A cumulative capital approach for dynamic transmission expansion planning: enhancing cost efficiency and grid development

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1 Scopus citations

Abstract

This study introduces a novel cumulative capital approach for dynamic transmission expansion planning (DTEP), enabling planners to carry over unspent budget across multiple years. Traditional models with rigid annual budgets often lead to investment infeasibility and suboptimal infrastructure development. In contrast, the proposed mixed-integer linear programming (MILP) model integrates budget carryover constraints, expanding the feasible solution space and enabling more strategic long-term investments. Simulation results on 6-bus, 24-bus, and 118-bus networks show that the proposed model achieves substantial improvements: load shedding is reduced by up to 88%, total costs by up to 53%, and the model maintains feasibility under tight budgets where classical models fail. For example, in budget-constrained scenarios, critical transmission lines that are unaffordable under annual caps become viable when capital is accumulated, allowing early resolution of network congestion. Furthermore, the model exhibits consistent scalability and robustness, with computation times acceptable for practical use even on large networks. These findings establish the cumulative capital approach as a cost-effective and technically sound strategy for transmission infrastructure planning under fiscal constraints. This work provides a valuable tool for policymakers and system planners aiming to balance reliability, cost, and regulatory flexibility over multi-year horizons.

Original languageEnglish
Article number128665
JournalExpert Systems with Applications
Volume292
DOIs
StatePublished - 1 Nov 2025

Bibliographical note

Publisher Copyright:
© 2025 Elsevier Ltd

Keywords

  • Dynamic transmission expansion planning
  • Investment
  • Mixed integer linear programming
  • Transmission capacity
  • Transmission lines

ASJC Scopus subject areas

  • General Engineering
  • Computer Science Applications
  • Artificial Intelligence

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