当前位置:首页 >> 电力/水利 >>

Multi-criteria decision-making in the selection of a renewable energy project


Renewable Energy 36 (2011) 498e502

Contents lists available at ScienceDirect

Renewable Energy
journal homepage: www.elsevier.com/locate/renene

Multi-crit

eria decision-making in the selection of a renewable energy project in spain: The Vikor method
J.R. San Cristóbal*
Marine Sciences Department, University of Cantabria, Escuela Técnica Superior de Náutica, German Gamazo, s/n Santander 39004, Spain

a r t i c l e i n f o
Article history: Received 18 February 2009 Accepted 24 July 2010 Available online 15 September 2010 Keywords: Renewable Energies Multi-criteria Vikor method

a b s t r a c t
One of the characteristics of the Spanish energy system is its high degree of dependence on imports. In 2005, the Spanish government approved the new Renewable Energy Plan in the following sectors: Windpower, Hydroelectric, Solar Thermal, Solar Thermo-electric, Photovoltaic, Biomass, Biogas and Biofuels. The aim of the Plan is to make it possible to reach the target of 12% of primary energy being met from renewable sources by 2010. When selecting one from various Renewable Energy investment projects different groups of decision-makers become involved in the process. Decision-making has to take into consideration several con?icting objectives because of the increasingly complex social, economic, technological, and environmental factors that are present. Traditional single-criterion decision-making is no longer able to handle these problems. The Compromise Ranking method, also known as the VIKOR method, introduces the Multi-criteria ranking index based on the particular measure of “closeness” to the “ideal” solution. In this paper, we apply the method in the selection of a Renewable Energy project corresponding to the Renewable Energy Plan launched by the Spanish Government. The method is combined with the Analytical Hierarchy Process method for weighting the importance of the different criteria, which allows decision-makers to assign these values based on their preferences. The results show that the Biomass plant option (Co-combustion in a conventional power plant) is the best choice, followed by the Wind power and Solar Thermo-electric alternatives. ? 2010 Elsevier Ltd. All rights reserved.

1. Introduction The exploitation of Renewable Energy (RE) sources has gained enormous interest during recent years. A rising awareness of environmental issues, due to the increase in negative effects of fossil fuels on the environment, the precarious nature of dependency on fossil fuel imports, and the advent of RE alternatives, has forced many countries, especially the developed ones, to use RE sources. These are environment-friendly and capable of replacing conventional sources in a variety of applications at competitive prices (Aras et al. [1]; Haralambopoulos and Polatidis [2]). The selection of various energy investment projects is a laborious task. Multiple factors that affect the success of an RE project must be analyzed and taken into account. Decision-making has to take into consideration several con?icting objectives because of the increasingly complex social, economic, technological, and environmental factors that are present. Different groups of decision-

* Tel.: ?33 942 201362; fax: ?33 942 201303. E-mail address: jose.sancristobal@unican.es. 0960-1481/$ e see front matter ? 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.renene.2010.07.031

makers become involved in the process, each group bringing along different criteria and points of view, which must be resolved within a framework of understanding and mutual compromise (concessions) (Haralambopoulos and Polatidis [2]). Traditional single-criterion decision-making is no longer able to handle these problems. The policy formulation for fossil fuels energy substitution by RE must be addressed in a multi-criteria context. The complexity of energy planning and energy projects makes Multicriteria analysis a valuable tool in the decision-making process. The Compromise Ranking Method, also known as the VIKOR method, is an effective tool in Multi-Criteria Decision-Making. This method introduces the Multi-criteria ranking index based on the particular measure of “closeness” to the “ideal” solution. In this paper, we show how the method can be used in the selection of an RE investment project. In order to do this, the method is applied to the Renewable Energy Plan launched by the Spanish Government in 2005 (Plan de Energias Renovables [3]). The paper is organised as follows. In the next section, we review the use of Multi-Criteria Decision-Making techniques in RE project investment. Then, the VIKOR method is applied to the selection of RE projects and, ?nally, there appears a concluding section with the main results of the paper.

J.R. San Cristóbal / Renewable Energy 36 (2011) 498e502

499

2. MCDM techniques and energy projects Multi-Criteria analysis, often called Multi-Criteria DecisionMaking (MCDM) or Multi-Criteria Decision Aid methods (MCDA), is a branch of a general class of Operations Research models which deal with the process of making decisions in the presence of multiple objectives. These methods, which can handle both quantitative and qualitative criteria, share the common characteristics of con?ict among criteria, incommensurable units, and dif?culties in design/selection of alternatives (Pohekar and Ramachandran [4]). Many speci?cations and categorisations exist. Following Guitoni and Martel [5], these methods can be assigned to one of the four following categories: (i) elementary methods; (ii) the single synthesizing criterion approach; (iii) the outranking synthesizing approach; and (iv) the mixed methods. Table 1 shows the main methods belonging to each of these categories. In general, MCDM methods are divided into Multi-Objective Decision Making (MODM) and Multi-Attribute Decision Making (MADM). The main distinction between the two groups of methods is based on the determination of alternatives. In MODM, also known as multi objective programming or a vector optimization/ maximization/minimization problem, the alternatives are not predetermined but instead a set of objective functions is optimized subject to a set of constraints. In MADM, where alternatives are predetermined, a small number of alternatives are to be evaluated against a set of attributes. The best alternative is usually selected by making comparisons between alternatives with respect to each attribute (Pohekar and Ramachandran [4]). MCDM methods have been widely used in RE projects in areas such as wind farm projects, geothermal projects, hydro-site selection, etc. MODM, Decision Support Systems, MADM (Analytical Hierarchy Process, PROMETHEE, ELECTRE, Multi-attribute utility theory), and Fuzzy programming have been the main MCDM methods applied to RE projects. Table 2 shows the application areas of these methods (Pohekar and Ramachandran [4]). MODM methods have been used in deciding the optimum mix of RE technologies in various sectors (Iniyan and Sumanthy [6], Suganthi and Williams [7], Sinha and Kandpal [8], Cormico et al. [9]) and RE energy-economy planning showing the interactions between the energy system and the economy (Borges and Antunes [10]). In AHP, a multiple criteria problem is structured hierarchically by breaking down a problem into smaller and smaller consistent parts. The goal (objective) is at the top of the hierarchy, criteria and subcriteria at levels and sub-levels of the hierarchy, respectively, and decision alternatives at the bottom of the hierarchy. The best alternative is usually selected by making comparisons between alternatives with respect to each attribute. This type of method has been used in RE planning (Mohsen and Akash [11], Wang and Feng [12], Ramanathan and Ganesh [13]), and Windfarm projects (Aras et al. [14], Lee et al. [15]). The PROMETHEE method uses the outranking principle to rank the alternatives, combined with ease of use and lessened complexity. It performs a pair-wise comparison of alternatives in order to rank them with respect to a number of criteria. The method has been used in geothermal projects (Goumas et al. [16], Goumas and Lygerou [17], Haralambopoulos and Polatidis [2]),
Table 1 List of some multi-criteria decision making methods (Guitoni and Martel [5]). Category Elementary methods Single synthesizing criterion Methods

Table 2 Application areas of multi-criteria methods. Method Multi-objective decision making Decision support systems Analytical Hierarchy Process PROMETHEE Application area RE planning [6e9] and RE economic planning [10] RE planning [28] RE planning [11e13] and wind farm projects [14,15] Geothermal projects [2,16,17], hydro-site selection [18] and parabolic solar cooker [19] RE planning [20,21] Solar energy projects [23] and RE planning [24] Wind site selection [25] and Solar system [26,27]

ELECTRE Multi-attribute utility theory Fuzzy programming

hydro-site selection (Mladineo et al. [18]) and for promoting parabolic solar cookers in India (Pohekar and Ramachandran [19]). The ELECTRE method is capable of handling discrete criteria of both quantitative and qualitative in nature, providing complete ordering of the alternatives. The method chooses alternatives that are preferred over most of the criteria and that do not cause an unacceptable level of discontent for any of the criteria. Based on a concordance, discordance indices and threshold values, graphs for strong and weak relationships are developed. These graphs are used in an iterative procedure to obtain the ranking of alternatives. Applications of this method in RE projects can be seen in Beccali et al. [20]; Georgopoulou et al. [21]. MAUT is concerned with the theory developed to help decision-makers assign utility values, taking into consideration the decision-maker’s preferences, to outcomes by evaluating these in terms of multiple attributes and combining these individual assignments to obtain overall utility measures (Keeney and Raiffa [22]). Selecting portfolios for solar energy projects (Golabi et al. [23]) and RE planning (Jones et al. [24]) are the main applications of this method in RE projects. Other decision-making tools used in RE investment projects are Fuzzy programming to evaluate solar system and wind site selection (Skikos and Machias [25], Mamlook et al. [26], Mamlook et al. [27]), Decision Support Systems for RE project planning (Georgeopoulos et al. [28]), and Geo-spatial multi-criteria analysis methodology used to deploy a wave energy farm (Nobre et al. [29]). Both the VIKOR method and TOPSIS method, which was developed by Huang and Yong [30] as an alternative to ELECTRE, are based on an aggregating function representing “closeness to the ideal” which originates in the compromise programming method. These two methods introduce different forms of aggregating function for ranking and different kinds of normalization to eliminate the units of criterion function (Opricovic and Tzeng [31]). Whereas the VIKOR method uses linear normalization and the normalized values do not depend on the evaluation unit of a criterion, the TOPSIS method uses vector normalization, and the normalized value could be different for a different evaluation unit of a particular criterion. As regards the aggregating function, the VIKOR method introduces an aggregating function representing the distance from the ideal solution, considering the relative importance of all criteria, and a balance between total and individual satisfaction. On the other hand, the TOPSIS

Outranking methods Mixed methods

Weighted sum, Lexicographic method, Conjunctive methods, Disjunctive method, Maximin method TOPSIS, MAVT (multi-attribute value theory), (UTA) utility theory additive, SMART (simple multiu-attribute rating technique, MAUT (multi-attribute utility theory), AHP (analytical hierarchy process), EVAMIX, Fuzzy weighted sum, Fuzzy maximin. ELECTRE, PROMETHEE, MELCHIOR; ORESTE; REGIME. QUALIFLEX, Fuzzy conjunctive/disjunctive method, Martel and Zaras method.

500

J.R. San Cristóbal / Renewable Energy 36 (2011) 498e502 Table 4 Criteria used to evaluate the alternatives. Name f1 f2 f3 f4 f5 f6 f7 Power (P) Investment Ratio (IR) Implementation Period (IP) Operating Hours (OH) Useful Life (UL) Operation and Maintenance Costs (O&M) Tons of CO2 avoided (tCO2/y) Unit KW V/KW Years Hours/year Years V * 10?3/KWh tCO2/y

method introduces an aggregating function including the distances from the ideal point and from the negative-ideal point without considering their relative importance. However, the reference point could be a major concern in decision-making, and to be as close as possible to the ideal is the rationale of human choice (Opricovic and Tzeng [31]). In this paper we show the use of the Compromise Ranking Method, also known as the VIKOR method, in the selection of a Renewable Energy project. The method is improved by introducing the Analytical Hierarchy Process for assigning the weights of relative importance of attributes. Similar approaches can be found in Rao [32], who applies the method for material selection for a given engineering application, or in Lihong et al. [33], who applies the VIKOR algorithm based on AHP and Shannon entropy in the selection of Thermal Power Enterprisés Coal suppliers in China. As the authors suggest, this combination allows the decision-maker to systematically assign the values of relative importance to the attributes based on their preferences. 3. Application One of the characteristics of the Spanish energy system is its high degree of dependence on imports. Eighty percent of energy consumption has to be met from imported sources. Spain imports approximately 64% of the coal, 99.5% of the oil and 99.1% of the gas it uses. Moreover, oil accounts for around 50% of primary energy consumption (Renewable Energy World [34]). On August 26, 2005 the Spanish government approved the new Renewable Energy Plan, which supersedes the Renewable Energy Promotion Plan dating back to 1999, for the following areas: Windpower, Hydroelectric, Solar, Biomass, Biogas and Biofuels. With the overall aim of making it possible to reach the target of 12% of primary energy being met from renewable sources, in 2010 electricity generation in Spain from renewable sources will account for 30.3% of gross consumption and liquid biofuels will account for 5.8% of petrol and diesel consumption for transport purposes. To do so, it has set more ambitious goals in those areas that have been developing successfully and has established new measures to support technologies that have not yet managed to take off. Of the different areas covered by the overall RE Project, we have selected as example for multi-criteria decision-making, only the alternatives for electric generation. These are shown in Table 3. The designed systems will be evaluated according to the criteria shown in Table 4. The attributes considered are: Power (P), Investment Ratio (IR), Implementation Period (IP), Operating Hours (OH), Useful Life (UL), Operation and Maintenance Costs (O&M) and tons of emissions of CO2 avoided per year (tCO2/y). These emissions are estimated by the Spanish Government according to the increase in RE projects [3]. The data considering the 13 alternative RE projects and 7 selection attributes are shown in Table 5.
Table 3 Alternatives for electric generation. Alternative A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 A13 Wind power P 5 MW Wind power 5 P 10 MW Wind power 10 P 50 MW Hydroelectric P 10 MW Hydroelectric 10 P 25 MW Hydroelectric 25 P 50 MW Solar Thermo-electric P ! 10 MW Biomass (energetic cultivations) P 5 MW Biomass (forest and agricultural wastes) P 5 MW Biomass (farming industrial wastes) P 5 MW Biomass (forest industrial wastes) P 5 MW Biomass (co-combustion in conventional central) P ! 50 MW Bio fuels P 2 MW

Assuming that each alternative is evaluated according to each criterion function, the compromise ranking could be performed by comparing the measure of “closeness” to the “ideal” solution, F*. The compromise solution Fc is a feasible solution that is the “closest” to the ideal solution and a compromise means an agreement established by mutual concessions (Opricovic and Tzeng [31]). The multi-criteria measure for compromise ranking is developed from the Lp-metric used as an aggregating function in a compromise programming method (Yu [35], Zeleny [36]):

( Lp;j ? 1 p

n Xh i?1

 . ip wi fi* ? fij fi* ? fi?

)1=p

N; j ? 1; 2; .; J

?1?

where L1,j (as Sj in Eq. (2)) and LN,j (as Rj in Eq. (3)) are used to formulate ranking measure. Within the VIKOR method, the various J alternatives are denoted as a1, a2,.,aj. For alternative aj the rating of the ith aspect is denoted by fij, i.e., fij is the value of the ith criterion function for the alternative aj; and n is the number of criteria. The compromise ranking algorithm VIKOR has the following four steps (Opricovic and Tzeng [31]). Step I: Determine the best fi* and the worst fi? values of all criterion functions, i ? 1,2,.,n. If the ith function represents a bene?t then fi* ? max fij and fi? ? min fij , while if the ith funcj j tion represents a cost fi* ? min fij and fi? ? max fij . Of the attrij j butes considered, Power, Operating Hours, Useful Life and Tons of emissions avoided are bene?cial attributes and so higher values are desirable. Investment Ratio, Implementation Period, and Operating and Maintenance Costs are non-bene?cial attributes and so lower values are desirable. Step II. Compute the values Sj and Rj, j ? 1,2,.,J by the relations

Sj ?

n X i?1

 .  fi* ? fi? wi fi* ? fij

(2)

Table 5 Alternatives and attributes for RE project selection. Alternatives Attributes P A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 A13 5000 10,000 25,000 5000 20,000 35,000 50,000 5000 5000 5000 5000 56,000 2000 IR 937 937 937 1.500 700 601 5.000 1.803 1.803 1.803 1.803 856 1.503 IP 1 1 1 1.5 2 2.5 2 1 1 1 1 1 1.5 OH 2350 2350 2350 3100 2000 2000 2596 7500 7500 7500 7500 7500 7000 UL 20 20 20 25 25 25 25 15 15 15 15 20 20 O&M 1.470 1.470 1.510 1.450 0.700 0.600 4.200 7.106 5.425 5.425 2.813 4.560 2.512 tCO2/y 1,929,936 3,216,560 9,649,680 472,812 255,490 255,490 482,856 2,524,643 2,524,643 2,524,643 2,524,643 4,839,548 5,905,270

J.R. San Cristóbal / Renewable Energy 36 (2011) 498e502

501

P Max fi* fi? 56,000 2000

IR Min 601 5000

IP Min 1 2.5

OH Max 7500 2000

UL Max 25 15

O&M Min 7.106 0.600

tCO2/y Max 9,649,680 255,490

h  . i fi* ? fi Rj ? max wi fi* ? fij
i

(3)

where wi are the weights of criteria, expressing the decisionmaker’s preference as the relative importance of the criteria. In the RE Plan launched by the Spanish government three stakeholders are involved: the government who subsidizes the projects, the banks that contribute with private funds and the development companies. It is these stakeholders who act as the decision-makers that must choose the most suitable RE project and who must, therefore, determine their preferences for weighting the importance of the different criteria. The weights of relative importance of the attributes may be assigned using AHP (Saaty [37]). The steps are explained below as follows (Rao [32]). 1. Find out the relative importance of different attributes with respect to the objective. To do so, one has to construct a pairwise comparison matrix using a scale of relative importance. The judgments are entered using the fundamental scale of the AHP. An attribute compared with itself is always assigned the value 1 so the main diagonal entries of the pair-wise comparison matrix are all 1. The numbers 3, 5, 7, and 9 correspond to the verbal judgments “moderate importance”, “strong importance”, “very strong importance”, and “absolute importance” (with 2, 4, 6, and 8 for compromise between the previous values). Assuming n attributes, the pair-wise comparison of attribute i with attribute j yields a square matrix An?n where aij denotes the comparative importance of attribute i with respect to attribute j. In the matrix, aij ? 1 when i ? j and aji ? 1=aij .

2. We need to know the vector W ? [W1,W2,.,WN] which indicates the weight that each criteria is given in pair-wise comparison matrix A. To recover the vector W from A we outline a method in a two-step procedure:  For each of the A’s column divide each entry in column i of A by the sum of the entries in column i. This yields a new matrix, called Anorm (for normalized) in which the sum of the entries in each column is 1.  Estimate Wi as the average of the entries in row i of Anorm. The weights calculated are WP ? 0.32; WIR ? 0.09; WOH ? 0.12; WUL ? 0.13; WO&M ? 0.04; WIP ? 0.03; WtCO2 =y ? 0:27. Once we have the pair-wise comparison matrix it is necessary to check it for consistency. Slight inconsistencies are common and do not cause serious dif?culties. We can use the following four-step procedure to check for the consistency in the decision-maker’s comparisons. From now on, W denotes our estimate of the decision-maker’s weight.  Compute AWT.  Find out the maximum Eigen value Xn T T lmax ? 1=n i?1 ith entry in AW =ith entry in W .  Compute the Consistency Index (CI) as follows: CI ? ?lmax ? ? n=n ? 1. The smaller the CI, the smaller the deviation from the consistency is. If CI is suf?ciently small, the decision-maker’s comparisons are probably consistent enough to give useful estimates of the weights for their objective. For a perfectly consistent decision-maker, the ith entry in AWT ? n (ith entry of WT). This implies that a perfectly consistent decision-maker has CI ? 0.  Compare the Consistency Index to the Random Index (RI) for the appropriate value of n, used in decision-making (Saaty, [37]). If (CI/RI) < 0.10, the degree of consistency is satisfactory, but if (CI/RI) > 0.10, serious inconsistencies may exist, and the AHP may not yield meaningful results The Eigen value, lmax, obtained is 7.73 and the Consistence Ratio is 0.093, which is less than the allowed value of 0.1. Thus, there is a good consistency in the judgments made. The values of Sj and Rj, obtained using Eqs. (2) and (3), are, respectively. Step III: compute the values Qj , by the relation

2

1 6 1=5 6 6 1=9 6 6 1=3 6 6 1=5 6 4 1=7 1

5 1 1=5 3 3 1=5 3

9 5 1 5 7 3 5

3 1=3 1=5 1 1 1=3 5

5 1=3 1=7 1 1 1=5 3

7 5 1=3 3 5 1 5

3 1 1=3 7 7 1=5 7 7 1=5 7 7 1=3 7 7 1=5 5 1

.   .   S? ?S* ??1?v? Rj ?R* R? ?R* Qj ? v Sj ?S*

(4)

A1 Sj Rj 0.713 0.301

A2 0.646 0.272

A3 0.371 0.183

A4 0.693 0.301

A5 0.621 0.273

A6 0.539 0.273

A7 0.536 0.266

A8 0.709 0.301

A9 0.698 0.301

A10 0.698 0.301

A11 0.681 0.301

A12 0.238 0.140

i13 0.545 0.319

Table 6 Values of Qj for different values of v. v 0 0.2 0.4 0.5 0.6 0.8 1 A1 0.901 0.923 0.944 0.955 1.326 0.897 1.009 A2 0.736 0.763 0.789 0.802 1.110 0.767 0.867 A3 0.242 0.251 0.259 0.263 0.364 0.251 0.284 A4 0.901 0.914 0.927 0.934 1.300 0.863 0.966 A5 0.742 0.757 0.771 0.778 1.082 0.725 0.814 A6 0.742 0.722 0.701 0.691 0.977 0.585 0.639 A7 0.706 0.691 0.676 0.669 0.944 0.576 0.632 A8 0.901 0.921 0.941 0.951 1.321 0.890 1 A9 0.901 0.916 0.931 0.939 1.307 0.872 0.977 A10 0.901 0.916 0.931 0.939 1.307 0.872 0.977 A11 0.901 0.909 0.917 0.921 1.286 0.843 0.941 A12 0 0 0 0 0 0 0 A13 1 0.930 0.861 0.826 1.191 0.621 0.652

502

J.R. San Cristóbal / Renewable Energy 36 (2011) 498e502

where S* ? minjSj; S? ? maxjSj; R* ? minjRj; R? ? maxjRj and v is introduced as a weight for the strategy of maximum group utility, whereas (1 ? v)is the weight of the individual regret. The solution obtained by minjSj is with a maximum group utility (“majority” rule), and the solution obtained by minjRj is with a minimum individual regret of the “opponent”. Normally, the value of v is taken as 0.5. However, v can take any value from 0 to 1. Table 6 shows the values of Qj (Eq. (4)) for different values of v. Step IV: rank the alternatives, sorting by the values S, R, and Q in decreasing order. The results are three ranking lists. Propose as a compromise solution the alternative A(1), which is the best ranked by the measure Q (minimum), if the following two conditions are satis?ed: a. Acceptable advantage. Q ?A?2? ? ? Q ?A?1? ? ! DQ , where DQ ? 1/ (J ? 1) and A(2) is the alternative with second position on the ranking list by Q; b. Acceptable stability in decision-making. The alternative A(1) must also be the best ranked by S or/and R. This compromise solution is stable within a decision-making process, which could be the strategy of maximum group utility (when v > 0.5 is needed), or “by consensus” (v z 0.5), or with veto (v < 0.5). If one of the conditions is not satis?ed, then a set of compromise solutions is proposed, which consists of: c. Alternative A(1) and A(2) if only condition b is not satis?ed, or d. Alternatives A(1), A(2),., A(M) if condition a is not satis?ed. A(M) is determined by the relation Q ?A?M? ? ? Q ?A?1? ? < DQ for maximum n (the positions of these alternatives are “in closeness”). Ranking the alternatives by the VIKOR method gives us, as a compromise solution and for all the values of v considered, the alternative A12. This alternative, a Biomass plant (Co-combustion in a conventional power plant) of P ! 50 MW is the best ranked by Q. In addition, conditions IV-a and IV-b are satis?ed as this alternative is also the best ranked by S and R, and Q ?A?3? ? ? Q ?A?12? ? ! DQ . 4. Conclusions Selecting the best from various Renewable Energy investment projects requires that different groups of decision-makers become involved in the process. The fact that social, economic, technological and environmental factors need to be taken into consideration in decision-making, make the process more complex. Traditional single-criterion decision-making is no longer able to handle these problems properly. The policy formulation for fossil fuels energy substitution by Renewable Energies must be addressed in a multicriteria context. In this paper, we have shown how the VIKOR method, which introduces the multi-criteria ranking index based on the particular measure of “closeness” to the “ideal” solution, can be used in the selection of a Renewable Energy project. Combining the VIKOR method with AHP for weighting the importance of the different criteria, allows the decision-maker to systematically assign the values of relative importance to the attributes based on their preferences. The results show that the Biomass plant alternative (cocombustion in a conventional power plant) is the best choice, followed by the Windpower 10 P 50 MW and Solar Thermo-electric alternatives. The greater weight that the decision-makers have given to the criteria of Power (KW) and amount of tCO2/y avoided, together with the highest values of these two criteria corresponding to the Biomass plant and Wind power alternatives against the highest Investment Ratio corresponding to the Solar Thermo-electric installation, has meant that the Biomass plant is the best choice.

References
[1] Aras H, Erdogmus S, Koc E. Multi-criteria selection for a wind observation station location using analytic hierarchy process. Renew Energy 2004;23:1383e92. [2] Haralambopoulos DA, Polatidis H. Renewable energy projects: structuring a multicriteria group decision-making framework. Renew Energy 2003;28:961e73. [3] Plan de Energías Renovables en Espa?a 2005e2010. Instituto para la diversi?cación y ahorro de energía. Ministerio de Industria, Turismo y Comercio; 2005. [4] Pohekar SD, Ramachandran M. Application of multi-criteria decision making to sustainable energy planning e A review. Renew Sustain Energy Rev 2004;8:365e81. [5] Guitoni A, Martel JM. Tentative guidelines to help choosing an appropriate MCDA method. Eur J Oper Res 1998;109:501e21. [6] Iniyan S, Sumanthy K. An optimal renewable energy model for various enduses. Energy Int J 2000;25:563e75. [7] Suganthi L, Williams A. Renewable energy in India e a modelling study for 2020e2021. Energy Pol 2000;28:1095e109. [8] Sinha CS, Kandpal TC. Optimal mix of technologies in rural area: the cooking sector. Int J Energy Res 1991;15:85e100. [9] Cormico C, Dicorato M, Minoia A, Trovato M. A regional energy planning methodology including renewable energy sources environmental constraints. Renew Sustain Energy Rev 2003;7:99e130. [10] Borges AR, Antunes CH. A fuzzy multiple objective decision support model for energy-economy planning. Eur J Oper Res 2003;145(2):304e16. [11] Mohsen MS, Akash BA. Evaluation of domestic solar water heating system in Jordan using analytical hierarchy process. Energy Convers Manage 1997;38 (18):1815e22. [12] Wang X, Feng Z. Sustainable development of rural energy and its appraisal system in China. Renew Sustain Energy Rev 2002;6:395e404. [13] Ramanathan R, Ganesh LS. A multi-objective programming approach to energy resource allocation problems. Int J Energy Res 1990;17:105e19. [14] Aras H, Erdogmus S, Koc E. Multi-criteria selection for a wind observation station location using analytic hierarchy process. Renew Energy 2004;23:1383e92. [15] Lee AHI, Chen HH, Kang HY. Multi-criteria decision making on strategic selection of wind farms. Renew Energy 2009;34:120e6. [16] Goumas MG, Lygerou VA, Papayannakis LE. Computational methods for planning and evaluating geothermal energy projects. Energy Pol 1999;27:147e54. [17] Goumas MG, Lygerou VA. An extension of the PROMETHEE method for decision-making in fuzzy environment: ranking of alternative energy exploitation projects. Eur J Oper Res 2000;123:606e13. [18] Mladineo N, Margeta J, Brans JP, Mareschal B. Multicriteria ranking of alternative locations for small scale hydro plants. Eur J Oper Res 1987;31:215e22. [19] Pohekar SD, Ramachandran M. Multi-criteria evaluation of cooking energy alternatives for promoting parabolic solar cooker in India. Renew Energy 2004;29(9):1449e60. [20] Beccali M, Cellura M, Mistretta M. Decision-making in energy planningapplication of the ELECTRE method at regional level for the diffusion of renewable energy technology. Renew Energy 2003;28(13):2063e87. [21] Georgopoulou E, Lalas D, Papagiannakis L. A multi-criteria decision aid approach for energy planning problems: the case of renewable energy option. Eur J Oper Res 1997;103(1):38e54. [22] Keeney RL, Raiffa H. Decisions with multiple objectives: preferences and value tradeoffs. New York: Wiley; 1976. [23] Golabi K, Kirkwood CW, Sicherman A. Selecting a portfolio of solar energy projects using multiattribute preference theory. Manage Sci 1981;22(2):174e89. [24] Jones M, Hope C, Hughes R. A multi-attribute value model for the study of UK energy policy. J Oper Res Soc 1990;41(10):919e29. [25] Skikos GD, Machias AV. Fuzzy multi criteria decision making for evaluation of wind sites. Wind Eng 1992;6(4):213e28. [26] Mamlook R, Akash BA, Mohsen MS. A neuro-fuzzy program approach for evaluating electric power generation systems. Energy 2001;26:619e32. [27] Mamlook R, Akash BA, Nijmeh S. Fuzzy set programming to perform evaluation of solar system in Jordan. Energy Convers Manage 2001;42:1717e26. [28] Georgopoulou E, Sara?dis Y, Diakoulaki D. Design and implementation of a group DSS for sustaining renewable energies exploitation. Eur J Oper Res 1998;109(2):483e500. [29] Nobre A, Pacheco M, Jorge R, Lopes MFP, Gato LMC. Geo-spatial multi-criteria analysis for wave energy conversion system deployment. Renew Energy 2009;34:97e111. [30] Hwang CL, Yoon K. Multi-attribute decision making: methods and applications. Berlin: Springer-Verlag; 1981. [31] Opricovic S, Tzeng GH. Compromise solution by MCDM methods: a comparative analysis of VIKOR and TOPSIS. Eur J Oper Res 2004;156:445e55. [32] Rao RV. A decision making methodology for material selection using an improved compromise ranking method. Mater Design 2008;29:1949e54. [33] Lihong M, Yanping Z, Zhiwei Z. Improved VIKOR algorithm based on AHP and Shannon entropy in the selection of Thermal Power Enterprise’s coal suppliers. In: International Conference on Information Management, Innovation Management and Industrial Engineering; 2008. [34] Renewable Energy World. Available from: RenewableEnergyWorld.com; 2009. [35] Yu PL. A class of solutions for group decision problems. Manage Sci 1973;19 (8):936e46. [36] Zeleny M. Multiple criteria decision making. New York: McGraw-Hill; 1982. [37] Saaty TL. Fundamentals of decision making and priority theory with AHP. Pitsburg: RWS Publications; 2000.


相关文章:
14001标准相关词汇中英文对照表
the label 33 basic criteria 34 Best Available ...decision-making 82 declaration of conformity 83 ...project 263 protection of the label 264 provide ...
LEED GA 考试题库03
building criteria for developers, owners and ...primary importance for the project team to ...Availability of Renewable Energy Certificates (RECs)...
ISO 14000 词汇
the label 授與標章 B basic criteria 基本準則 ...decision-making 決策 declaration of conformity 符合...project 專案計畫 protection of the label 標章之...
...of international construction projects_图文
Moreover, as the project outcomes are affected by...support risk-related decision-making in practice. ...[32] developed an on-line multi-criteria risk ...
On the human resources building construction enterp...
management of multi-agency projects and change . ... first strategic business decision-making and ...criteria, using appropriate performance assessment ...
更多相关标签: