AU2020103059A4 - An Evaluation Method for the Economic Feasibility of Renewable Energy-saving Technology - Google Patents
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Abstract
The invention relates to the field of computer software, and discloses an evaluation
method for the economic feasibility of renewable energy-saving technology, which provides
reference for different decision-makers and promotes the popularization of renewable energy
saving technology. The method includes: a. determining the evaluation index term; b.
establishing a triangular fuzzy number -analytic hierarchy process data envelopment
comprehensive evaluation model; c. solving the comprehensive evaluation model. The scheme
based on the invention can analyze the initial cost, adjusted cost, baseline cost and incremental
cost synthetically, so as to promote the acceptance of consumers from the cost, and provide
reference for different decision-makers to promote the popularization of renewable energy
saving technology.
1/2
FIGURES
Determining the evaluation indicators.
Establishing a trianular fuzzy number -analytic
hierarchy process -data envelopment
comprehensive evaluation model;
Solving the comprehensive evaluation model
Figure 1
20000
15000 I.
10000
- initial cost
5000 - _ _Agsac 500 A ~justad cost
-Baselinecost
1 3 5 7 9 13 -x-Incremental cost
-5000
-10000
-15000
Figure 2
Description
1/2
Determining the evaluation indicators.
Establishing a trianular fuzzy number -analytic hierarchy process -data envelopment comprehensive evaluation model;
Solving the comprehensive evaluation model
Figure 1
20000
15000 I.
10000 - initial cost
5000 - 500 _ A_Agsac ~justad cost
-Baselinecost
1 3 5 7 9 13 -x-Incremental cost -5000
-10000
-15000 Figure 2
An Evaluation Method for the Economic Feasibility of Renewable Energy-saving Technology
[0001] The invention relates to the field of computer software, in particular to an evaluation method for the economic feasibility of renewable energy-saving technology.
[0002] According to the Green Economy report of UNEP, C02 emissions from energy
consumption of industrial country buildings in 2012 accounted for 1/3 of the global fossil fuel carbon dioxide emissions, and most of the greenhouse gas emissions from buildings came from fossil fuels. Therefore, it is an important task of the construction industry to advocate the development of renewable energy energy-saving technology. In order to promote the promotion of renewable energy use, China has promulgated the Evaluationstandardforapplicationofrenewable energy in buildings in recent years, the US EPA issued the 2014-2016"Renewable Fuel Standard", and Alberta, Canada implemented the "Renewable Fuel Standard" on April 1, 2011. There are also many literature studies on the use of renewable energy, for example, Khalid analysis evaluates the use of wind turbines, solar collectors and ground-source heat pumps in homes. Heravi reveals that passive solar energy is the best use of renewable energy in Iranian urban buildings. Deblois simulates the reduced cooling load of a new opaque household roof solar chimney structure. Zhu measures actual energy performance by combining energy-saving characteristics with solar energy applications. Karisek analyzed the application of green investment schemes in solar thermal systems and heat pumps in the Czech Republic by 2015.
[0003] However, consumers are not willing to pay for higher cost on renewable energy technologies at one time, although it will bring greater economic benefits. This makes the utilization of renewable energy difficult to be popularized. Therefore, economic factors become a key factor affecting the promotion of green building, and it is the root of the difficulty of promotion. And to break through the bottleneck of the promotion of renewable energy, it becomes inevitable to combine with the study of renewable energy use technology and incremental cost, which is conducive to the realization of the economic evaluation of the proposed renewable energy technology. At the same time, it is of great significance for promoting renewable energy utilization to reach the level of consumer acceptance in economy.
[0004] Therefore, it is necessary to establish an integrated and effective model to evaluate the economic feasibility of renewable energy technology based on consumers' willingness to buy (utility evaluation). In recent years, more and more scholars have done some research in this field; however, these studies either explore the application of renewable energy technology, analyze consumers' willingness to pay, or use different methods to evaluate cost-effectiveness. Few studies are based on consumer acceptance, and propose a comprehensive quantitative model to explore the economic feasibility of renewable energy technology.
[0005] The technical problems to be solved in the invention is to propose an evaluation method for the economic feasibility of renewable energy-saving technology, which provides references for different decision-makers and promotes the popularization of the energy-saving technology of renewable energy.
[0006] The scheme used to solve the above technical problems is as follows.
[0007] An evaluation method for the economic feasibility of renewable energy-saving technology includes the following steps:
[0008] a. Determining the evaluation indicators;
[0009] b. Establishing a triangular fuzzy number -analytic hierarchy process -data envelopment comprehensive evaluation model;
[0010] c. Solving the comprehensive evaluation model.
[0011] Further, the evaluation index in step a includes: input index, output index, initial cost, adjusted cost, baseline cost and incremental cost;
[0012] The input index is the added expense item in the life cycle. The initial purchase fee, the late renewal fee, and the maintenance fee are used as the input items respectively.
[0013] The output index is an indicator of the degree of consumer satisfaction degree, including the economy, comfort and functionality of the product or technology. A comprehensive consumer satisfaction degree is taken as the output item.
[0014] The initial cost is the sum of the initial values of the input items obtained from the field investigation.
[0015] The adjusted cost is the sum of the input items adjusted by the DEA method.
[0016] The baseline cost is the cost of obtaining hot water without using the solar water heater, which includes calculating the base cost respectively for electricity, liquid gas, and firewood.
[0017] The incremental cost is the difference between the adjusted cost and the base cost.
[0018] Further, step b includes following steps.
[0019] bl. Establishing TFN-AHP (triangular fuzzy number-level analysis) model:
[0020] b2. Establishing DEA (Data Envelopment) model.
[0021] Further, the methods for establishing the TFN-AHP model described in step bl includes following steps.
[0022] b11. Establishing comparison scale based on the triangular fuzzy theory.
[0023] b12. Establishing a judgment matrix based on the survey results according to the comparison scale.
[0024] The judgment matrix is as follows.
1,,1) (k,2,r,z,) ... (k,,,ri,,z.,) A[a,j(I/z 2 ,I/r,1/k1 2) (1,1,1) --. (k2Y,r,z 2Y)
[002 ] -1/z ,, ,,1/k,) (1/z,1/r,1k2) --- (1, 1,1)
[0026] A represents positive reciprocal matrix, aij=(kij, rij, zij), l/aij,(k12, r1 2, zi 2)=(1/ki 2 ,1/r2, 1/Z12), and aij > 0; Especially, aij represents the triangular fuzzy number, kij represents the minimum of the triangular fuzzy number, rij represents the average of the minimum and maximum of the triangular fuzzy number, and zij represents the maximum of the triangular fuzzy number.
[0027] b13. Carrying on an order and consistency inspection ofjudgement matrix:
[0028] If the matrixA=(r,), is a consistency judgment matrix, then the triangular fuzzy judgment matrix A=(aij) xy is a consistency fuzzy judgment matrix, in which x represents the total number of rows of the matrix and y represents the total number of columns of the matrix. The calculation is as follows.
[0029] A W = A.. -W
[0030] H rp
1 (A W)
[0031]A,,. Y Y i=1 Wi
[0032] Especially, W and .max represent the eigenvector and the maximum eigenvalue corresponding to the matrix W respectively, and y represents the order of matrix A.
[0033] CI= A y y- 1 CI
[0034]CR = RI
[0035] Especially, CI represents consistency index, CR represents consistency ratio, RI represents mean random consistency index, and it was considered as acceptable when CR <0.01.
[0036] b14. Calculating the comprehensive importance of each element according to the judgment matrix:
[0037] R
[038 R i=1 g' =2 $k,j=1 r$z i=1
[0038]IRg' k Io zi
[0039][Z xE;2I( 1= ____=I j
[0040] b15. Calculating the probability degree of each index greater than the other, and d'(Ai) is used to indicate it.
[0041] d'(A,)= min J-1,2,-,x;J# V(H H),i=1,2,---,y
[0042] The probability degree of two triangular fuzzy numbers Hi > H2. The calculation formula is as follows.
l,r 2 r
[0043]V(H ( H2)= k2 -zj k , ri <r2, z, >k2 (r - zi)-(r2 -2) 0, Others
[0044] b16. Normalizing the possibility degree to obtain the weight matrix:
[0045] Normalizing the possibility degree W'=(d'(A 1), d'(A 2), ... d'(Ay)) to obtain the weight matrix W=(d (A), d (A2 ). .. d (Ay)) in which i=1, 2,. .. , y; j=1, 2,. .. , x;
[0046] b7. Obtaining comprehensive satisfaction degree which was referred to as L by combining with the satisfaction of different aspects of consumers and the weight gained by step b16:
[0047]
[0048] The satisfaction degree is divided into nine categories: 1 point for totally no acceptance, 3 points for no acceptance, 5 points for acceptance, 7 points for equivalent acceptance, 9 points for full acceptance, with 2, 4, 6, 8 points in the middle, respectively;
[0049] After calculating the comprehensive consumer satisfaction degree according to the above formula, the object with less than 5 points is eliminated, and the remaining object is used as the decision-making unit in DEA.
[0050] Further, the DEA model established in step b2 includes following steps:
[0051] b21. Referring to the CCR model with non-Archimedes infinitesimals:
[0052] min(-C(s-+s')]=V,
[0053 ]s.t. OA -s* =O
_S- 10,s O'" 0
[0054] Especially, 0 represents relative efficiency, c represents arbitrary non-Archimedean infinitesimal, s-, s+ represent relaxation variable and residual variable respectively,VDjrepresents the optimal value, I represents the input value of decision-making unit, On represents the output value of decision-making unit, )n represents income scale used to judge decision-making unit, n is number of decision-making unit.
[0055] b22. Conversion of non-effective decision-making units:
[0056] First, the effective decision-making and non-effective decision-making units are determined by 0 judgement:
[0057] 0=1, the decision-making unit is valid
[0058] 0#1, the decision-making unit is not valid
[0059] Then, the non-effective decision-making unit is adjusted to the effective decision-making unit, it is as follows.
[0060] j=O0j 5 0
[0061] 0 O=O+s*°
[0062] Especially,00, s-0 ,s0 represent the optimal solution of the corresponding linear programming of the decision-making unit represents the relative effective projection value of the previous decision-making unit (Io, o).
[0063] b23. Ideal cost control: Calculating the adjusted cost of each decision-making unit according to the adjusted input. The formula is as follows.
M f= I
[0064] - =1
[0065] Especially, m represents the number of input items, m=l, 2,..., M.
[0066] Further, the solution of the comprehensive evaluation model described in step c includes following contents.
[0067] Based on the comprehensive evaluation model, the evaluation results of the related indexes are calculated after the input index, output index and decision-making unit are imported.
[0068] The beneficial effect of the invention is:
[0069] The scheme can analyze the initial cost, adjusted cost, baseline cost and incremental cost synthetically, so as to promote the acceptance of consumers from the cost, and to provide reference for different decision-makers to promote the popularization of energy-saving technology of renewable energy.
[0070] Figure 1 is the flowchart of the evaluation method of the invention;
[0071] Figure 2 is a diagram of the relationship between the initial cost, the adjusted cost, the baseline cost and the incremental cost of using electricity.
[0072] Figure 3 is the diagram of the relationship between the initial cost, the adjusted cost, the baseline cost and the incremental cost of using LPG.
[0073] Figure 4 is the diagram of the relationship between the initial cost, the adjusted cost, the baseline cost, and the incremental cost of using diesel oil.
[0074] The invention aims to put forward a method for evaluating the economic feasibility of renewable energy-saving technology, and provide reference for different decision-makers to promote the popularization of renewable energy-saving technology. The following is a step-by-step description of the scheme of the invention in conjunction with the appended drawings and embodiments.
[0075] As shown in Figure 1, the evaluation method for the economic feasibility of renewable energy-saving technology in the invention includes the following steps.
[0076] a. Determining the evaluation indicators;
[0077] b. Establishing a triangular fuzzy number -analytic hierarchy process -data envelopment comprehensive evaluation model;
[0078] c. Solving the comprehensive evaluation model.
[0079] In the specific implementation, the evaluation index items of the step a include: input index, output index, initial cost, adjusted cost, baseline cost and incremental cost.
[0080] The input index is the added expense item in the life cycle. The initial purchase fee, the late renewal fee and the maintenance fee are used as the input items respectively.
[0081] The output index is an indicator of the consumer satisfaction degree, including the economy, comfort and functionality of the product or technology. The comprehensive consumer satisfaction degree is taken as the output item.
[0082] The initial cost is the sum of the initial values of the input items obtained from the field investigation.
[0083] The adjusted cost is the sum of the input items adjusted by the DEA method.
[0084] The baseline cost is the cost of obtaining hot water without using the solar water heater, which includes calculating the base cost respectively for electricity, liquid gas and firewood.
[0085] The incremental cost is the difference between the adjusted cost and the base cost.
[0086] For the above step b, the establishment of a triangular fuzzy number-hierarchy analysis data envelopment comprehensive evaluation model includes:
[0087] bl. Establishing TFN-AHP model:
[0088] bI1. Establishing comparison scale based on the triangular fuzzy theory, and the comparison scale is established as shown in Table 1.
[0089] Table 1: lingual variable based on triangular fuzzy numbers
[0090]
Lingual variable Triangular fuzzy number Reciprocal of triangular fuzzy numbers
Equal importance (1,1,1) (1,1,1)
Intermediate value between the (1,2,3) (1/3,1/2,1) two
More important (2,3, 4) (1/4,1/3,1/2)
Intermediate value between the (3,4,5) (1/5,1/4,1/3) two
Important (4,5,6) (1/6,1/5,1/4)
Intermediate value between the (5,6,7) (1/7,1/6,1/5) two
Very importan (6,7,8) (1/8,1/7,1/6)
Intermediate value between the (7,8,9) (1/9,1/8,1/7) two
Extremely important (9,9,9) (1/9,1/9,1/9)
[0091] b12. Establishing a judgment matrix based on the survey results according to the comparison scale.
[0092] The judgment matrix is as follows.
(1,1,1) (k12 ,r2,z 12 ) --- (kr,z )
[00 9 3 ]A=a=(1/z,lI/r,1/k12) (1,1,1) (ky r Zy)
-(I1/z ,'1/r,,1/k ) (1/ Z2 y,1/r2,1/ ) --- (1,,1
[0094] In which A is a reciprocal matrix, aij=(kij, rij, zij), aij=/aij, that is (k 1 2 , r12,z1 2 )=(1/ki 2 ,1/r12,
1/z12), and aij > 0; Especially, aij represents a triangular fuzzy number, kij represents the minimum of the triangular fuzzy number, rij represents the mean of the triangular fuzzy number, and zij represents the maximum of the triangular fuzzy number.
[0095] b13. Carrying on consistency inspection of judgement matrix.
[0096] If the matrix Z=(r), is a consistency judgment matrix, then the triangular fuzzy judgment matrix A=(aij) xy is a consistency fuzzy judgment matrix, in which x represents the total number of rows of the matrix and y represents the total number of columns of the matrix. The calculation is as follows.
[0097] A -W =AsW
[0098] $ x-l r~ ya1 rJ
1 Y (A W)i
[0099]1- = Y i=1 W
[0100] Especially, W and max represent the eigenvector and the maximum eigenvalue corresponding to the matrix W respectively, and y represents the order of matrix A.
[0101] CI= "
y- 1 CI
[0102] CR = RI
[0103] Especially, CI represents consistency index, CR represents consistency ratio, RI represents mean random consistency index, and it was considered as acceptable when CR <0.01.
[0104] b14. Calculating the comprehensive importance of each element according to the judgment matrix:
y 01Y
[0105] 1j" i -
[0106]R k,$ ,$
[0107] - RgJ R=
[0108] b15. Calculating the probability degree of each index greater than the other, and it was denoted by d'(Ai).
[0109] d'(A,)= min V(HI 2 H ),i =1, 2 ,...,y
[0110] The probability degree of two triangular fuzzy numbers Hi1 H2. The calculation formula is as follows
1r, L- r
[0111] V(H 1 2 H 2 )={ , r, < r2 ,z, k2
2 ),0 Others
[0112] b16. Normalizing the possibility degree to obtain the weight matrix:
[0113] Normalizing the possibility degree W'=(d'(A1), d'(A2),.. . d '(Ay)) to obtain the weight matrix W=(d (Ai), d (A2 ). .. d (Ay)) in which i=1, 2,. .. , y; j=1, 2,. .. , x;
[0114] b7. Obtaining comprehensive satisfaction degree which was referred to as L by combining with the satisfaction of different aspects of consumers and the weight gained by step b16:
Y L Lix W,
[0115] i-1
[0116] The satisfaction degree is divided into nine categories: 1 point for totally no acceptance, 3 points for no acceptance, 5 points for acceptance, 7 points for equivalent acceptance, 9 points for full acceptance, with 2, 4, 6, 8 points in the middle, respectively;
[0117] After calculating the comprehensive consumer satisfaction degree according to the above formula, the object with less than 5 points is eliminated, and the remaining object is used as the decision-making unit in DEA.
[0118] b2. Establishing DEA model:
[0119] b21. Referring to the CCR model with non-Archimedes infinitesimals:
[0120] min[-e(s-+s )]=V
SI"A +s =01, N n=1 sat O"A -s* =00
[0121] 2,n=12.,N
[0122] Especially, 0 represents relative efficiency, , represents arbitrary non-Archimedean
infinitesimal, s-, s+ represent relaxation variable and residual variable, VD, represents the optimal value, In represents the input value of decision-making unit, On represents the output value of decision-making unit, kn represents income scale used to judge decision-making unit.
[0123] b22. Conversion of non-effective decision-making units:
[0124] Firstly, the effective decision-making and non-effective decision making units are determined by judging of0 value:
[0125] 0=1, the decision unit is valid
[0126] 0#1, the decision-making unit is not valid
[0127] Then, the non-effective decision-making unit is adjusted to the effective decision-making unit, it is as follows.
[0128] = Io=9°I0 -s-°
[0129] 0 00 =00 ±+S+
[0130] Especially,0 0 , s-0 ,s0 represent the optimal -olption for the corresponding linear programming of the decision-making unit. (Yo,00)represents the relative effective projection value of the previous decision-making unit (Io ,Oo);
[0131] b23. Ideal cost control: Calculating the adjusted cost of each decision-making unit according to the adjusted input. The formula is as follows. M
[0132]I = f in m r
[0133] Especially,mnrepresents the number of input items, m=l, 2,...
[0134] For the step c, the process of solving the model is the process of calculating the evaluation results of the relevant indexes after importing the input index, the output index and the decision making unit based on the comprehensive evaluation model. In the concrete implementation, the intelligent tool can be designed to calculate the model, which makes the implementation of the model more convenient and simple. The realization of the intelligent tool mainly consists of three parts: data import, calculation of relative effectiveness, adjustment of non-effective decision-making unit.
[0135] Embodiment:
[0136] The energy-saving technology of solar water heater was taken as the research object, the research site is Pan xi area, China. The case calculated the baseline cost respectively aiming at usage of electricity, liquefied gas and firewood. Through the investigation and the TFN-AHP method introduced in this paper, the input and output values were obtained, and 34 valid questionnaires were given, as shown in Table 2. The number 7 of electricity, the number 8 of liquefied and the number 3 of firewood were deleted as the comprehensive consumer satisfaction degree of them were less than points, and 13, 12 and 6 decision-making units for DEA model are obtained respectively.
[0137] Table 2: Input and output values for regional surveys
Iniout value(vuan) Outnut value (vuan) Object 11 13 01 1 4300.00 4080.45 3468.65 8.0000 2 2750.00 2609.59 3468.65 7.9233 3 2300.00 2182.56 2103.93 9.0000 4 2300.00 2182.56 2786.29 7.0000 5 4300.00 4080.45 3468.65 7.3589 6 2750.00 2609.59 2786.29 5.7307 Electricity 7 2750.00 2609.59 3468.65 4.1807 8 2750.00 2609.59 3468.65 5.0000 9 2750.00 2609.59 3468.65 5.8411 10 1800.00 1708.09 3468.65 6.3333 11 2750.00 2609.59 2786.29 9.0000 12 2300.00 2182.56 2786.29 9.0000 13 1800.00 1708.09 3468.65 8.0000 14 2750.00 2609.59 3468.65 8.0000 1 2750.00 2609.59 2454.32 6.0000 2 2300.00 2182.56 1937.95 5.0000 3 2750.00 2609.59 2970.70 9.0000 4 2750.00 2609.59 2454.32 9.0000 5 6700.00 6357.91 4785.05 8.2822 6 2750.00 2609.59 4785.05 5.0000 Liquefiedgas 7 2300,00 2182.56 3953.31 5.0000 8 4300.00 4080.45 4785.05 4.3745 9 2300.00 2182.56 3121.56 9.0000 10 2750.00 2609.59 4785.05 6.4043 11 2300.00 2182.56 3953.31 6.0000 12 2750,00 2609.59 4785.05 7.7162 13 2750.00 2609.59 4785.05 5.3031 1 2750.00 2750.00 2786.29 9.0000 2 2750.00 2750.00 3468.65 7.0000 3 3500.00 3500.00 3468.65 3.0000 Firewod 4 4300.00 4300.00 2786.29 6.0000 5 4300.00 4300.00 3468.65 8.1795 6 5350.00 5350.00 3468.65 9.0000
[0138] 7 3750.00 3750.00 2786.29 7.0000
[0139] According to the TFN-AHP-DEA model established in this application, the relative effectiveness of each decision-making unit were calculated for electricity, liquefied gas and firewood, and the non-effective decision-making unit was adjusted as an effective decision-making unit, and the initial cost, the adjusted cost, the baseline cost, the incremental cost were calculated. The aggregated data are shown in Table 3.
[0140] Table 3: Initial cost, adjusted cost, baseline cost and incremental cost after calculation
Decision- Initial cost Adjusted cost (yuan) Baseine cost (yuran) Incremental cost making unit Number (Y) yuan)
1 11849.10 5855.11 14984.56 -9129.45 2 8828.24 6211.54 14984.56 -8773.02 3 6586.50 6586.50 4994.85 1591.65 4 7268.86 5437.10 9989.71 -4552.60 5 11849.10 5386.03 14984.56 -9598.53 6 8145.88 4281.47 9989.71 -5708.23 7 8828.24 3919.74 14984.56 -11064.82 8 8828.24 4579.21 14984.56 -10405.36 9 6976.74 5523.49 14984.56 -9461.07 10 8145.88 6724.41 9989.71 -3265.29 11 7268.86 6990.46 9989.71 -2999.25 12 6976.74 6976.74 14984.56 -8007.82 13 8828.24 6271.57 14984.56 -8712.99 1 7813.91 5209.54 7559.71 -2350.18 2 6420.52 4341.17 3779.86 561.31 3 8330.29 7719.68 11339.57 -3619.89 4 7813.91 7813.91 7559.71 254.20 5 17842.96 7190.60 11339.57 -4148.97 6 10144.64 4224.04 11339.57 -7115.53 Liquefied gas 7 8435.87 4224.89 7559.71 -3334.82 8 7604.12 7604.12 3779.86 3824.26 9 10144.64 5410.54 11339.57 -5929.03 10 8435.87 5069.69 7559.71 -2490.02 11 10144.64 6519.82 11339.57 -4819.74 12 10144.64 4480.47 11339.57 -6859.10 1 8145.88 8145.88 0.00 8145.88 2 8828.24 6335.90 0.00 6335.90 Firewood 3 11166.74 5430.96 0.00 5430.96 4 11849.10 7402.74 0.00 7402.74 5 13895.48 8146.24 0.00 8146.24
[0141] 6 10094.82 6335.85 0.00 6335.85
[0142] According to the data in Table 3, the initial cost, adjusted cost, baseline cost and incremental cost of electricity, liquefied gas and firewood were analyzed, and the changes and relationships between them were obtained as shown in Figure 2-4. It can be seen that the adjusted cost is obviously reduced in the initial cost. In addition, the incremental cost of electricity and liquefied gas is almost negative even though the baseline cost is high. It shows that in the case of consumer acceptance, the use of electricity, liquid gas objects with solar water heaters to replace can bring economic benefits. However, for the object of using firewood, since the cost of using it is very small, this application ignores its base cost, so the incremental cost is equal to the adjusted cost. It is pointed out that if the cost of solar water heater is controlled reasonably, it can bring economic benefits and reach the purpose of popularization and use.
Claims (6)
1. An evaluation method for the economic feasibility of renewable energy-saving technology, Wherein it includes the following steps.
a. Determining the evaluation indicators;
b. Establishing a triangular fuzzy number -analytic hierarchy process -data envelopment comprehensive evaluation model;
c. Solving the comprehensive evaluation model;
2. The evaluation method for the economic feasibility of renewable energy-saving technology as described in claim 1, Wherein the evaluation index includes: input index, output index, initial cost, adjusted cost, baseline cost and incremental cost;
The input index is the added expense item in the life cycle. The initial purchase fee, the late renewal fee and the maintenance fee are used as the input items respectively.
The output index is an indicator of the consumer satisfaction degree, including the economy, comfort and functionality of the product or technology. The comprehensive consumer satisfaction degree is taken as the output item.
The initial cost is the sum of the initial values of the input items obtained from the field investigation.
The adjusted cost is the sum of the input items adjusted by the DEA method.
The baseline cost is the cost of obtaining hot water without using the solar water heater, which includes calculating the baseline cost respectively for electricity, liquid gas and firewood.
The incremental cost is the difference between the adjusted cost and the baseline cost.
3. The evaluation method for the economic feasibility of renewable energy-saving technology as described in claim 1, wherein step b includes the following steps.
b1. Establishing TFN-AHP model;
b2. Establishing DEA model.
4. The evaluation method for the economic feasibility of renewable energy-saving technology as described in claim 3, wherein the method for establishing the TFN-AHP model as described in step bl includes the following steps.
b11. Establishing comparison scale based on the triangular fuzzy theory;
b12. Establishing a judgment matrix based on the survey results according to the comparison scale. The judgment matrix is as follows.
A is positive reciprocal matrix, aij=(kij, rij, zij), aij=1/aij, (k 12, r12, z2)=(1/k12, 1/r12, 1/z12), and aij >0;
Especially, aij represents the triangular fuzzy number, kij represents the minimum of the triangular fuzzy number, rij represents the average of the minimum and maximum of the triangular fuzzy number, and zij represents the maximum of the triangular fuzzy number.
b13. Carrying on an order and consistency inspection of judgement matrix:
If the matrix 4=(rv)x, is a consistency judgment matrix, then the triangular fuzzy judgment matrix A=(aij) xy is a consistency fuzzy judgment matrix, and the calculation is as follows.
A-W = A. W
I Y (A W)i yi=1 (W
Especially, W and max represent the eigenvector and the maximum eigenvalue corresponding to the matrix W respectively, and y represents the order of matrix A.
CRCRCI =-C RI
Especially, CI represents consistency index, CR represents consistency ratio, RI represents mean random consistency index, and it was considered as acceptable when CR <0.01.
b14. Calculating the comprehensive importance of each element according to the judgment matrix:
Rg= k, r,, z, z r ZkJ b15. Calculating the probability degree of each index greater than the other:
The probability degree of two triangular fuzzy numbers H1 > H2. The calculation formula is as follows.
1,r, r
V(H H2)= « 2-ZI ri <r 2 ,z , k2 (r -zI)-(r2-k 2 )
0, Others
b16. Normalizing the possibility degree to obtain the weight matrix:
Normalizing the possibility degree W'=(d'(Ai), d'(A 2),.. . d '(Ay)) to obtain the weight matrix W=(d (Ai), d (A2 ). .. d (Ay)) in which i=1, 2,. .. , y; j=1, 2,. .. ,x;
b17. Obtaining comprehensive satisfaction degree which was referred to as L by combining with the satisfaction of different aspects of consumers and the weight gained by step b16:
The satisfaction degree is divided into nine categories: 1 point for totally no acceptance, 3 points for no acceptance, 5 points for acceptance, 7 points for equivalent acceptance, 9 points for full acceptance, with 2, 4, 6, 8 points in the middle, respectively.
After calculating the comprehensive consumer satisfaction degree according to the above formula, the object with less than 5 points are eliminated, and the remaining objects are used as the decision-making units in DEA.
5. The evaluation method for the economic feasibility of renewable energy-saving technology as described in claim 4, wherein the specific steps for establishing the DEA model in step b2 include following steps.
b21. Referring to the CCR model with non-Archimedes infinitesimals:
S2O,n =1,2,---,N s- >0 >0
Especially, 0 represents relative efficiency, , represents arbitrary non-Archimedean infinitesimal, s-, s+ represent relaxation variable and residual variable respectively, VD, represents the optimal value, In represents the input value of decision-making unit, On represents the output value of decision making unit, Xa represents income scale used to judge decision-making unit.
b22. Conversion of non-effective decision-making units:
Firstly, the effective decision-making and non-effective decision-making units are determined by judging of 0 value:
0=1, the decision-making units are effective
0#1, the decision-making unit is not valid
Then, the non-effective decision-making unit is adjusted to the effective decision-making unit, it is as follows.
O0 = O" + s*O Especially,0 0, s-0 ,s* represent the optimal solution for the corresponding linear programming
of the decision-making unit. (io,00)represents the relative effective projection value of the previous decision-making unit (o,Oo);
b23. Ideal Cost Control: Calculating the adjusted cost of each decision-making unit based on the adjusted input, the formula is as follows. For:
Especially, n represents the number of decision-making units, m represents the number of input items, m=l, 2,. .. , M.
6. . The evaluation method for the economic feasibility of renewable energy-saving technology as described in claim 1, wherein the solution of the comprehensive evaluation model described in step c includes following contents.
Based on the comprehensive evaluation model, the evaluation results of the related indexes are calculated after the input index, output index and decision-making unit are imported.
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