Power line routing design by GIS-driven fuzzy traveling salesman problem-binary integer programming for green energy integration

Far Chen Jong and Musse Mohamud Ahmed

Applied Energy, 2024, vol. 374, issue C, No S0306261924014752

Abstract: In response to the obstacles posed by finite resources and environmental issues, Sarawak has transitioned its focus towards sustainable and green energy to establish a resilient and eco-friendly energy landscape. Generally, Sarawak is blessed with abundant green energy resources; however, constructive green energy integration methods were absent. Proper green energy integration is necessary due to its intermittent properties and geographical dispersion. Therefore, the paper proposes a novel methodology using Geographical Information System tools incorporating geographical databases, fuzzy logic operations and Traveling Salesman Problem-Binary Integer Programming algorithms to integrate green energies focusing on optimal power line routing design. The methodology begins with clustering the green energies based on geographical divisions. Then, it considers three influential factors (distance, elevation difference, and average ground flash density) and transforms them into matrix data for each cluster. Fuzzy logic operations optimize the trade-off among these factors and form fuzzy values. Following this, the Traveling Salesman Problem-Binary Integer Programming formulation creates pairs of green energy, distance vectors, equality constraints, and binary bounds. The optimization process constructs and eliminates multiple subtours to generate an optimal single loop with minimal value, representing the optimal power line routing design. Rigorous analyses, comparison, and validation demonstrate that the proposed method consistently outperforms ordinary Traveling Salesman Problem-Binary Integer Programming, ranking top 1 across all clusters. Further evaluation against the state-of-the-art fuzzy Traveling Salesman Problem algorithms reveals that the proposed model secures the lowest fuzzy values with extremely low computation times across all clusters. Furthermore, the paper presents an innovative way to consider the ground flash density factor in green energy integration. The comprehensive algorithms and coding provided are valuable assets for researchers and investors to explore and conduct in-depth research on this prospect. Finally, this paper provides valuable directions for regional development, especially in harnessing and integrating green energies to boost economic and state infrastructure.

Keywords: Binary integer programming; Fuzzy; Geographical information system; Green energy integration; Traveling salesman problem (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1016/j.apenergy.2024.124092

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