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<p>Distributed generation (DG) and air-powered switches (APS) are essentials components for minimizing power losses, improving voltage profiles, and isolating faults in modern distribution networks. However, most existing studies address their optimization separately, leading to reverse-power-flow miscoordination, protection malfunctions, and suboptimal renewable hosting capacity. This review aims to bridge this gap by systematically examining integrated optimization approaches for APS and DG placement using metaheuristic and AI-driven techniques. A hybrid methodology combining PRISMA-based systematic review and bibliometric analysis is applied to 2020–2025 RStudio (Biblioshiny) are used to visualize keyword co-occurrence networks, research collaborations, and thematic evolution. The findings reveal a research shift from isolated network reconfiguration toward integrated frameworks emphasizing DG integration, self-healing, and intelligent fault management. A quantitative synthesis of comparable studies indicates that integrated APS-DG planning reduces losses by a median of 17.8% [95% CI: 15.2–20.4%] compared with separate optimization. Future research is expected to focus on hybrid AI–metaheuristic approaches for adaptive real-time operation, resilience-based planning for enhanced renewable hosting, and incorporating digital innovations such as blockchain, quantum computing, and predictive analytics. This review consolidates current knowledge, identifies research gaps, and provides actionable insights for designing cost-efficient, resilient, and future-ready distribution systems.</p>

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Computational Methods for Joint DG-Switch Optimization in Distribution Networks: A Systematic Review and Bibliometric Analysis (2020–2025)

  • 期刊论文

  • 发表日期：

    2026年2月25日

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  • Elnaz Yaghoubi,
  • Elaheh Yaghoubi,
  • Reza Lotfinia,
  • Mohammad Reza Maghami,
  • Mehdi Zareian Jahromi,
  • Sivakumar Sivanesan,
  • Mohamed Mazlan

摘要

Distributed generation (DG) and air-powered switches (APS) are essentials components for minimizing power losses, improving voltage profiles, and isolating faults in modern distribution networks. However, most existing studies address their optimization separately, leading to reverse-power-flow miscoordination, protection malfunctions, and suboptimal renewable hosting capacity. This review aims to bridge this gap by systematically examining integrated optimization approaches for APS and DG placement using metaheuristic and AI-driven techniques. A hybrid methodology combining PRISMA-based systematic review and bibliometric analysis is applied to 2020–2025 RStudio (Biblioshiny) are used to visualize keyword co-occurrence networks, research collaborations, and thematic evolution. The findings reveal a research shift from isolated network reconfiguration toward integrated frameworks emphasizing DG integration, self-healing, and intelligent fault management. A quantitative synthesis of comparable studies indicates that integrated APS-DG planning reduces losses by a median of 17.8% [95% CI: 15.2–20.4%] compared with separate optimization. Future research is expected to focus on hybrid AI–metaheuristic approaches for adaptive real-time operation, resilience-based planning for enhanced renewable hosting, and incorporating digital innovations such as blockchain, quantum computing, and predictive analytics. This review consolidates current knowledge, identifies research gaps, and provides actionable insights for designing cost-efficient, resilient, and future-ready distribution systems.