Development of Decision Model for Selection of Appropriate ... - J-Stage

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Development of Decision Model for Selection of Appropriate Power Generation System Using Distance Based Approach Method∗ Anugerah WIDIYANTO∗∗ , Seizo KATO∗∗∗ and Naoki MARUYAMA∗∗∗∗ For solving decision problems in electric generation planning, a matrix operation based deterministic quantitative model called the Distance Based Approach (DBA) has been proposed for comparing the technical-economical and environmental features of various electric power plants. The customized computer code is developed to evaluate the overall function of alternative energy systems from the performance pattern corresponding to the selected energy attributes. For the purpose of exploring the applicability and the effectiveness of the proposed model, the model is applied to decision problems concerning the selection of energy sources for power generation in Japan. The set of nine energy alternatives includes conventional and new energy technologies of oil fired-, natural gas fired-, coal fired-, nuclear power, hydropower, geothermal, solar photovoltaic, wind power and solar thermal plants. Also, a set of criteria for optimized selection includes five areas of concern; energy economy, energy security, environmental protection, socio-economic development and technological aspects for electric power generation. The result will be a ranking of alternative sources of energy based on the Euclidean composite distance of each alternative to the designated optimal source of energy. Key Words: Power Plant, Fossil Fuel Fired Power Generation, Alternative Energy, Energy Sources, Optimization, Multi Attributes, Electric Generation Systems

1.

Introduction

Generation system planning is one of the most crucial steps in the expansion plan of a modern electric utility. Expansion strategy choices at this stage have a tremendous effect on all other phases of system expansion and dictate the future posture of the project. Generation planning problems aim at determining the economical type and size of the generation plant(s) which should be constructed in ∗ ∗∗

∗∗∗

∗∗∗∗

Received 21st April, 2003 (No. 03-4042) Energy System Design Laboratory, Department of Mechanical Engineering, Mie University, 1515 Kamihamacho, Tsu, Mie 514–8507, Japan. E-mail: anugerah@es. mach.mie-u.ac.jp Energy System Design Laboratory, Department of Mechanical Engineering, Mie University, 1515 Kamihamacho, Tsu, Mie 514–8507, Japan. E-mail: seizo@ mach.mie-u.ac.jp Energy System Design Laboratory, Department of Mechanical Engineering, Mie University, 1515 Kamihamacho, Tsu, Mie 514–8507, Japan. E-mail: naoki@mach. mie-u.ac.jp

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order to satisfy a forecast demand for electricity. Many studies conducted for solving the decision problem in this area have led to the development of various solutions ranging from ones based on mathematical programming, stochastic approach, standard matrix to models that employ econometric and marginal analysis. However as the complexity of the problem increases due to the inclusion of objectives, the extension of model brings about more complexity in mathematical formulation, and creates a tedious computational process, which tends to reduce analysis efficiency. An exception to the above situation is the model that based on matrix operation, such as the Analytic Hierarchy Process (AHP), that has been proven to be practicable for solving complicated and elusive problem in many decision areas. However, this model was applied in far more problems which involved qualitative elements, as opposed to quantitative, that play an essential role in the decision problem. There is a need for the development of a method that requires relatively simple mathematical formulation for solving complicated and elusive multi attribute decision problem, as in the AHP. The method should be able Series B, Vol. 47, No. 2, 2004

388 to take any number of decision variables, without reducing its computational efficiency. The DBA (Distance Based Approach) method proposed below is one such attempt to accomplish these requirements. 2. Distance Based Approach The development of the DBA begins with defining the optimal state of the overall objective, and specifies the ideal good values of attributes involved in the process. In this study, the optimal state of the objective is represented by the optimum energy system, the OPT IMAL, so that the vector OP (x1 , x2 ,..., xn ) is considered to be the set of “optimum” simultaneous attribute values. In an n-dimensional space, the vector OP is called the optimal point. For practical purposes, the optimal good value for attributes is defined as the best value which exists within the range of values of attributes. The OPT IMAL, then, is simply the power system that has all of the best values of attributes. It may happen that a certain system has the best values for all attributes, but this is very unlikely. Instead, a variety of alternatives may be used to simulate the optimal state. For this reason, in this study, the OPT IMAL is not to be considered as a feasible alternative, but it is used only as reference to which other alternatives are quantitatively compared. The numerical difference resulting from comparison represents the effectiveness of alternatives to achieve the optimal state of the objective. The smaller numerical differences, the closer the alternative resembles the optimal states, and vice versa. Hence, here, the decision problem is to find a feasible solution (= power system alternative) which is as close to the optimal point as possible. The objective function for finding such a solution can be formulated as: Minimize δ {Alt(x), OPT IMAL} Subject to

xςX

(1) (2)

where Alt(x) represents a power system alternative in the n-dimensional space, and δ is the distance of points to optimal point. Thus, the problem and its solutions depend on the choice of the optimal point, OPT IMAL, and the distance metric, δ, used in the model. In two dimensional space, this solution function can be illustrated as in Fig. 1, where H is the feasible region and the OP is the optimal point. The approach determines the point in H region which is “the closest” to the optimal point, and is graphically explained in Fig. 2 for two dimensional cases. Note that lines (Alt − OP)X1 , and (Alt − OP)X2 are parallel to the X1 and X2 axis respectively. Consequently, (Alt − OP)X1 = |OPX1 −AltX1 |, and (Alt−OP)X2 = |OPX2 −AltX2 |, and based on Pythagoras theorem, δ = [(OPX1 − AltX1 )2 + (OPX2 − AltX2 )2 ]1/2

(3)

in two dimensional space. In general terms, the “distance Series B, Vol. 47, No. 2, 2004

Fig. 1 Distance based approach

Fig. 2 Distances of real vector

δ” can be formulated as: δ = [Σ(OPi j − Alti j )2 ]1/2

(4)

where i = 1,2,3,...,m = decision attribute, j = 1,2,3,...,n = alternative power system. To implement the above approach, first assume that there is a complete set of power system alternatives consisting of 1,2,3,...,n number of alternative power systems, and 1,2,3,...,m (number of power system attributes corresponding to each alternative), where Alt1 (X11 , X12 ,..., X1m ), and Alt2 (X21 , X22 ,..., X2m ),..., Altn (Xn1 , Xn2 ,..., Xnm ), and the OPT IMAL (Xb1 , Xb2 ,..., Xbm ), where Xbm = the best value of attribute m. The whole set of alternatives can be represented by the following matrix,    X11 X12 ... X1m     X21 X22 ... X2m   ... ... ... ...     Xn1 Xn2 ... Xnm    Xb1 Xb2 ... Xbm . Thus, in this matrix, a vector in an m-dimensional space represents every power system alternative. In order to expedite, the process, and at the same time to eliminate the influence of various units of measurement, the matrix is standardized using Z formula as: Zi j =

Xi j − X j Sj

N 1 Xi j n j=1 1/2  N   1  2  S j =  (Xi j − X j )  n j=1

Xj=

(5) (6) (7) JSME International Journal

389 where j = 1,2,3,...n

Table 1

Computational steps needed in analysis

m= number of different power system attribute n= number of power system alternative Xi j = indicator value for alternative power system i for attribute j S j = standard deviation of attribute j. The standardized matrix will be as follows:    Z11 Z12 ... Z1m     Z21 Z22 ... Z2m   ... ... ... ...     Zn1 Zn2 ... Znm  ZOP1 ZOP2 ... ZOPm where X11 − X 1 X12 − X 2 X1m − X m , Z12 = , Z1m = . Z11 = S1 S2 Sm The next step is to obtain the difference or distance from each alternative to the reference point, the OPT IMAL, by subtracting each element of the optimal by the corresponding element in the alternative set, which results in another interim matrix    Z1OP − Z11 Z1OP − Z12 ... Z1OP − Z1m     Z2OP − Z21 Z2OP − Z22 ... Z2OP − Z2m    ... ... ... ...   ZnOP − Zn1 ZnOP − Zn2 ... ZnOP − Znm . Finally, the Euclidean composite distance, CD, between each alternative power system to the optimal state OPT IMAL, is derived from the following formula: 1/2  m    2  (8) CDOP−Alt =  (Zi OP − Zi j )  . j=1

Within any given set of power system alternatives, the distance of each alternative to another is obviously a composite distance. In other word, it can be called the mathematical expression of several distances on each of several dimensions in which power systems can be compared. 3.

Differences from Other Methods

An attempt to depart from complexity of the formulation of objective and constraint functions that occurs when the mathematical programming model is used in a multiattributes decision problem, has led to the use of a somewhat simpler method like the standard matrix operation. Saaty(13), (14) , who for the first time in the mid 70’s introduced the Analytical Hierarchy Process (AHP), pioneered the work in this area, among others. To inherit the practical nature of the AHP, a similar mathematical formulation, namely, the multi-attributes justification approach based on the standard matrix operation, is used for modeling the decision making process in this study. The DBA employing simple and straightforward mathematical computation is potentially capable of solving a decision problem that traditionally is in the domain of AHP analysis. Using such a way, these results may be JSME International Journal

seen as a demonstration of the DBA internal consistency, and at the same time shows the external validity of the system. The applicability of the DBA analysis becomes more obvious when observed from the extent of the computational steps needed to arrive at the final ranking of energy alternatives. In the following demonstration the DBA requires only six computational steps (with 8 attributes). For the same problem with the same attributes involved, the AHP needs twenty computational steps (2m+4). The more attributes that are included in the process, the more computational steps are needed in the AHP analysis, while for DBA, the number of computational steps stays the same, regardless of the number of attributes included in the process. Table 1 summarizes the computational steps needed for both DBA and AHP in this model demonstration. Another advantage of using DBA is that the graphical pattern of influence of attributes on each alternative can be easily drawn from the standardized matrix. From the pattern, both critical and non-critical attributes can be identified directly, so that when a sensitivity test is needed, it is already apparent which attribute(s) should be manipulated. 4.

Model Demonstration

The objective of this demonstration is to develop a procedure for combining various attributes relevant to power plant operation into a single measure, whereby a comprehensive ranking of the alternative could be made. To begin with the model demonstration, let us assume a set of power system alternatives including coal, natural gas, nuclear, hydro, and photovoltaic solar power. A summary of attribute levels for the selection of a power system is presented in Table 2. This model is not meant to be exhaustive. Many other attributes may be included in the analysis depending upon the problem(s) and the decision maker preference(s). The model demonstration was conducted for the purpose of testing the applicability of the proposed model, the DBA, and to develop a procedure for an effective model Series B, Vol. 47, No. 2, 2004

390 Table 2 Valuation of attributes for each alternate energy

application. A hypothetical decision problem concerning the selection of energy sources for electricity generation was developed for this purpose. In this problem, five energy systems, namely, coal-fired, natural gas-fired, nuclear energy, hydro, and solar photovoltaic, were assumed to be a set of alternatives. At the same time, eight energy related attributes including capital cost, fuel cost, operating and maintenance cost, refurbishment cost, primary energy consumption (in the life cycle), global warming effect (in

  Alt1  Alt2   Alt3   Alt4   Alt5  OP

         

[ X1   32.8  33.5   32.4   0   30.5  33.5

X2 X3 X4

X5

X6

X7

0.7 0 1.9 2.7 2.7 2.7

1.1 1.4 0 10.6 11.4 11.4

0 410 968 931 972 972

2780 2920 2600 1400 0 2920

0.3 1.1 0 0.9 1.3 1.3

24.4 24.7 24.4 0 24.6 24.7

the life cycle), land use, and water requirement, were assumed to be the set of attributes. The following can represent the adjusted matrix of the process with the data from Table 2. Note, for some of attributes that have smaller numerical value for their best than the worst level, to avoid confusion and difficulties in performing the analysis, those values are adjusted using the formula; Adjusted value (Xa ) = Y − Xi , where Y > 0, and Y ≥ largest Xi .

X8 ]  28.5   28.8   28.3   32.0   0   32.0

From Eqs. (6) and (7), the average of attributes capital cost, fuel cost, operating and maintenance cost, refurbishment cost, primary energy consumption, global warming effect, land use, and water requirement, are 25.8, 1.6, 0.72, 19.6, 4.9, 656, 1 940, 23.5, and standard deviation, S j , are 12.9, 1.08, 0.49, 9.81, 5.01, 390, 1 110, 11.8, respectively. By using the Z formula in Eq. (5), the Standardized matrix will be as follows:    0.54 −0.83 −0.85 0.49 −0.76 −1.68 0.76 0.42     0.59 −1.48 0.77 0.52 −0.70 −0.63 0.88 0.45   0.51 0.28 −1.46 0.49 −0.98 0.80 0.59 0.40     −1.99 1.01 0.37 −2.00 1.14 0.70 −0.49 0.72     0.36 1.01 1.18 0.51 1.30 0.81 −1.75 −1.99    0.59 1.01 1.18 0.52 1.30 0.81 0.88 0.72 and the Distance Matrix can be represented by   0.05 1.84 2.03 0.03 2.06 2.49 0.13   0 2.49 0.41 0 2.00 1.44 0  0.08 0.74 2.64 0.03 2.28 0.01 0.29   2.59 0 0.81 2.52 0.16 0.10 1.37  0.23 0 0 0.01 0 0 2.63

 0.30   0.27   0.31   0   2.70 .

Finally, the following matrix is the required result of the Euclidean composite distance matrix    0.003 3.401 4.139 0.001 4.229 6.186 0.016 0.087   0 6.199 0.166 0 3.986 2.068 0 0.073     0.008 0.544 6.995 0.001 5.180 0 0.083 0.098     6.683 0 0.662 6.339 0.026 0.011 1.876 0    0.054 0 0 0 0 0 6.924 7.308 .

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391 Table 3

Rank of energy source alternative

(renewable energy) may require a higher investment, but the total operating cost in subsequent year are less. 5. Selection of Energy Sources

Fig. 3

Performance profile of each system

Employing the DBA principles that have been described previously, an analysis for measuring the effectiveness of each of the alternate energy options in terms of their appropriateness compared to the optimal state was conducted. By using Eq. (8), it can be seen in Table 3 that DBA quantitatively compares and ranks the energy source alternative on the basis of the Euclidean composite distance of each alternative to the optimal state. The result shows that among the alternative energy systems, the natural gas option has the smallest numerical score, followed by nuclear, hydro, solar PV, and coal fired power system. Figure 3 shows the performance profile of coal fired, natural gas fired, nuclear power, solar PV, and hydropower compared to OPTIMAL, respectively. In the case of the coal energy systems, for example, the graph clearly shows that critical attributes are fuel cost (X2), O&M cost (X3), and global warming (X6). In the case of natural gas fired power plants, fuel cost (X2), primary energy consumption (X5) and global warming (X6) are the critical attributes. It can be seen that in the case of nuclear power systems, O&M cost (X3), and primary energy consumption (X5) are the critical attributes. However, for the solar PV, capital-investment cost (X1), refurbishment cost (X4) and land-use (X7) are the critical attributes. The graph also clearly shows that capital cost (X1), land-use (X7), and water requirement (X8) are the critical attributes for hydropower system. It is clear that new energy technology Capital cost [¥/kWh] =

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For the purpose of exploring the applicability and the effectiveness of the proposed model, the DBA, the model was applied in a decision problem concerning the selection of energy source for power generation in Japan, involving a set of seven energy alternatives and twenty-three attributes. Most of the data required for the model application were obtained from various sources related to the power industry in Japan, both in published and public domains. It is clear that analysis of a complex system such as electric generation involves both quantifiable and nonquantifiable attributes. For the quantifiable attributes, there are readily available common or natural scales, such as [¥=/kWh] for attributes relevant to cost. On the other hand, for non-quantifiable attributes, measurement can only be done after the construction of a subjective scale. In this study, a (0 – 100) constructed scale is used for measuring the subjective attribute. Table 4 shows a summary of attribute levels for the selection of energy systems. The list consists of 23 attributes relevant to decision variable for energy selection, ranging from economical, technological, energy security, and environmental aspects of the problem. Many of the attributes are self-explanatory, but some subjective attributes will be discussed in the following. 5. 1 Energy economy aspects In this study, energy economy is defined as the cost of electricity generation. Generally there are two types of costs which are incurred in electricity generation. First, there are expenses associated with the annual operation of the plant, including fuel cost, operation and maintenance cost. Second, there are costs associated with the investment in the facilities necessary to generate electricity (capital investment in the plant, equipment and construction costs). The main component in the second group is the initial capital cost, which traditionally also becomes the determinant factor in the selection of energy sources for power generation. In this study, the cost of electric generation is classified into four attributes; capital cost, fuel cost, O&M cost, and refurbishment cost(2), (4), (7), (17), (18) . In this calculation, the assumed annual average load of each alternative is 75%, 75%, 75%, 45%, 60%, 70%, 20%, 35%, and 30% for oil-, natural gas-, coal-fired, hydro, geothermal, nuclear, solar PV, wind power, and solar thermal power plant, respectively(9), (15), (16) . Table 5 shows the generating cost of selected systems that used in the calculation. In here,

Construction cost [¥/kW] × CRF[-] Net electricity generated [kWh /kW]

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392 Table 4 Attribute levels for alternative energy systems

Table 5 Generating costs of selected systems

Fuel cost [¥/kWh] =

Heat rate [kcal/kWh] × Fuel price [¥/kg] Fuel calorific value [kcal/kg]

Where: r × (1 + r)n (1 + r)n − 1 r = Discount rate [%/year], n = Lifetime/capital recovery period [years]

Capital recovery factor (CRF)[-] =

Net electricity generated = Capacity [kW] × Capacity factor × 8 760 [h/year]

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393 5. 2 Energy security aspects As one of the largest economies in the world, Japan is a major consumer of energy. However, Japan is dependent on imported fuel for most of its energy. About 40% of Japan’s primary energy including imported fuel is consumed in electricity generation. Usually, the energy security aspects of an electric generation system is expected to satisfy the existing and projected demand of a stated reliable supply of electricity for supporting and maintaining national development. It is important to specifically consider factors that the directly related to power plant operation, such as future fuel availability. In this, the security aspect is represented by foreign dependency, length of fuel supply, and length of plant life(1), (8), (18) . 5. 3 Environmental aspects No source of electricity generation is free from environmental impact, either direct or indirect. Use of natural resources, such as land and water, and pollution of air and water, can be considered as direct impacts, while others such as aesthetic considerations, and social and habitat modification are indirect impacts that can not easily be assessed and compared, and tend to be more subjective in their valuation. 5. 3. 1 Land use Sizes of actual power plant sites, even for a given fuel type, vary over a considerable range, and depend upon factors such as individual utility design specifications, land costs, onsite- or offsite-waste disposal, and the installed capacity. A coal-fired power plant, for example, requires a larger area compared to an oil or natural gas plant, because it needs space for fuel stock as well as for ash disposal. A nuclear plant also occupies a considerably, large area, because it is mandated to have an exclusion area, which is, in many cases, utilized as a green belt area(4), (16) . 5. 3. 2 Air pollution One obvious impact of power generation, especially those fueled with energy sources, is air pollution. Fossil fuel plants emit SOx, NOx, CO, CO2 , HC, trace elements, particulates and even radio nuclides. OECD studies in this matter suggest that fossil fuel plants emit the most greenhouse gases and other particulates into the air, followed by the geothermal plant, nuclear power plant, and the rest of the renewable energy plants(7), (12) . 5. 3. 3 Discomfort Discomfort caused by the operation of an electric generating plant may take both physical and psychological forms. Either one can be measured only by using a subjective unit such as 0 to 100 points. Subjective judgments in this case are as follow: physical discomfort for coal is 45, 35 for nuclear, 20 for solar PV, and 50 for geothermal. In the case of geothermal power plants, for example, hydrogen sulfide emission often exceeds not only the ambient air quality standard, but the odor can easily be detected far beyond the plant area. On the contrary, the psychological discomfort is estimated as: JSME International Journal

15 for coal, 90 for nuclear, 10 for solar, and 40 for geothermal(6), (10) . 5. 4 Socio-economic aspects The close relationship between energy in general, and electricity in particular, with national development has been extensively discussed in a number of studies. In these studies, socio-economic aspects of power generation assumed to have important affects on the selection of energy sources or energy technology include employment creation, industrial development, and local participation. 5. 4. 1 Employment According to Maczakis(11) , every one million MWh increase in the electricity production in a power plant with conventional fuel (fossil or nuclear) may create additional employment of as many as 4 500 persons. Power plant run by renewable energy, in general, would have less impact on employment creation. The reason include the fact that they have to be offsited and load centers are difficult to build, which means they have less capability to support a large industrial load (11), (18) . 5. 4. 2 Industrial development For reasons similar to those described above, the development of renewable energy power plants would have less impact on development of other industry when compared to conventional fuel power plants. Oil and gas-fired plants have the most flexible sitting requirements; therefore, they are the most frequent energy used in industrial development(12) . 5. 5 Technological aspects Technological attributes relevant to electric generation used in this study are a direct reflection of the policies for minimizing potential problems by selecting proven technology, to maximize the participation of the local industries, and to encourage technological development. Except for routine shutdown for maintenance purposes, a power plant is expected to be operable at all times. However, this expectation is difficult to be attained when the plant is powered by renewable natural resources like flowing water, solar power, wind power, and to some degree hydrothermal power(3), (8) . Solar and wind power plants are by far, are the most sensitive to the daily variability of the flow source of energy. 6. Results and Discussion 6. 1 The full model analysis The result of full model analysis using all 23 attributes shows that among the conventional energy systems, the natural gas option has the smallest numerical score. In other words, it is the conventional energy option that closely resembles the optimal state (see Table 6). It also means that when the country chooses to use the energy technology that is already in place, the selection apparently should be the power plant fueled by natural gas. In the observations on the overall system including the new technology group such as nuclear, geothermal and Series B, Vol. 47, No. 2, 2004

394 solar reveals that the natural gas system has a better score followed by the nuclear option. In order to find the supporting factors for the natural gas option, an overall performance profile of nuclear and natural gas options is presented in Fig. 4. This graph clearly highlights the critical factors for both options. It can be seen that, in technological factors (X11 – X15), the Table 6 Composite distance values of energy systems

Fig. 4 Performance profile of natural gas and nuclear

Fig. 6

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nuclear option has a comparative advantage over the natural gas option. Figure 5 shows sensitivity analysis based on manipulation of cost factors (X1 – X4). These results show that the nuclear option continues to be a better alternative, even when the fuel cost (X2) of the nuclear option increased up to 60% of their base value. The full model analysis also reveals another interesting situation concerning the solar energy option. Contrary to popular belief, this renewable energy system is the least viable system for large base electricity generation. Further analysis on the basis of environmental (X16 – X23) aspects of the alternative, also confirms this. This situation is mainly the result of its high capital cost, low energy density, and relatively low operating efficiency. 6. 2 Partial analysis Figure 6 shows a summary of results of the analysis series, from which the general trend of the selectivity level of each alternative can be easily found. Using only attributes that are relevant to technology (X11 – X15), the result of the model analysis shows that the nuclear option is by far the most viable alternative for power generation.

Fig. 5 Sensitivity test for nuclear vs natural gas on cost factors

General trend of composite distance values for each energy system

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395 However, this situation no longer holds when the levels of socio-economic related attributes of the nuclear option are slightly lowered from its base levels. Comparative analysis of alternative energy systems on the basis of their cost (X1 – X4) and environmental (X16 – X23) related attributes, shows that except for the coal option, all of the conventional energy systems have better positions than the new energy technology group, with natural gas having the best CD score. Despite the above potential contribution, work in this model is far from completion. There is more work to be done to make this model more useful and applicable in many, areas of decision problem. Further work incorporating formal formulation of uncertainties aspect of the problem with probabilistic input data, and test it to a larger real world problem, would be a logical extension of this study. 7.

Concluding Remarks

The result of the model application using data related to power expansion in Japan demonstrates that, once a complete set of criteria for energy system selection, along with the set of alternatives and their levels of attributes are laid out, an effective justification process around multiattribute decision model DBA can be performed. This model allows a decision maker to perform, not just a general analysis, but also other various focused analysis regarding his or her personal preferences. Literally, the decision maker has unlimited choices in exploring the influences of different sets of attributes to the final decision. In-depth issue-specific analysis including sensitivity test, can be performed without any major adjustments. As the result of analysis using all 23 attributes shows that among the nine energy alternatives, the natural gas option has the best numerical score followed by nuclear, oil fired, hydropower, coal fired, wind power, solar thermal, geothermal, and solar PV. The above findings validate the effectiveness of the model, that even though it employs a relatively simple mathematical formulation and straight-forward matrix operation, it is capable of solving complex multi-attribute decision problems, incorporating both quantitative and qualitative factors. The usefulness of this model, however, can only be ascertained through extensive field testing, followed by further refinements.

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Series B, Vol. 47, No. 2, 2004