Evaluation of Combined Heat and Power (CHP) Systems Using Fuzzy Shannon Entropy and Fuzzy TOPSIS

Abstract

Combined heat and power (CHP) or cogeneration can play a strategic role in addressing environmental issues and climate change. CHP systems require less fuel than separate heat and power systems in order to produce the same amount of energy saving primary energy, improving the security of the supply. Because less fuel is combusted, greenhouse gas emissions and other air pollutants are reduced. If we are to consider the CHP system as "sustainable", we must include in its assessment not only energetic performance but also environmental and economic aspects, presenting a multicriteria issue. The purpose of the paper is to apply a fuzzy multicriteria methodology to the assessment of five CHP commercial technologies. Specifically, the combination of the fuzzy Shannon's entropy and the fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approach will be tested for this purpose. Shannon's entropy concept, using interval data such as the α-cut, is a particularly suitable technique for assigning weights to criteria-it does not require a decision-making (DM) to assign a weight to the criteria. To rank the proposed alternatives, a fuzzy TOPSIS method has been applied. It is based on the principle that the chosen alternative should be as close as possible to the positive ideal solution and be as far as possible from the negative ideal solution. The proposed approach provides a useful technical-scientific decision-making tool that can effectively support, in a consistent and transparent way, the assessment of various CHP technologies from a sustainable point of view.

A Sustainable Performance Assessment Framework for Plastic Film Supply Chain Management from a Chinese Perspective

“…Fuzzy TOPSIS methods are very suitable for solving real-life application problems under a fuzzy environment [51,53]. Cavallaro et al [52] applied fuzzy TOPSIS to evaluate combined heat and power systems. Zyoud et al [53] proposed a fuzzy AHP-TOPSIS framework for water loss management in developing countries.…”

“…TOPSIS is another MCDM method, which is based on the chosen alternative being closest to the positive ideal solution and farthest from the negative ideal solution [47,52,53]. In the classical TOPSIS approach, personal judgments are represented using crisp values; in reality, however, measurements using crisp values are not always possible.…”

“…Fuzzy AHP methods combine AHP with fuzzy set theory to solve hierarchical fuzzy problems and are able to handle linguistic variables to include expert uncertain imprecise judgments. Recently, fuzzy AHP has been widely used to solve multi-criteria decision problems in other fields, such as human resource management, supplier selection and performance evaluation [48,49,52]. For the sustainable assessment in this paper, a proposed fuzzy AHP approach was developed as outlined below.…”

“…Fuzzy TOPSIS is based on the concept that the chosen alternative should be closest to the positive ideal solution (PIS) and the furthest from the negative ideal solution (NIS) [47,52,53]. Fuzzy TOPSIS methods are very suitable for solving real-life application problems under a fuzzy environment [51,53].…”

Evaluating the Comprehensive Benefit of Group-Affiliated New Energy Power Generation Enterprises for Sustainability: Based on a Combined Technique of STBI and TOPSIS

“…The most frequently used methods are analytic hierarchy process(AHP), analytic network process(ANP), Delphi method, the entropy method, grey relational analysis, the factor analysis, principal component analysis(PCA) and the technique for order preference by similar to ideal solution (TOPSIS) [26][27][28][29][30][31][32][33], among which, the former three are subjective methods, which determine the weight coefficient of each indicator by comprehensive consulting score and thus reflect the consultancy to the fullest and the rest are objective methods, which determines the weight of each indicator based on internal relationship and variation degree among indicators. However, through reviewing the related literature, it can be seen that these methods have only been employed to deal with comprehensive evaluation (CE) problems with a single type or certain types of evaluation data, such as linguistic information or real numbers.…”