View in EDS HTML Full Text PDF Full Text

Year‐Long Robust DG Allocation and Probability‐Based Battery Scheduling in Uncertain Hybrid Renewable Energy System Using GO and BRO Algorithms.

Saved in:
Bibliographic Details
Title: Year‐Long Robust DG Allocation and Probability‐Based Battery Scheduling in Uncertain Hybrid Renewable Energy System Using GO and BRO Algorithms.
Authors: Abbas, Asad1 (AUTHOR), Sajjad, Intisar Ali2 (AUTHOR), Kim, Jonghoon1 (AUTHOR) qwzxas@hanmail.net, C., Dhanamjayulu (AUTHOR) dhanamjayulu.c@vit.ac.in
Source: International Transactions on Electrical Energy Systems. 3/24/2026, Vol. 2026, p1-24. 24p.
Subject Terms: *Distributed power generation, *Hybrid power systems, *Multi-objective optimization, *Energy storage, *Mathematical optimization, *Electric power systems, *Metaheuristic algorithms, *Renewable energy sources
Abstract: Escalating load demand at the distribution level necessitates the incorporation of distributed generators (DGs) into power systems. Consequently, the utilization of DGs into power systems based on renewable energy has become a primary approach in the pursuit of affordable and sustainable energy supply. However, this integration introduces significant challenges owing to the inherent variability and uncertainty of both the load and renewable energy sources, particularly wind‐ and photovoltaic‐based DG. This paper addresses these challenges by proposing a comprehensive strategy that utilizes the minimax regret criterion to optimize two types of DG allocation throughout an entire year with the latest metaheuristic algorithms in the presence of uncertain generation in hybrid renewable energy systems. In addition to this primary contribution, this study considers proportional probability–based optimal battery scheduling using a metaheuristic algorithm. The key achievement of this study is the formulation of a multiobjective optimization (MOP) framework that evaluates four objective indices: active power loss (APL), reactive power loss (RPL), total voltage deviation (TVD), and line stability (LS). To the best of the authors' knowledge, this is the first study that considers all four indices collectively for nonradial systems for such a comprehensive one‐year problem. Moreover, this study analyzes the performance of the newly developed growth optimizer (GO) algorithm for optimal DG allocation and battery scheduling. Three case studies are meticulously examined (1) a system with optimal battery scheduling, (2) a system with optimal DG allocation, and (3) a system with optimal battery scheduling and DG allocation. These case studies provide valuable insights into the benefits and trade‐offs of each approach, thereby demonstrating the importance of the proposed optimization framework. The findings are compared with those of the battle royale optimization (BRO) algorithm and reveal that optimal DG allocation significantly improves the performance of the hybrid renewable energy (HRE) system throughout the year. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: enr
DbLabel: Energy & Power Source
An: 192477626
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Year‐Long Robust DG Allocation and Probability‐Based Battery Scheduling in Uncertain Hybrid Renewable Energy System Using GO and BRO Algorithms.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Abbas%2C+Asad%22">Abbas, Asad</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sajjad%2C+Intisar+Ali%22">Sajjad, Intisar Ali</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kim%2C+Jonghoon%22">Kim, Jonghoon</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> qwzxas@hanmail.net</i><br /><searchLink fieldCode="AR" term="%22C%2E%2C+Dhanamjayulu%22">C., Dhanamjayulu</searchLink> (AUTHOR)<i> dhanamjayulu.c@vit.ac.in</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22International+Transactions+on+Electrical+Energy+Systems%22">International Transactions on Electrical Energy Systems</searchLink>. 3/24/2026, Vol. 2026, p1-24. 24p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Distributed+power+generation%22">Distributed power generation</searchLink><br />*<searchLink fieldCode="DE" term="%22Hybrid+power+systems%22">Hybrid power systems</searchLink><br />*<searchLink fieldCode="DE" term="%22Multi-objective+optimization%22">Multi-objective optimization</searchLink><br />*<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="%22Electric+power+systems%22">Electric power systems</searchLink><br />*<searchLink fieldCode="DE" term="%22Metaheuristic+algorithms%22">Metaheuristic algorithms</searchLink><br />*<searchLink fieldCode="DE" term="%22Renewable+energy+sources%22">Renewable energy sources</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Escalating load demand at the distribution level necessitates the incorporation of distributed generators (DGs) into power systems. Consequently, the utilization of DGs into power systems based on renewable energy has become a primary approach in the pursuit of affordable and sustainable energy supply. However, this integration introduces significant challenges owing to the inherent variability and uncertainty of both the load and renewable energy sources, particularly wind‐ and photovoltaic‐based DG. This paper addresses these challenges by proposing a comprehensive strategy that utilizes the minimax regret criterion to optimize two types of DG allocation throughout an entire year with the latest metaheuristic algorithms in the presence of uncertain generation in hybrid renewable energy systems. In addition to this primary contribution, this study considers proportional probability–based optimal battery scheduling using a metaheuristic algorithm. The key achievement of this study is the formulation of a multiobjective optimization (MOP) framework that evaluates four objective indices: active power loss (APL), reactive power loss (RPL), total voltage deviation (TVD), and line stability (LS). To the best of the authors' knowledge, this is the first study that considers all four indices collectively for nonradial systems for such a comprehensive one‐year problem. Moreover, this study analyzes the performance of the newly developed growth optimizer (GO) algorithm for optimal DG allocation and battery scheduling. Three case studies are meticulously examined (1) a system with optimal battery scheduling, (2) a system with optimal DG allocation, and (3) a system with optimal battery scheduling and DG allocation. These case studies provide valuable insights into the benefits and trade‐offs of each approach, thereby demonstrating the importance of the proposed optimization framework. The findings are compared with those of the battle royale optimization (BRO) algorithm and reveal that optimal DG allocation significantly improves the performance of the hybrid renewable energy (HRE) system throughout the year. [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=192477626
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1155/etep/3504780
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 24
        StartPage: 1
    Subjects:
      – SubjectFull: Distributed power generation
        Type: general
      – SubjectFull: Hybrid power systems
        Type: general
      – SubjectFull: Multi-objective optimization
        Type: general
      – SubjectFull: Energy storage
        Type: general
      – SubjectFull: Mathematical optimization
        Type: general
      – SubjectFull: Electric power systems
        Type: general
      – SubjectFull: Metaheuristic algorithms
        Type: general
      – SubjectFull: Renewable energy sources
        Type: general
    Titles:
      – TitleFull: Year‐Long Robust DG Allocation and Probability‐Based Battery Scheduling in Uncertain Hybrid Renewable Energy System Using GO and BRO Algorithms.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Abbas, Asad
      – PersonEntity:
          Name:
            NameFull: Sajjad, Intisar Ali
      – PersonEntity:
          Name:
            NameFull: Kim, Jonghoon
      – PersonEntity:
          Name:
            NameFull: C., Dhanamjayulu
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 24
              M: 03
              Text: 3/24/2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 20507038
          Numbering:
            – Type: volume
              Value: 2026
          Titles:
            – TitleFull: International Transactions on Electrical Energy Systems
              Type: main
ResultId 1