Researchers have developed a hybrid decision-making technique designed to identify the best and worst combinations of solar power with other energy sources for isolated industrial micro-grids, according to a study published in Complex & Intelligent Systems.
The study, authored by Zeeshan Ali, Nadeem Javaid and Miin-Shen Yang, addresses a practical constraint in energy planning: solar energy alone is not sufficient and cannot guarantee continuous power, because its output changes with weather conditions and is unavailable at night.
To overcome this limitation, the authors evaluated five possible integrations of solar power with other sources: solar with wind, solar with biomass, solar with diesel, solar with hydro, and solar with geothermal. The goal was to determine which of these pairings performs best and which performs worst when combined with isolated industrial micro-grids.
The core of the work is a technique the authors call linguistic intuitionistic fuzzy combined compromise solution, or LIF-CoCoSo. According to the abstract, this is a hybrid decision-making technique used for the assessment of required targets, and its construction requires aggregation operators as well as operational laws.
A key methodological contribution lies in the operational laws the researchers built on Sugeno–Weber norms. From these norms they derived Sugeno–Weber aggregation operators, which the authors say can help aggregate data into a singleton set during the decision-making process.
Using the derived model, the researchers assessed the best combination between solar power and isolated industrial micro-grids. The abstract states that this assessment was carried out with the newly developed technique, which brings together linguistic intuitionistic fuzzy information and the combined compromise solution framework.
To check the validity and strength of the approach, the authors performed a comparative analysis between the proposed ranking results and existing ranking results. This comparison was based on some numerical examples, and the authors report that it was used to derive the validity and supremacy of the derived approach.
The work sits within a broader effort to make energy production more efficient in isolated industrial settings, where the main purpose of integrating solar power with micro-grids is to play an important and efficient role in energy production. Because solar output is variable, pairing it with complementary sources is presented in the study as a way to support more reliable supply.
The article is published as an open access original article, with the version shared early to provide faster access to peer-reviewed, accepted research. The publisher notes that this version is citable, carries a permanent DOI, and is subject to further edits before the final Version of Record replaces it.
The study appears in Complex & Intelligent Systems and was published on 27 October 2026.
Subject of Research: A hybrid linguistic intuitionistic fuzzy decision-making model for ranking combinations of solar power with other energy sources in isolated industrial micro-grids.
Article Title: Integration of solar power and isolated industrial micro-grids based on LIF-CoCoSo models with Sugeno–Weber approaches
Article References: Ali, Z., Javaid, N., & Yang, M.-S. (2026). Integration of solar power and isolated industrial micro-grids based on LIF-CoCoSo models with Sugeno–Weber approaches. Complex & Intelligent Systems. https://doi.org/10.1007/s40747-026-02560-5
Image Credits: AI Generated
DOI: 10.1007/s40747-026-02560-5
Keywords: solar power, micro-grids, decision-making, intuitionistic fuzzy sets, aggregation operators, Sugeno-Weber norms, renewable energy, energy planning, biomass, wind energy, hydro power, geothermal
News Source: Faith Mcneil. (October 27, 2026). New Fuzzy Decision Model Ranks Solar Pairings for Isolated Industrial Micro-Grids. Scienmag.
