View in EDS HTML Full Text PDF Full Text

An Innovative AI‐Driven Algorithm for Efficient and Precise Distribution System Planning.

Saved in:
Bibliographic Details
Title: An Innovative AI‐Driven Algorithm for Efficient and Precise Distribution System Planning.
Authors: Singh, Harshit1 (AUTHOR), Singh, Sachin1 (AUTHOR), Singh, Rajiv Kumar2 (AUTHOR), Maniraguha, Fidele3 (AUTHOR) manifilsr@gmail.com
Source: Energy Science & Engineering. Dec2025, Vol. 13 Issue 12, p6302-6321. 20p.
Subject Terms: *Distribution planning, *Reinforcement learning, *Statistical accuracy, *Automated planning & scheduling, *Economic efficiency, *Distributed resources (Electric utilities), *Constraint satisfaction, *Empirical research
Abstract: This paper presents GRATE–DRL–AI, an Artificial Intelligence (AI)–driven algorithm designed to enhance the efficiency and accuracy of distribution system planning. Leveraging advanced AI methodologies, including graph learning, transfer learning, deep reinforcement learning (DRL), and physics‐guided neural networks, this model efficiently addresses the growing complexity and uncertainties in modern distribution grids with high penetration of distributed energy resources. Case studies on the Institute of Electrical and Electronics Engineers 33‐bus and 123‐bus systems show that GRATE–DRL–AI reduces planning cost by up to 8.5%, achieves 99%–100% feasibility, and significantly lowers computation time (e.g., 580 s vs. 1610 s for the 342‐bus system). Even under ±30% uncertainty in demand and renewable generation, feasibility remains above 99%. In addition to strong performance gains, the study also highlights limitations, such as data availability, computational requirements, and regulatory considerations, which must be addressed for real‐world deployment of AI‐driven planning frameworks. [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: 190211826
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: An Innovative AI‐Driven Algorithm for Efficient and Precise Distribution System Planning.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Singh%2C+Harshit%22">Singh, Harshit</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Singh%2C+Sachin%22">Singh, Sachin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Singh%2C+Rajiv+Kumar%22">Singh, Rajiv Kumar</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Maniraguha%2C+Fidele%22">Maniraguha, Fidele</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> manifilsr@gmail.com</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Energy+Science+%26+Engineering%22">Energy Science & Engineering</searchLink>. Dec2025, Vol. 13 Issue 12, p6302-6321. 20p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Distribution+planning%22">Distribution planning</searchLink><br />*<searchLink fieldCode="DE" term="%22Reinforcement+learning%22">Reinforcement learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Statistical+accuracy%22">Statistical accuracy</searchLink><br />*<searchLink fieldCode="DE" term="%22Automated+planning+%26+scheduling%22">Automated planning & scheduling</searchLink><br />*<searchLink fieldCode="DE" term="%22Economic+efficiency%22">Economic efficiency</searchLink><br />*<searchLink fieldCode="DE" term="%22Distributed+resources+%28Electric+utilities%29%22">Distributed resources (Electric utilities)</searchLink><br />*<searchLink fieldCode="DE" term="%22Constraint+satisfaction%22">Constraint satisfaction</searchLink><br />*<searchLink fieldCode="DE" term="%22Empirical+research%22">Empirical research</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This paper presents GRATE–DRL–AI, an Artificial Intelligence (AI)–driven algorithm designed to enhance the efficiency and accuracy of distribution system planning. Leveraging advanced AI methodologies, including graph learning, transfer learning, deep reinforcement learning (DRL), and physics‐guided neural networks, this model efficiently addresses the growing complexity and uncertainties in modern distribution grids with high penetration of distributed energy resources. Case studies on the Institute of Electrical and Electronics Engineers 33‐bus and 123‐bus systems show that GRATE–DRL–AI reduces planning cost by up to 8.5%, achieves 99%–100% feasibility, and significantly lowers computation time (e.g., 580 s vs. 1610 s for the 342‐bus system). Even under ±30% uncertainty in demand and renewable generation, feasibility remains above 99%. In addition to strong performance gains, the study also highlights limitations, such as data availability, computational requirements, and regulatory considerations, which must be addressed for real‐world deployment of AI‐driven planning frameworks. [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=190211826
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1002/ese3.70318
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 20
        StartPage: 6302
    Subjects:
      – SubjectFull: Distribution planning
        Type: general
      – SubjectFull: Reinforcement learning
        Type: general
      – SubjectFull: Statistical accuracy
        Type: general
      – SubjectFull: Automated planning & scheduling
        Type: general
      – SubjectFull: Economic efficiency
        Type: general
      – SubjectFull: Distributed resources (Electric utilities)
        Type: general
      – SubjectFull: Constraint satisfaction
        Type: general
      – SubjectFull: Empirical research
        Type: general
    Titles:
      – TitleFull: An Innovative AI‐Driven Algorithm for Efficient and Precise Distribution System Planning.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Singh, Harshit
      – PersonEntity:
          Name:
            NameFull: Singh, Sachin
      – PersonEntity:
          Name:
            NameFull: Singh, Rajiv Kumar
      – PersonEntity:
          Name:
            NameFull: Maniraguha, Fidele
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 12
              Text: Dec2025
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 20500505
          Numbering:
            – Type: volume
              Value: 13
            – Type: issue
              Value: 12
          Titles:
            – TitleFull: Energy Science & Engineering
              Type: main
ResultId 1