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A Unified Co-Optimization Framework for Hybrid Renewable Systems Incorporating Degradation-Aware Multi-Storage and Demand-Side Management.

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Title: A Unified Co-Optimization Framework for Hybrid Renewable Systems Incorporating Degradation-Aware Multi-Storage and Demand-Side Management.
Authors: Alotaibi, Majed A.1,2 (AUTHOR)
Source: Energies (19961073). Jun2026, Vol. 19 Issue 11, p2705. 36p.
Subject Terms: *Pumped storage power plants, *Mixed integer linear programming, *Mathematical optimization, *Computer simulation, *Energy demand management, *Energy storage, *Hybrid power systems, *Genetic algorithms
Geographic Terms: Saudi Arabia
Abstract: The intermittent nature of renewable energy systems and the mismatch between power generation and load demand necessitate the integration of efficient energy storage systems (ESSs). Among large-scale energy storage technologies, pumped hydro-energy storage systems (PHESs) are widely recognized as one of the most cost-effective and longest-lifetime storage solutions under favorable geographical conditions. This study proposes and optimizes a hybrid renewable energy system (HRES) for the Wadi Baish region in Saudi Arabia as a real case study, where the significant elevation difference between the nearby mountains and the existing lake provides favorable conditions for PHES implementation. A nested optimization framework is developed to determine the optimal sizing and operation of the HRES components. The external optimization loop employs the non-dominated sorting genetic algorithm II (NSGA-II) to optimize system sizing, while the internal optimization loop uses mixed-integer linear programming (MILP) to optimally dispatch the PHES, battery energy storage system (BESS), and hydrogen energy storage system (HESS). In addition, demand-side management (DSM) is coordinated with the MILP dispatch strategy to improve system performance and reliability. The results show that the optimized system can supply a 10 MW average load with a renewable energy penetration of 98.7%. The proposed configuration achieves a total lifecycle cost of USD 231.37 million and avoids approximately 898.58 kt of CO2 emissions over the project lifetime. PHES operates as the primary bulk energy storage technology due to its high storage capacity and low degradation characteristics. Furthermore, the degradation-aware model predicts battery replacement every 12 years and HESS replacement every 5 years. Compared with rule-based control, the MILP-based dispatch strategy reduces grid dependency by 87%. The coordinated DSM and MILP operation also reduces the levelized cost of energy to USD 0.066/kWh while improving overall system reliability. These findings demonstrate the importance of coordinated energy management and accurate degradation modeling in the optimal design and operation of renewable-based HRES configurations. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 194588093
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Label: Title
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  Data: A Unified Co-Optimization Framework for Hybrid Renewable Systems Incorporating Degradation-Aware Multi-Storage and Demand-Side Management.
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  Data: <searchLink fieldCode="AR" term="%22Alotaibi%2C+Majed+A%2E%22">Alotaibi, Majed A.</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Jun2026, Vol. 19 Issue 11, p2705. 36p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Pumped+storage+power+plants%22">Pumped storage power plants</searchLink><br />*<searchLink fieldCode="DE" term="%22Mixed+integer+linear+programming%22">Mixed integer linear programming</searchLink><br />*<searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br />*<searchLink fieldCode="DE" term="%22Computer+simulation%22">Computer simulation</searchLink><br />*<searchLink fieldCode="DE" term="%22Energy+demand+management%22">Energy demand management</searchLink><br />*<searchLink fieldCode="DE" term="%22Energy+storage%22">Energy storage</searchLink><br />*<searchLink fieldCode="DE" term="%22Hybrid+power+systems%22">Hybrid power systems</searchLink><br />*<searchLink fieldCode="DE" term="%22Genetic+algorithms%22">Genetic algorithms</searchLink>
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  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Saudi+Arabia%22">Saudi Arabia</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The intermittent nature of renewable energy systems and the mismatch between power generation and load demand necessitate the integration of efficient energy storage systems (ESSs). Among large-scale energy storage technologies, pumped hydro-energy storage systems (PHESs) are widely recognized as one of the most cost-effective and longest-lifetime storage solutions under favorable geographical conditions. This study proposes and optimizes a hybrid renewable energy system (HRES) for the Wadi Baish region in Saudi Arabia as a real case study, where the significant elevation difference between the nearby mountains and the existing lake provides favorable conditions for PHES implementation. A nested optimization framework is developed to determine the optimal sizing and operation of the HRES components. The external optimization loop employs the non-dominated sorting genetic algorithm II (NSGA-II) to optimize system sizing, while the internal optimization loop uses mixed-integer linear programming (MILP) to optimally dispatch the PHES, battery energy storage system (BESS), and hydrogen energy storage system (HESS). In addition, demand-side management (DSM) is coordinated with the MILP dispatch strategy to improve system performance and reliability. The results show that the optimized system can supply a 10 MW average load with a renewable energy penetration of 98.7%. The proposed configuration achieves a total lifecycle cost of USD 231.37 million and avoids approximately 898.58 kt of CO2 emissions over the project lifetime. PHES operates as the primary bulk energy storage technology due to its high storage capacity and low degradation characteristics. Furthermore, the degradation-aware model predicts battery replacement every 12 years and HESS replacement every 5 years. Compared with rule-based control, the MILP-based dispatch strategy reduces grid dependency by 87%. The coordinated DSM and MILP operation also reduces the levelized cost of energy to USD 0.066/kWh while improving overall system reliability. These findings demonstrate the importance of coordinated energy management and accurate degradation modeling in the optimal design and operation of renewable-based HRES configurations. [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=194588093
RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.3390/en19112705
    Languages:
      – Code: eng
        Text: English
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      Pagination:
        PageCount: 36
        StartPage: 2705
    Subjects:
      – SubjectFull: Pumped storage power plants
        Type: general
      – SubjectFull: Mixed integer linear programming
        Type: general
      – SubjectFull: Mathematical optimization
        Type: general
      – SubjectFull: Computer simulation
        Type: general
      – SubjectFull: Energy demand management
        Type: general
      – SubjectFull: Energy storage
        Type: general
      – SubjectFull: Hybrid power systems
        Type: general
      – SubjectFull: Genetic algorithms
        Type: general
      – SubjectFull: Saudi Arabia
        Type: general
    Titles:
      – TitleFull: A Unified Co-Optimization Framework for Hybrid Renewable Systems Incorporating Degradation-Aware Multi-Storage and Demand-Side Management.
        Type: main
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          Name:
            NameFull: Alotaibi, Majed A.
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          Dates:
            – D: 01
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
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              Value: 19961073
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              Value: 19
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              Value: 11
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            – TitleFull: Energies (19961073)
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