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Selecting sustainable renewable energy-powered desalination: an MCDM framework under uncertain and incomplete information

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Abstract

Renewable energy (RE)-powered desalination technology benefits both the water supply and energy utilization. Therefore, it is critical to propose a decision-making tool to identify the best technology by incorporating multi-criteria from the environmental, economic, social, and technological aspects. However, considering multiple criteria usually comes at costs. On the one hand, the lack of enough data regarding the multi-criteria decreases the reliability of the decision-making; on the other hand, the uncertain data that arise from the complex nature of the RE-powered desalination technologies limit the accuracy of the decision-making. With this in mind, this work aims to develop a hybrid multi-criteria decision-making (MCDM) framework to select sustainable technology of the RE-powered desalination under uncertain and incomplete information by combining adjacent comparative programming, the interval entropy, and interval VIKOR (vlsekriterijuska optimizacija i komoromisno resenje). Compared to the existing works, the proposed framework has the following contributions and advantages: It introduces adjacent comparative programming, which can address the data patching issue by resorting to the linguistic-based comparisons among incomplete information and known data; it adopts interval entropy for assigning the weights and interval VIKOR for ranking the technologies, which not only handle the uncertain condition by incorporating the interval number, but also provide easy yet reliable decision-making tools that can fully utilize the numerical data. The feasibility of the framework was confirmed by studying an illustrative case, which investigated nine configurations between different desalination technologies (multistage flash, humidification and dehumidification, multi-effect distillation, reverse osmosis, electrodialysis, and mechanical vapor compression) and renewable energy resources (solar thermal, geothermal, solar photovoltaic, wind energy, and hybrid photovoltaic and wind). Besides, this work conducted the sensitivity analysis and comparative analysis regarding the results of the case study. It also offered corresponding theoretical implications and management inspirations. In general, the proposed framework could make methodological contributions to the decision-making issues regarding RE-powered desalination under uncertain and incomplete information. The obtained result also can serve as an early reference in integrating renewable energy resources and desalination technologies.

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Availability of data and material

The data that support the findings of this study can be found in the article.

Code availability

The code regarding the programming in the article is available from the author upon reasonable request.

Abbreviations

AD:

Adsorption desalination

AHP:

Analytic hierarchy process

CDI:

Capacitive deionization

CSP:

Concentrated solar power

DANP:

Decision-making trial and evaluation laboratory-based analytic network process

ED:

Electrodialysis

EDR:

Electrodialysis reversal

EDI:

Electrodeionization

C j :

The jth criterion

GE:

Geothermal

HDH:

Humidification and dehumidification

MD:

Membrane distillation

MED:

Multi-effect distillation

MSF:

Multistage flash

MVC:

Mechanical vapor compression

VCD:

Vapor compression distillation

TVC:

Thermal vapor compression

PV:

Photovoltaics

PROMETHEE:

Preference ranking organization method for enrichment evaluations

RE:

Renewable energy

RO:

Reverse osmosis

ST:

Solar thermal

USD:

US dollar

(VA)TOPSIS:

(Vector-aided) technique for order of preference by similarity to ideal solution

VIKOR:

Vlsekriterijuska optimizacija i komoromisno resenje

WE:

Wind

WSM:

Weighted sum method

α :

The coefficient of a-cut in interval entropy

A i :

The ith technology

A * :

The best vector of the alternative technologies

A − :

The worst vector of the alternative technologies

\(DS_{{\left( {A_{i} \sim\,A_{j} } \right)}}\) :

The distance between two interval numbers

C j :

The jth criterion

\(\left[ {e_{j}^{l} \, e_{j}^{u} } \right]\) :

The entropy value of the jth criterion

\(\left[ {t_{ij}^{l} \, t_{ij}^{u} } \right]\) :

The original interval data of the performance of the ith alternative on the jth criterion

\(\left[ {t_{ij(\alpha )}^{l} \, t_{ij(\alpha )}^{u} } \right]\) :

The adjusted data for \(t_{ij} = \left[ {t_{ij}^{l} \, t_{ij}^{u} } \right]\) with a-cut

\(\left[ {nt_{ij(\alpha )}^{l} \, nt_{ij(\alpha )}^{u} } \right]\) :

The normalized version of \(\left[ {t_{ij(\alpha )}^{l} \, t_{ij(\alpha )}^{u} } \right]\)

\(\left[ {w_{j}^{l} \, w_{j}^{u} } \right]\) :

The interval weight of the jth criterion

S j :

The parameter for the utility measure for the jth criterion in interval VIKOR

S * :

The minimal value in Sj

S − :

The maximal value in Sj

R j :

The parameter for the regret measure for the jth criterion in interval VIKOR

R * :

The minimal value in Rj

R − :

The maximal value in Rj

\(P_{{\left( {A_{i} \succ A_{j} } \right)}}\) :

The possibility of an interval number (Ai) being higher than another (Aj)

v :

The coefficient of compromise in interval VIKOR

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Acknowledgements

This research is supported by the Science and Technology Research Program of Chongqing Municipal Education Commission of China (No. KJQN202103212).

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  1. Chongqing Industry Polytechnic College, Chongqing, China

    Qiongjian Huang

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Appendix

Appendix

See Tables 11, 12 and 13.

Table 11 Information regarding the data patching by using adjacent comparative programming
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Table 12 The sets of weights with different values of α-cut
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Table 13 The ranking results of interval versions of WSM, TOPSIS, and VIKOR
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Huang, Q. Selecting sustainable renewable energy-powered desalination: an MCDM framework under uncertain and incomplete information. Clean Techn Environ Policy 24, 1581–1598 (2022). https://doi.org/10.1007/s10098-021-02268-9

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