Scholarlink

<p>This paper presents a novel metaheuristic methodology for Transmission Network Expansion Planning (TNEP) that incorporates demand uncertainty and <i>N</i>-1 security constraints using Quadratic Unconstrained Binary Optimization (QUBO) in combination with Monte Carlo Simulations (MCS). As power systems become increasingly complex, there is a growing need for planning techniques that ensure reliability and cost-effectiveness under uncertain conditions. The proposed method reformulates the constrained TNEP problem into an unconstrained binary optimization model, allowing for efficient decision-making through the use of binary variables. The Monte Carlo simulation is employed to evaluate system performance for varied demand conditions. The method is applied to the IEEE 14-Bus system under normal operation and with a 20% load growth. The methodology is first benchmarked on the IEEE 14-bus system, and its scalability and generalizability are subsequently validated on the large-scale IEEE 57-bus system, confirming the robustness of the optimization algorithm. The results indicate improved operational costs, load coverage, line congestion, and system reliability. In comparison with conventional methods, the proposed method is observed to perform equally well in terms of investment cost, operating cost, and serviceability. The method provides a flexible and robust platform for addressing various transmission network planning challenges.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A Hybrid-Metaheuristic Approach to Solve the Transmission Network Expansion Problem

  • 期刊论文

  • 发表日期：

    2026年4月30日

查看全文
  • Vansh Suri,
  • Ravi Sharma,
  • Neelu Nagpal,
  • Neelam Kassarwani,
  • D. Lakshmi,
  • Saurabh Agarwal,
  • Wooguil Pak

摘要

This paper presents a novel metaheuristic methodology for Transmission Network Expansion Planning (TNEP) that incorporates demand uncertainty and N-1 security constraints using Quadratic Unconstrained Binary Optimization (QUBO) in combination with Monte Carlo Simulations (MCS). As power systems become increasingly complex, there is a growing need for planning techniques that ensure reliability and cost-effectiveness under uncertain conditions. The proposed method reformulates the constrained TNEP problem into an unconstrained binary optimization model, allowing for efficient decision-making through the use of binary variables. The Monte Carlo simulation is employed to evaluate system performance for varied demand conditions. The method is applied to the IEEE 14-Bus system under normal operation and with a 20% load growth. The methodology is first benchmarked on the IEEE 14-bus system, and its scalability and generalizability are subsequently validated on the large-scale IEEE 57-bus system, confirming the robustness of the optimization algorithm. The results indicate improved operational costs, load coverage, line congestion, and system reliability. In comparison with conventional methods, the proposed method is observed to perform equally well in terms of investment cost, operating cost, and serviceability. The method provides a flexible and robust platform for addressing various transmission network planning challenges.