1.2 Rubricantes de la cancillería de Alfonso X 115
1.2.1 Visadores 119
MCDM greatly contributed to theoretical and practical progress in different fields. MCDM methods have been widely adopted in the field of strategic planning and energy portfolios. They gained popularity in the energy planning field owing to the ability to deal with large amounts of conflicting data and information in a systematic structure as a result of increased complex energy management problems which cannot be resolved by traditional single-criteria approaches [63]. MCDM methods promote decision quality through more explicit, rational, and efficient quantification and problems analysis. Pohekar and Ramachandran [25] indicated that MCDM overcomes single-criteria methods through providing enhanced understanding of inherent features of decision problems, promoting the role of participants in the processes of decision- making, facilitating compromise and collective decisions, and providing a good platform to understand the different perceptions involved.
Among the better known and more utilized modeling approaches are multi-attribute utility theory (MAUT), AHP [34]–[37], analytic network process (ANP) [64], the technique for order of
25
preference by similarity to ideal solution (TOPSIS) [65], [66], and outranking methods such as the method of elimination and choice expressing reality (ELECTRE) [67]–[69] and the preference ranking organization method for enrichment evaluation (PROMETHEE) [23], [68]– [71]. Of these modeling methods, there is no best method, as each has its own benefits and drawbacks. Analysts decide which model to adopt depending on the problem at hand [64]. Furthermore, it is well understood that, although it would be the ideal option, it is difficult to find an alternative with the best performance in all considered aspects. MCDM thus facilitates justifying the selection of alternatives by making tradeoffs between decision criteria rather than finding one optimum alternative [23].
Pohekar and Ramachandran [25] discussed over 90 published papers using various MCDM methods in order to highlight the trends through classifications of methods and application areas, including renewable energy planning, resources allocation, building energy management, transportation energy systems, and project planning. In addition, Taha and Daim [63] introduced a literature review on MCDM applications in the renewable energy field. They discussed the spectrum of equipment and tools utilized for renewable energy policy planning, evaluation, and projects selection. Mateo [72] also discussed the use of MCDM in the renewable energy industry with greater focus on mathematical explanations of the different methods as well as the definition of criteria.
2.4.1. MCDM in solar thermal power assessment
Various studies in the literature assessed RESs through regular and fuzzy MCDM methods [34], [35], [73]–[76]. For CSP studies specifically, many evaluated CSP technologies for electricity generation, but were mostly focused on the technical and economic aspects [58], [77]– [80]. Few studies have discussed the assessment of solar thermal power technologies from a
26
multi-criteria viewpoint. Nixon et al., [37] utilized AHP to select an optimal solar thermal collection technology for north-west India. The authors suggested additional evaluation criteria in future work in addition to increasing the number of participants to acquire more accurate results. The study concluded that linear Fresnel (LF) technology with a secondary compound PT or PD reflectors was the preferred option. Aragonés-Beltrán et al., [64] conducted a study to assess the economic feasibility and analysis of projects risks, and prioritized CSP projects for medium-sized Spanish companies to maximize their profits. The study was completed in three phases, where each phase included a set of decision criteria defined by local project teams and decision makers. The study aimed mainly at aiding companies in evaluating and selecting the project offers they received. CSP projects were analyzed with a focus on a financial opportunistic perspective. Cavallaro [23] utilized PROMETHEE to assess CSP technologies in Italy. Twelve different alternative scenarios were defined in the study, including changes in plants technologies and components. Seven decision criteria were defined for the evaluation process based on technical, environmental, and economic perspectives, which were derived primarily from the European concentrated solar thermal roadmapping report [81]. Peterseim et al., [82] utilized AHP to evaluate the suitability of CSP technologies for hybridization with conventional and renewable energy plants. They assessed the capability of each collection technology to generate the host plant temperatures and subsequently evaluated the available options based on the defined criteria. Nixon et al. [83] evaluated novel designs of LF collectors through a model that combines MCDM and quality function deployment (QFD) methods.
2.4.2. Analytic hierarchy process (AHP) in energy sector
AHP is categorized as an MCDM method and was developed by Thomas Saaty to facilitate the evaluation and prioritization of multiple alternatives considering several decision criteria
27
[84]–[86]. Amer and Daim [34] built a model using AHP for the selection and prioritization of four RESs in Pakistan considering 25 parameters, in which stakeholders’ judgments were collected via questionnaire for pairwise comparisons. The authors recommended the use of AHP for renewable energy regional development and national roadmapping. Ahmad and Tahar [35] constructed a model through AHP for RESs assessment in Malaysia to prioritize four renewable alternatives based on 16 parameters in two hierarchical levels. The authors highlighted that the conditions of resource availability vary between countries, which results in different production costs. Daniel et al., [87] utilized AHP with the Delphi technique for evaluating three RESs in India considering 7 criteria. Gok [88] prioritized solar, hydropower, biomass, geothermal, and wind energy sources in Turkey considering 2 evaluation criteria and 8 sub-criteria. The author underlined the suitability of AHP in dealing with problems that involve conflicts.
In addition, Chatzimouratidis and Pilavachi used AHP to evaluate 10 alternatives including conventional, nuclear, and renewable power plants considering economic, technical, and sustainability criteria [36], and considering their impact on living standards of local communities [89]. Kablan [90] conducted a study for the evaluation of energy conservation policies in Jordan using AHP. The study presented five policy measures suggested to governments as alternatives to support energy conservation considering demand satisfaction, economic growth, increased RESs utilization, and clean environment as assessing criteria. Phdungsilp and Wuttipornpun [33] provided a supporting tool for decision makers for promoting sustainable energy systems through assessing the benefits of power plant generation systems from both environmental and social points of view. Adopting AHP, they emphasized that the most sensitive part of applying multi- criteria analysis is the selection of criteria. Furthermore, Mousavi-Seyedi et al., [91] evaluated various distributed generation alternatives for microgrid through AHP. They also used HOMER
28
simulation software to obtain the quantitative data to be incorporated in the model. Lee et al., [92] associated AHP, based on three criteria, with the benefits, opportunities, costs, and risk (BOCR) method for a strategic selection of wind farm projects.
2.4.3. Fuzzy analytic hierarchy process (FAHP) in energy sector
Extensions of fuzziness have been proposed to AHP by several authors, such as Van Laarhoven and Pedrycz [93] and Chang [94]. The fuzzy concept provides different systematic approaches, computational methods, and problem justification to deal with the uncertainties, decrease subjectivity, and enhance accuracy in capturing participants’ perceptions. Kahraman et al., [73] combined an FAHP with an axiomatic design to select among 5 RESs in Turkey considering 4 criteria and 17 sub-criteria. Tasri and Susilawati [74] developed an FAHP to determine the most appropriate RES for commercial electricity generation in Indonesia. Zheng et al., [75] used an FAHP to develop a model that facilitates the conservation assessment of building energy. Ansari et al., [76] integrated an FAHP with a fuzzy VIKOR (Višekriterijumsko kompromisno rangiranje) for selecting the best energy generating technology in India. Bozbura et al., [95], [96] and Demirel et al., [97] presented extensive comparisons of the different AHP fuzzy extents and their advantages and disadvantages.
It is essential to note that the studies introduced in this section obtained decision criteria directly through the literature or practical experience considering the perspectives of local technical teams associated with certain projects. In addition, the required quantitative data were mostly acquired from similar projects or international databases based on data from developed countries, due to lacking data availability in developing countries.
29