研究目的
To assess and rank concentrated solar power (CSP) technologies (Solar tower, Parabolic solar trough, Compact linear Fresnel reflector, Dish Stirling) based on economic, technical, and environmental criteria using a modified intuitionistic fuzzy TOPSIS method with trigonometric entropy weights.
研究成果
The ranking of CSP technologies is PST > ST > CLFR > DS, with PST being the most favorable due to lower financial risk and good technological maturity. ST ranks second despite higher efficiency due to economic and technical issues. CLFR is promising but has optical losses, and DS is penalized by high costs and low commercial outlook. The method is robust as subjective weight comparisons yield similar rankings, indicating its suitability for renewable energy technology assessment under uncertainty.
研究不足
The method relies on subjective linguistic evaluations which may introduce bias; it does not include real-world experimental data or physical testing of CSP technologies. The entropy measure used may not fully capture all uncertainties, and the criteria set is fixed without sensitivity to varying contexts.
1：Experimental Design and Method Selection:
The study employs a modified intuitionistic fuzzy TOPSIS method combined with trigonometric entropy weights for multi-criteria decision analysis. This involves building an intuitionistic fuzzy matrix, determining vector weights using entropy measures, and calculating distances to ideal solutions.
2：Sample Selection and Data Sources:
The alternatives evaluated are four CSP technologies: Solar tower (ST), Parabolic solar trough (PST), Compact linear Fresnel reflector (CLFR), and Dish Stirling (DS). Data for evaluation are based on linguistic terms derived from expert knowledge and literature.
3：List of Experimental Equipment and Materials:
No specific equipment or materials are mentioned for experimental setup; the method is computational and based on fuzzy set theory.
4：Experimental Procedures and Operational Workflow:
Steps include building the intuitionistic fuzzy decision matrix, calculating trigonometric entropy and weights, determining positive and negative ideal solutions, computing separation measures, and ranking alternatives based on closeness coefficients.
5：Data Analysis Methods:
Data analysis involves mathematical calculations for entropy weights, Euclidean distances in intuitionistic fuzzy sets, and relative closeness coefficients using the TOPSIS algorithm.
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