. Under low-, medium-, and high-growth scenarios, storage demand increases significantly with renewable development scale. In the high-growth scenario, the required storage capacity increases from 277,836 MWh in 2030 to 926,120 MWh in 2035. Meanwhile, the role of storage shifts from short-term power balancing to peak shaving and inter-temporal energy shifting, while the optimal storage duration remains stable at 3–4 h. The proposed framework provides a basis for long-term energy storage planning in power systems with high renewable penetration. [ABSTRACT FROM AUTHOR]
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3390/en19112612
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 20
        StartPage: 2612
    Subjects:
      – SubjectFull: Energy storage
        Type: general
      – SubjectFull: Mathematical optimization
        Type: general
      – SubjectFull: K-means clustering
        Type: general
      – SubjectFull: Long short-term memory
        Type: general
      – SubjectFull: Hybrid power systems
        Type: general
      – SubjectFull: Renewable natural resources
        Type: general
      – SubjectFull: Electric power systems
        Type: general
      – SubjectFull: Planning techniques
        Type: general
      – SubjectFull: Qinghai Sheng (China)
        Type: general
    Titles:
      – TitleFull: Stage-Wise Optimal Configuration of Energy Storage for Multi-Energy Complementary Systems in Qinghai-Based on a Bilevel Optimization Model.
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          Name:
            NameFull: Tuo, Changjun
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            NameFull: Han, Yunlong
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            NameFull: Yang, Xinlian
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          Dates:
            – D: 01
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 19961073
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              Value: 19
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            – TitleFull: Energies (19961073)
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