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Comprehensive evaluation of coal-fired power plants based on grey relational analysis and analytic hierarchy process
Energy Policy 39 (2011) 2343–2351
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Energy Policy journal homepage: www.elsevier.com/locate/enpol
Comprehensive evaluation of coal-fired power plants based on grey relational analysis and analytic hierarchy process Gang Xu a,n, Yong-ping Yang a, Shi-yuan Lu a, Le Li a, Xiaona Song b a
Key Lab of Condition Monitoring and Control for Power Plant Equipment of Ministry of Education, School of Energy Power and Mechanical Engineering, North China Electric Power University, Beijing 102206, China b Electromechanical Practice Center, Beijing Information Science and Technology University, Beijing, China
a r t i c l e i n f o
abstract
Article history: Received 25 August 2010 Accepted 26 January 2011 Available online 26 February 2011
In China, coal-fired power plants are the main supplier of electricity, as well as the largest consumer of coal and water resources and the biggest emitter of SOx, NOx, and greenhouse gases (GHGs). Therefore, it is important to establish a scientific, reasonable, and feasible comprehensive evaluation system for coal-fired power plants to guide them in achieving multi-optimisation of their thermal, environmental, and economic performance. This paper proposes a novel comprehensive evaluation method, which is based on a combination of the grey relational analysis (GRA) and the analytic hierarchy process (AHP), to assess the multi-objective performance of power plants. Unlike the traditional evaluation method that uses coal consumption as a basic indicator, the proposed evaluation method also takes water consumption and pollutant emissions as indicators. On the basis of the proposed evaluation method, a case study on typical 600 MW coal-fired power plants is carried out to determine the relevancy rules among factors including the coal consumption, water consumption, pollutant, and GHG emissions of power plants. This research offers new ideas and methods for the comprehensive performance evaluation of complex energy utilisation systems, and is beneficial to the synthesised consideration of resources, economy, and environment factors in system optimising and policy making. & 2011 Elsevier Ltd. All rights reserved.
Keywords: Multi-objective evaluation Improved grey relational analysis Coal-fired power plant
1. Introduction In China, coal-fired power plants are the main supplier of electricity, as well as the largest consumer of coal and water resources. Besides, these power plants discharge a large amount of pollutants and greenhouse gases (GHGs). For instance, in 2008, the electricity generated by coal-fired power plants accounted for about 81.2% of the total production of the country; at the same time, approximately 60% coal and 20% industrial water out of the total consumption in the country were expended by these plants. Moreover, the corresponding SO2, NOx, and CO2 emissions by these coal-fired power plants reached 45.8%, 50%, and 48%, respectively, of the total amount discharged in the country (China Electricity Council, 2009). To respond to this challenge, China has introduced various types of pollutant treatment equipment to a large number of power plants to alleviate the negative impacts on the environment (China Electricity Council, 2009). For example, various environmental protection equipments, such as dedusting, desulfurization, and denitration equipments are getting more and more applications in China’s power plants. Numerous dry cooling coal-fired power
n
Corresponding author. E-mail addresses: [email protected] (G. Xu), [email protected] (Y.-p. Yang). 0301-4215/$ - see front matter & 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.enpol.2011.01.054
generation units that can decrease water consumption rate by approximated 70% have been constructed in water-deficient regions. Moreover, CO2 capture technologies have drawn increased attention; consequently, several small-scale CO2 capture demonstration equipments have already been put into operation in power plants. Pollutant treatment and CO2 capture, however, may consume extra energy and water, inevitably increasing coal and water consumption rate to a certain extent. Compared with wet cooling power generation units, the coal consumption rate of a dry cooling unit will increase by 5–15%. Thus so far, the most commonly used evaluation criterion for coal-fired power plants in China is coal consumption rate, which denotes the standard coal consumption amount per kWh electricity. Although it can satisfactorily indicate the primary techno-economic performance of power plants, it can hardly reflect performance levels for other areas of power plant operations, including environmental pollution and water consumption (Greening and Bernow, 2004). Thus, as a single criterion, coal consumption rate cannot comprehensively reflect plant’s performance in its entirety. Even worse, in adopting a single criterion, power plants with various environmental protection and water conservation equipment installed cannot obtain a fair evaluation; this hinders the improvement of environmental protection measures and pollutant control in power plants. Therefore, establishing a scientific, reasonable, and feasible comprehensive evaluation system for coal-fired power plants is imperative.
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In view of these, many scholars have studied on the evaluation methods of power plants. Chatzimouratidis and Pilavachi (2009) used the analytic hierarchy process (AHP) to evaluate the technological, economic, and sustainability performance of a power plant, and presented the advantage of using AHP in the analysis of a complex problem, such as the multi-objective performance of various power plants. However, AHP is a subjective evaluation method; thus, the evaluation of power plant performance using this approach lacks objectivity and reliability. Montanari (2004) used a new evaluation method called ‘‘Technique for Order Preference by Similarity to Ideal Solution’’ (TOPSIS), a combination of an ideal solution method and AHP method, to classify the environmental performance of a power plant based on several single-objective performance indices, including SO2, NOx, CO2 emissions rates, and so on. Through this method, highly precise results can be obtained. However, the calculation for TOPSIS is highly complex and requires a large amount of operating data. Kim (2007) analysed neoclassical and institutional economics methodologies to evaluate the environmental and social effects of electricity generation, in which social cost analysis and the multicriteria decision method were adopted in the evaluation of the effects of various forms of power generation on the environment from an economic perspective. However, this method laid particular emphasis on economic theory and is quite difficult to understand. Generally, in the comprehensive evaluation of the performance of a complex system, such as a power plant, the indicators used for the evaluation should not be independent of each other, but correlated; such a condition accords with the characteristics of a ‘‘grey system’’ (Liu et al., 2000). The grey relational analysis (GRA) method is a comprehensive evaluation approach for the grey system. Since its introduction in 1982, it has been widely used in various fields of science because of its advantages in modelling, control, prediction, and decision-making (Lin et al., 2007; Yuan et al., 2010). For example, Wang et al. (2008) used the GRA method to achieve a comprehensive evaluation of the distributed triple-generation systems from the perspectives of technical, economic, environmental, and social performance. However, the objective index weight determination method adopted in the paper is complex and incomprehensible. In addition, although the GRA method is a widely used evaluation method suitable for the grey system; thus so far, no open literatures have reported the use of this method in the performance evaluation of coal-fired power plants. This paper presents a novel comprehensive evaluation method, which is based on the combination of GRA with AHP, to assess the multi-objective performance of power plants. Using the novel assessment method, several typical Chinese 600 MW power generation power plants are evaluated and compared. Besides, the relationships between the comprehensive performance of the power plants and the primary single indices are also discussed in the paper.
2. Evaluation indices of power plant performance The comprehensive performance of a power plant is affected by various factors that cannot be neglected. At present in China, three key types of indices currently used should be taken into account in power plant’s evaluation. These indices are discussed briefly in the following sections. 2.1. Resource consumption index 2.1.1. Coal consumption rate Coal consumption rate is one of the most important indices representing power plant performance. Thus, it is widely used in techno-economic comparisons among various power plants.
The rate represents the amount of standard coal consumed per kWh and can be deduced from net efficiency, according to the following equation: b ¼ 3600=ðHstd Znet Þ
ð1Þ
where b is the coal consumption rate, g/kWh (which means ‘‘gram standard coal per kWh’’); Hstd denotes the heat value of the standard coal, which is equal to 29.308 kJ/g; and Znet is the net efficiency of the power plant. As a whole, coal consumption rate is an important technoeconomic index that can reflect the rationality, operating status, maintenance quality, and management level of a power plant. 2.1.2. Water consumption rate In the past, the importance of water consumption has always been neglected in China. However, upon realizing that water resources are limited and valuable, water consumption has been considered a key index for reflecting the environmental performance of an enterprise, particularly in water-deficient regions, such as Northwest China. Therefore, a comprehensive evaluation of power plants should include the water consumption index. 2.2. Pollutant index Pollutant emissions from coal burning are a major contributor to environmental pollution in China. Therefore, incorporating pollutant emissions into the evaluation system is necessary. In general, pollutant emissions from power plants are composed mainly of exhausts (gaseous pollutants), water pollutants, and solid contaminants. 2.2.1. Exhausts In this study, exhausts refer to gaseous pollutants; generally, the main exhausts of power plants include SO2, NOx, and particulate matter (PM10). Since 2004, detailed rules for pollutant emissions have been issued by NDRC et al. (2003). The charging standards of exhausts are shown in Table 1. 2.2.2. Water pollutants Discharged wastewater from coal-fired power plants consists of sluicing water, circulating cooling water, foul drainage, chemical wastewater, oily wastewater, coal-bearing wastewater, desulfuration wastewater, and sanitary drainage. The main pollutants in the wastewater of power plants include chemical oxygen demand (COD), suspended substances (SS), oil, and fluoride. Since 2004, detailed rules for pollutant emissions have been issued by NDRC et al. (2003) and their pollution equivalent values are shown in Table 2. 2.2.3. Solid contaminants The solid contaminants from power plants include mainly ash and slag. Ash discharge accounts for about 80% of solid waste Table 1 Charging standards of exhausts. Pollutants
SO2
NOx
PM10
Charging standards (CNY/t)
630
630
220
Table 2 Pollution equivalent value of water pollution. Pollutants
COD
SS
Oil
Fluoride
Pollution equivalent value
1
0.25
10
2
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3.1. Establishing the hierarchical structure of evaluation
Table 3 Charging standards of solid contamination. Pollutants
Ash
Slag
Charging standards (CNY/t)
30
25
discharge. Ash falling into water causes river siltation, and the toxic chemicals in ash bring forth serious harm to creatures and humans alike. Slag discharge is considerably less copious compared with that of ash, but still requires proper treatment because of its heavy metal component. In general, slag can be utilised as construction material. The charging standards of solid contamination are displayed in Table 3 (NDRC et al., 2003).
2.3. GHG index Climate change continues to develop at an alarming rate so that an increasing number of countries and international organisations have been exerting joint efforts to address environmental issues. One of the primary measures for alleviating climate deterioration is reducing GHG emissions (Xu et al., 2010a; Working Group III of IPCC, 2005). Among the known GHGs, CO2, whose accumulation is caused mainly by human activities, has the highest discharge amount and contributes the most to the greenhouse effect (Working Group III of IPCC, 2005; Xu et al., 2010b). China is one of the biggest CO2-emitting countries, accounting for approximately one fifth of the world’s total CO2 emission (Jin et al., 2008). The GHG discharged from coal-fired power plants includes CO2 and N2O, but the amount of the former is far higher than that of the latter. In China, coal-fired power plants are the largest CO2-emitting resources and account for nearly half of the country’s total CO2 emission. In comparison, the amount of N2O discharged from coal-fired power plants is minimal.
3. Methodology of the comprehensive evaluation combined GRA with AHP Grey system theory was put forward in 1982. Since then, it has been widely used in various fields of science because of its advantages in evaluating complex systems with various correlated indicators. However, although the GRA method can easily reflect the preferential order of different investigated objects according to a certain performance index, distinguishing the relative significance among different types of indices is difficult. In comparison, AHP is a classic method used for evaluating relative significance among indices. Therefore, integrating the two methods enables the maximisation of the advantages of both. This combination also facilitates the multi-objective performance evaluation of a complex system. Hence, a novel comprehensive evaluation method combining GRA and AHP is proposed to achieve multi-hierarchy and multi-criteria evaluation. The proposed method uses GRA to determine the preferential order of each index and AHP to obtain the weights of the evaluation indices. The hierarchical structure of evaluation is established first and then the grey relational coefficient matrix is obtained through the GRA methodology. Subsequently, the weights of various indices are calculated through the AHP method. Eventually, the grey relational degree is obtained based on the grey relational coefficient matrix and the weights of the indices. The methodology is described in detail as follows.
According to the proposed method, the evaluation of a complex problem can be decomposed into a hierarchical structure of several layers (Saaty, 1990). After construction of the hierarchical structure, we can carry out the comprehensive evaluation of the indices of each layer by GRA and AHP methodology, conducting the evaluation from the bottom to the top layer. Once the evaluation of a layer is completed, the obtained results are then used to proceed with the evaluation of the succeeding layer until the final result is obtained. The hierarchical structure of a traditional coal-fired power plant can be decomposed into 3 index layers (Table 4). 3.2. Establishing the grey relational coefficient matrix The basic thought of grey relational theory is to ascertain the correlation degree according to the similarity among the sequence curves, that is, the more similarity between two curves, the higher the correlation degree. Thus, in each index layer, we assume an ideal or optimal index sequence as reference and then consider the similarity between the indices of the object to be evaluated and that of the reference sequence. The more similarity between the two index sequences, the better the comprehensive performance of the object investigated. Therefore, when evaluating a system using the grey relational method, an ideal object with optimal indices should first be introduced as the reference sequence (parent sequence), then the grey relational coefficients between the reference (or optimal) sequence and each object (sub sequence) are calculated to establish the grey relational coefficient matrix. 3.2.1. Normalising the evaluation indices Provided that there are m kinds of evaluation indices called fi(1rirm) and n projects (for example, n power plants) to be evaluated called aj(1rjrn), then they can form matrix X¼(xij)m n, called ‘‘the decision matrix’’, composed of original data. The evaluation indices in the decision matrix cannot be compared directly because they have different dimensions and magnitudes. Therefore, normalisation is necessary to transfer an original sequence to a comparable sequence. In this paper, we use the linear scale transformation method to normalise the evaluation indices. In X¼(xij)m n, when the characteristic of the index is ‘‘higher is better’’, the original sequence can be normalised as follows: yij ¼
xij ximax
ð1r i rm,1 rj rnÞ
ð2Þ
When the characteristic of the index is ‘‘lower is better’’, the original sequence can be normalised as follows: yij ¼
ximin xij
ð1r i rm,1 rj rnÞ
ð3Þ
In Eqs. (1) and (2), ximax and ximin refer to the maximum and minimum values of the ith row of the decision matrix, respectively. After transformation, the obtained matrix Y¼(yij)m n is called the linear scale standardized matrix, in which the direction of each indicator reaches unanimity, that is, all of the indices can be ‘‘higher is better’’, with consideration for the diversity of indices. 3.2.2. Calculating the grey relational coefficients According to grey system theory, the optimal value of each row in matrix Y ¼(yij)m n can form a reference sequence with m elements; here, the ith (1rirm) elements of the reference sequence can be denoted as yi,opt ¼ maxfyij gð1 rj rnÞ, where yi,opt
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Table 4 Evaluation indices for power plant assessment. First hierarchy index
Second hierarchy index
Resource consumption index (B1)
Coal consumption index (C11) Water consumption index (C12)
Environmental index (B2)
Exhausts (C21)
Third hierarchy index
SO2 emissions (C211) NOx emissions (C212) PM emissions (C213) y. COD emissions (C221) SS emissions (C222) Oil emissions (C223) Fluoride emissions (C224) y. Ash emissions (C231) Slag emissions (C232) y.
Water pollutant (C22)
Solid contaminants (C23)
Greenhouse gas index (B3)
CO2 emissions (C31) N2O emissions (C32)
is the maximal value of ith index in all n projects. Calculating the difference between the elements of the reference sequence and the elements of each column of matrix Y ¼(yij)m n through the following formula, we can obtain matrix Z ¼(zij)m n zij ¼ 9yi,opt yij 9
ð1 rj r nÞ
ð4Þ
Then, based on the GRA method, we can obtain the grey relational coefficient xij from zij through Eq. (5) (Lu et al., 2008), and these grey relational coefficients (xij) can form the grey relational coefficient matrix X ¼(xij)m n
Table 5 Analytical hierarchy process scale. Importance intensity
Definition
Meaning (X compared with Y)
1 3
Equal importance Moderate importance
5
Strong importance
7
Very strong importance Extreme importance
X is as equally important as Y X is moderately more important than Y X is strongly more important than Y X is very strongly more important than Y X is extremely more important than Y
9
min
xij ¼
min ðzij Þmn þ r max
1 r i r m1 r j r m
max ðzij Þmn
1 r i r m1 r j r m
zij þ r max
max ðzij Þmn
ð5Þ
1rirm 1rjrm
where xij is the grey relational coefficient between the ith index of the jth project to be evaluated and the ith element of the reference (or optimal) sequence; zij is the absolute difference between yi,opt and yij; min1 r i r m min1 r j r m ðzij Þmn denotes the minimum absolute difference between yi,opt and yij; max1 r i r m max1 r j r m ðzij Þmn represents the maximum absolute difference between yi,opt and yij; and r is the distinguishing coefficient, rA[0,1] and usually r ¼0.5. Grey relational coefficient xij reflects the correlation degree between two compared sequences. For example, when zij ¼ min1 r i r m min1 r j r m ðzij Þmn , then xij ¼1; and when zij ¼ max1 r i r m max1 r j r m ðzij Þmn , then xij ¼0. For other cases, when min1 r i r m min1 r j r m ðzij Þmn ozij omax1 r i r m max1 r j r m ðzij Þmn , then 0o xij o1. Therefore, the range of the relational coefficient is 0r xij r1.
3.3. Weighting through the AHP method After calculating the relational coefficient, we need to decide the weight of each coefficient/index. AHP is an effective method, commonly used for weighting calculation. In this paper, it is used to weigh indices. AHP was developed by Saaty at the University of Pittsburgh in 1971, (Saaty, 1990). It is a systematic analysis method for quantitatively treating complex and multi-criteria systems, and can decompose a complex problem into multi-layers and multi-factors, as well as expediently compare and calculate weights. Since its proposal, it has become quite popular in scientific research, (Vaidya and Kumar, 2006; Chatzimouratidis and Pilavachi, 2007; Lee and Hwang, 2010).
3.3.1. AHP methodology The basic thought of AHP is to decompose the research object into various factors by several levels according to the characteristics of the object and the research target. An ordered hierarchical decision system is then composed by their dominance relationship. Within the same level, the weights among different indices are determined through pairwise comparison. Different objects are sorted and the decision can be well made by comparing the magnitude of the weights and characteristics of various objects. Generally, the AHP method reflects people’s basic characteristics of thinking: decomposition, judgment, and synthesising. Essentially, AHP is a subjective weighting method. The element value of the judgment matrix reflects the relative importance between two comparative factors. Generally, the measurement scale of 1–9 is used to represent such relative importance, as shown in Table 5 (Saaty, 1980). Table 5 shows the importance intensity of X compared with Y. For the case of Y compared with X, importance intensity can take the reciprocal of that of X compared with Y. This method can be used to convert qualitative data acquired from expert opinions or relevant professional knowledge into quantitative data. However, when the objective data used for deducing the importance among various factors are available, the elements of judgment matrix can also be determined by these objective data.
3.3.2. Elaboration on the analytic process of AHP The analytic process of AHP should comply with the following steps: 1) The pairwise comparison matrix (also called the judgment matrix) is constructed according to the above-mentioned
G. Xu et al. / Energy Policy 39 (2011) 2343–2351
method and Table 4 0 a11 a12 B B a21 a22 A ¼ ðaij Þmm ¼ B B ^ ^ @ am1 am2
3.4. Calculating the grey relational evaluation result vector ^
1 a1m C a2m C C ^ C A amm
ð6Þ
k¼1
ði,j ¼ 1,2, . . ., mÞ
akj
ð7Þ
Then, normalisation matrix A can be obtained A ¼ ðaij Þmm
ð8Þ
3) Adding the elements of the same line/row of normalisation matrix A, we can obtain
oi ¼
m X
aij ,
The final result vector of the comprehensive evaluation is based on AHP and GRA methodology. R ¼ ðr1 ,r2 , . . ., rn Þ can be deduced by the following equations: R ¼ ðr1 ,r2 , . . ., rn Þ ¼ W X
where aij is the importance intensity of the ith factor compared with the jth factor. 2) The elements of matrix A are normalised according to the following formula: aij aij ¼ Pm
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ði ¼ 1,2, . . ., mÞ
ð9Þ
ð14Þ
or ri ¼
m X
xij oj
ð15Þ
j¼1
where R is the comprehensive evaluation result vector of n objects; X ¼(xij)m n denotes the grey relational coefficients matrix of indices, which can be calculated by Eq. (3); and W ¼ ðo1 , o2 , . . ., om Þ is the weight vector, which can be deduced by Eq. (8). Finally, the evaluation among various projects (that is, different power plants) can be carried out by sorting the grey relational degree ri of these projects. Obviously, the larger the ri, the better the project i will be, indicating that project i is closer to the reference index sequence.
j¼1
4) The weight vector W ¼ ðo1 , o2 , . . ., om Þ is then obtained through the following formula:
o oi ¼ P m i
k¼1
ok
,
ði ¼ 1,2, . . ., mÞ
ð10Þ
P Clearly, m i ¼ 1 oi ¼ 1 5) The maximum value lmax is calculated as follows:
lmax ¼
m 1X ðAWÞi m i ¼ 1 oi
ð11Þ
6) Finally, a consistency check is carried out by calculating the consistency ratio (CR) CR ¼ CI=RI
ð12Þ
where RI is the random index. The values of RI, which change with variations in the dimensions, are shown in Table 6. CI is the consistency index, and can be calculated by CI ¼
lmax m
ð13Þ
m1
In Eq. (11), m is the dimension of the comparison matrix. Generally, if CR o0.1, the calculation results are passed through the consistency check, whereas if CR Z0.1, it is advisable to adjust the judgment matrix and recalculate until the results can pass through the consistency check. Table 6 RI values. Dimension
1
2
3
4
5
6
7
8
9
RI
0.00
0.00
0.58
0.90
1.12
1.24
1.32
1.41
1.45
4. Multi-criteria evaluation of the performance of 600 MW power plants In this sector, the comprehensive evaluation of four typical 600 MW pulverised coal-fired power plants in China is conducted, based on the above-mentioned combined GRA and AHP evaluation method. Among the plants, Plant C adopts dry cooling condensation, whereas the rest are wet cooling power plants (Table 7). In addition, all of the plants have dedusting equipment. Each plant has a desulfurization as well as wastewater treatment equipment except for Plant A, and only Plant D is equipped with denitration equipment. Table 8 shows the original performance data of the four plants. From one side of the coal consumption, Plant A exhibits the best performance at only 311.83 g/kWh, even though it is not equipped with desulfurization, denitration, and wastewater treatment equipment, leading to its high pollutant emission. In comparison, Plant D takes environmental protection into serious consideration and has installed four major environmental protection equipment. Thus, although Plant D has high coal consumption (320.42 g/kWh), it exhibits low pollutant emission. In addition, because of the use of dry cooling, Plant C exhibits the highest coal consumption of up to 337.05 g/kWh, which is approximately 30 g/kWh higher than that of the other plants; its water consumption rate, however, is only 0.884 m3/MWh. Each plant obviously performs differently in respect of resource consumption, as well as pollutant and GHG emissions; thus, to make a reasonable evaluation of the merits and demerits of various power plants, a comprehensive and multiple evaluation index instead of a single one is essential. Based on the evaluation indices detailed in Table 8, we make a comprehensive assessment of the four power plants. The hierarchical structure of the comprehensive evaluation is shown in
Table 7 Equipment information of the four power plants. Equipment
Plant A
Plant B
Plant C
Plant D
Desulfurization Denitration Dedusting Wastewater treatment Condensation mode
– – O – Wet cooling
O – O O Wet cooling
O – O O Dry cooling
O O O O Wet cooling
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Table 8 Features of the four power plants. Index
Units
Plant A
Plant B
311.83 2.981
315.45 3.371
337.05 0.884
320.42 3.158
8.731
0.418
0.452
0.521
2.483 0.207 685.71 347.76 222.04 105.34
2.276 0.178 30.01 1.50 2.85 2.27
2.673 0.221 31.56 1.87 2.62 3.34
Resource consumption
Coal consumption Water consumption
g/kWh m3/MWh
Pollutant emissions
Exhausts SO2 NOx PM10 Water pollutant COD SS Oil Fluoride Solid contaminates Ash Slag
g/kWh
t/MWh t/MWh
0.381 0.044
CO2 emissions N2O emissions
g/kWh g/MWh
953.753 5.234
Greenhouse gas
g/kWh g/kWh mg/MWh mg/MWh mg/MWh mg/MWh
Destination layer
Principal layer
Index layer
Solution layer
Resources
0.3537 0.0381
946.75 5.48
Pollutant index
B2
C12
C21
Water consumption
SO2
NOx
PM1
C211
C212
C213
CO C221
Power plant B
B3
C22
Exhausts
Coal consumption
Power plant A
1.2019 0.181 32.43 1.34 2.54 1.27 0.3537 0.0358
1016.48 5.809
948.249 5.602
Performance
consumption index
C11
0.3104 0.0408
Plant D
Power Plants Comprehensive
A
B1
Plant C
SS
C23 Solid contaminant
C222
C223
Power plant C
C224
C32 CO2
Water
Fluoride
gas index C31
pollutant
Oil
Greenhouse
Ash C231
Power plant D
N2O
Slag C232
……
Fig. 1. Hierarchy tree of comprehensive evaluation.
Fig. 1. In the comprehensive evaluation, the combined method of AHP with GRA is adopted and conducted from the bottom to the top layer. After accomplishing the evaluation of one layer, we use the obtained results in the evaluation of subsequent layers until the final result is eventually obtained. Since there are so many indices considered in the comprehensive evaluation, some kinds of influences may exist among these indices, such as the influence of coal consumption index (C11) on CO2 emission index (C31). On one hand, such influences are the reflection of the objective relationship among indices. On the other hand, such influence may lead to the double counting of the weight coefficient of some indices. Therefore, the setting of the weight among indices should be careful and reasonable, so as to control such ‘‘double counting’’ to an acceptable degree.
4.1. Multi-criteria evaluation of resource consumption According to the grey relational evaluation method, the reference (or optimal) sequence should first be defined. The grey relational degree of resource consumption can be calculated based on the data provided in Table 8 and Eqs. (1)–(3), as shown in Table 9. To obtain the subjective weights of coal and water consumption assessed by experts within the scale of 1–9, AHP is adopted in weight determination. According to this method, coal
Table 9 Grey relational degree of power plant resource consumption. Index
Plant A
Plant B
Plant C
Plant D
C11(0.75) C12(0.25) B1
1.0000 0.3440 0.8360
0.9698 0.3333 0.8107
0.8316 1.0000 0.8737
0.9322 0.3387 0.7839
consumption, which has a score of 3, is moderately important compared with water consumption. During weight calculation, Eqs. (4)–(11) are used, and the weight of coal and water consumption is 0.75 and 0.25, respectively. Finally, we obtain the grey relational degree of resource consumption using Eq. (13). The results are also shown in Table 9. Table 9 shows that Plant C exhibits the best performance in terms of resource consumption. Plant C is a dry cooling plant, whose coal consumption rate is approximately 10% higher than those of other plants; its water consumption rate, however, is about 70% lower than those of other wet cooling plants. Although water consumption is considered slightly less important than coal consumption, Plant C still has the best comprehensive performance in terms of resource consumption, indicating that the decrease in its water consumption is more remarkable than the increase in its coal consumption.
G. Xu et al. / Energy Policy 39 (2011) 2343–2351
The other three plants have similar water consumption rates and all use wet cooling condensation. Their resource consumption characteristics are mainly determined by the coal consumption rate. Hence, for the grey relational degree of resource consumption, Plant A ranks second, followed by Plants B and D. The ranking corresponds to the order of coal consumption of the plants from low to high.
4.2. Multi-criteria evaluation of pollutant emissions The method for calculating grey relational coefficients of pollution indices is almost the same as that for the resource indices because the indices of pollutant emissions are also ‘‘smaller is better’’. Therefore, the minimum values of various indices make up the optimal reference sequence. The grey relational coefficients of power plant pollutant emissions are calculated according to Eq. (3). The results are shown in Table 10. As shown in Tables 1–3, for various power plant emissions, there are clear rules and penalties. Thus, in determining the weights of pollutant emissions by AHP, we can directly introduce the charging fee or pollution equivalent value into the pairwise comparison matrix to obtain the relatively objective weights (as shown in Table 11). Such a weighting process, that is, calculating weights by reliable and objective data directly, is a useful attempt to reduce the impact of the subjectiveness brought by AHP method.
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According to this method, we can easily obtain the weights of various exhausts, water pollutants, and solid contaminants. However, obtaining accurate objective data is difficult when determining the weights among the three kinds of pollutants. Thus, a received weight has to be based on the subjective method, that is, the weight scale of l–9 depending on the subjectivity of the decision-makers. The approach combining subjectivity and objectivity is, therefore, substantially adopted to determine weights of pollutant emissions. The final results of weighting are presented in Table 12. Finally, we can obtain the grey relational degree of pollutant emissions according to Eq. (13). The results are shown in Table 13. Table 13 shows that Plant D has the best environmental performance, and a grey relational degree of pollutant emission of as high as 0.9847. By contrast, Plant A exhibits the lowest value in this aspect at only 0.3902 because it has no desulfurization, denitration, and wastewater treatment equipment. The scores of Plants B and C are 0.7596 and 0.6248, respectively, which are less than that of Plant D, but much higher than that of Plant A. The almost similar results from the two plants is attributed to the fact that in both plants similar environmental protection apparatus have been installed, including dedusting, desulfurization, and wastewater treatment equipment, representing the typical configuration of a Chinese conventional coal-fired power plant. 4.3. Multi-criteria evaluation of GHG emissions
Table 10 Grey relational coefficients of power plant pollutant emissions. Index
Plant A
Plant B
Plant C
Plant D
C21 C211 C212 C213
0.3333 0.4799 0.7726
1.0000 0.5022 1.0000
0.8636 0.4638 0.7099
0.7066 1.0000 0.9664
C22 C221 C222 C223 C224
0.3425 0.3333 0.3350 0.3352
1.0000 0.8236 0.8208 0.5307
0.9083 0.6370 0.9419 0.4456
0.8692 1.0000 1.0000 1.0000
C23 C231 C232
0.3333 0.3346
0.4319 1.0000
0.6069 0.6040
1.0000 0.4322
Table 11 Pairwise comparison matrix of exhausts.
SO2 NOx PM10
SO2
NOx
PM10
1 630/630 220/630
630/630 1 220/630
630/220 630/220 1
Table 12 Weight values of pollutant emissions. Index (weight) C21: exhausts (0.6694)
C211: SO2 (0.4257) C212: NOx (0.4257) C213: PM10 (0.1486)
C22: water pollutant (0.2426)
C221: C222: C223: C224:
C23: solid contaminates (0.0880)
COD (0.07547) SS (0.01887) oil (0.75472) fluoride (0.15094)
C231: ash (0.5454) C232: slag (0.4546)
The severe threat of global warming has made it necessary to introduce the index of GHG emission to the evaluation system. GHGs emitted by power plants include CO2 and NO2, and the amount of the former is far more than that of the latter. Thus, the weight coefficient relevant to NO2 of CO2 is set to 9 when evaluating the characteristics of the GHG emission of plants. This indicates that CO2 is extremely important in GHG emission evaluation. The grey relational degree of GHG emissions can be calculated based on the data in Table 7 and Eqs. (1)–(13). The results are shown in Table 14. Table 14 shows that Plant B has the best performance in terms of GHG emissions. The GHG emissions indices of Plants D and A are slightly lower than those of Plant B. As a whole, Plant C exhibits the worst GHG emission index at 0.4105—a considerable disparity from those of the other three plants. This performance is attributed mainly to Plant C being a dry cooling plant and its higher coal consumption compared with the rest of the plants. Thus, its CO2 and NO2 emissions per kWh are remarkably higher than those of the other three plants.
Table 13 Grey relational degree of pollutant emission. Index
Plant A
Plant B
Plant C
Plant D
C21 C22 C23 B2
0.4610 0.3356 0.3340 0.3902
0.7881 0.7906 0.7418 0.7596
0.6705 0.8587 0.6053 0.6248
0.8701 0.9901 0.6903 0.9847
Table 14 Grey relational degree of GHG emissions. Index
Plant A
Plant B
Plant C
Plant D
C31(0.9) C32(0.1) B3
0.8708 1.0000 0.8837
1.0000 0.5244 0.9524
0.4191 0.3333 0.4105
0.9690 0.4297 0.9151
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Table 15 Grey relational degree of the comprehensive performance of the four power plants. Index
(Weights)
Plant A
Plant B
Plant C
Plant D
Resource consumption index (B1) Environmental index (B2) Greenhouse gas index (B3)
(0.6370) (0.2583) (0.1047)
0.8360 0.3902 0.8837
0.8107 0.7596 0.9524
0.8737 0.6248 0.4105
0.7839 0.9847 0.9151
Grey relational degree (A)
–
0.7279
0.7658
0.7901
0.8261
4.4. Multi-criteria evaluation of comprehensive performance The comprehensive evaluation results of the four power plants, denoted as the grey relational degree of comprehensive performance, can be deduced from the various indicators at the principal level (i.e., B1, B2, and B3) by employing the novel comprehensive evaluation method discussed in Section 3. The grey relational degree represents the similarity of the comprehensive performance between the plant to be evaluated and an ideal reference power plant. The ideal reference power plant is a virtual plant, whose performance indices are composed of the optimal reference index values. The higher the grey relational evaluation value of a plant, the better its comprehensive performance. Table 15 provides a summary of the indices of various items and the final calculation results of the grey relational degree. The weights of the indices are obtained according to the AHP method introduced in Section 3.3, based on the assumption that resource consumption index (B1) is moderately more important than environmental index (B2), but strongly more important than greenhouse gas index (B3), and environmental index (B2) is moderately more important than greenhouse gas index (B3). It means that the importance intensity of B1–B2, B1–B3, and B2–B3 is 3, 5, and 3, respectively. Such assumption can pass through the consistency check and also reflect the importance of these performance factors in China. As shown in Table 15, Plant D has the most outstanding comprehensive performance because of its remarkable environmental index (B2) that stems from having the most comprehensive environmental protection measures. Moreover, its resource consumption index (B1) and GHG index (B3) are as high as 0.7839 and 0.9151, respectively; these are not the highest values but still at acceptable levels. These results indicate that Plant D has not only the optimal environmental performance, but also other superior properties that provide guarantees for the plant to achieve an excellent performance. Plant C ranks closely after Plant D in comprehensive performance mainly because of its outstanding resources consumption index (B1) stemming from its low water consumption. Compared with other wet cooling plants, Plant C, as a dry cooling plant, exhibits somewhat higher coal consumption, but its water consumption is only about 30% of that of the other three plants (Table 8). The environmental index (B2) of Plant C is acceptable on account of its good environmental protection measures. Plant B shows the highest GHG index (B3), as well as a good resources consumption index (B1) and the environmental index (B2), because of its relatively low coal consumption and relatively good environmental protection measures. The final grey relational degree of Plant B reaches up to 0.7658. By comparison, Plant A deserves quite a high resources consumption index (B1) because it has the lowest coal consumption; however, its large amount of pollutant emissions render the Plant an environmental index (B2) of only 0.3902. The low score of Plant A can be attributed to its lack of environmental protection equipment. As a result, the overall performance of Plant A declines to such a
considerable extent that its final grey relational degree is only 0.7279, reflecting the worst comprehensive performance and the farthest deviation from the benchmark power plant. Compared with the traditional single-index evaluation, the comprehensive evaluation based on the novel AHP and GRA method can reveal the various performance levels of power plants, covering areas such as resource consumption, pollutants, and GHG emissions. Moreover, the comprehensive evaluation can assess the influence of environmental protection measures on the comprehensive performance of the systems. All these advantages indicate that the comprehensive evaluation method will encourage power plants to strive for environmental improvement and pollutant control.
5. Conclusions Scientific and reasonable evaluation is the precondition and foundation of policy making. This paper proposes a comprehensive evaluation method for large-scale coal-fired power plants, which is beneficial to the comprehensive consideration of resources, economy and environment factors in optimising the design and operation of a power plant, as well as in determining industrial policies. In this method, the weights of evaluation indices are determined by the AHP method and the performance levels of the plants depend on the criteria of grey relational degree. To verify the effectiveness of the proposed method, the comprehensive analysis of typical 600 MW units in China is conducted. In summary, the following conclusions are drawn: a) The new comprehensive evaluation method can take full account of the grey system characteristics of complex systems and consider a variety of subjective and objective factors in a more scientific and complete manner. For example, during the process of determining the weight of indices, the objective information, such as development status, price, relevant parameters, etc., are fully considered within the framework of AHP method. b) The case study reveals that the new method can facilitate a better evaluation of the multi-objective performance of coalfired plants, covering resource consumption, environmental protection, and greenhouse effect. The proposed method can also encourage plants to retrofit their environmental and energy-saving equipment. c) The method emphasises objective data and unites with the subjective flexibility of AHP, which enables the subjective and objective characteristics to be synthesised to a certain extent. We believe that the novel comprehensive evaluation method can play an active role in guiding power plants in adjusting their operations or carrying out technical reformation to obtain satisfactory results, as well as in making sustainable development policies of China’s power industry with fully consideration of environment protection and resources conservation.
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Acknowledgments The paper is supported by National Nature Science Foundation Project (No. 51025624), the National Major Fundamental Research Program of China (No. 2009CB219801) and the Program for Changjiang Scholars and Innovative Research Team in University (PCSIRT0720). References China Electricity Council, 2009. The Current Status of Air Pollution Control for Coal-Fired Power Plants in China 2009. China Electric Power Press, Beijing (in Chinese). Chatzimouratidis, A.I., Pilavachi, P.A., 2009. Technological, economic and sustainability evaluation of power plants using the analytic hierarchy process. Energy Policy 37, 778–787. Chatzimouratidis, A.I., Pilavachi, P.A., 2007. Objective and subjective evaluation of power plants and their non-radioactive emissions using the analytic hierarchy process. Energy Policy 35, 4027–4038. Greening, L.A., Bernow, S., 2004. Design of coordinated energy and environmental policies: use of multi-criteria decision-making. Energy Policy 32, 721–735. Jin, H.G., et al., 2008. Fundamental study of CO2 control technologies and policies in China. Science in China Series E: Technological Sciences 51, 1–14. Kim, S.H., 2007. Evaluation of negative environmental impacts of electricity generation: neoclassical and institutional approaches. Energy Policy 35, 413–423. Liu, S.F., et al., 2000. Gray System Theory and its Application. China Science Press, Beijing (in Chinese).
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Lin, S.J., et al., 2007. Grey relation performance correlations among economics, energy use and carbon dioxide emission in Taiwan. Energy Policy 35, 1948–1955. Lu, I.J., et al., 2008. Grey relation analysis of motor vehicular energy consumption in Taiwan. Energy Policy 36, 2556–2561. Lee, D.J., Hwang, J., 2010. Decision support for selecting exportable nuclear technology using the analytic hierarchy process: a Korean case. Energy Policy 38, 161–167. Montanari, R., 2004. Environmental efficiency analysis for ENEL thermal-power plants. Journal of Cleaner Production 12, 403–414. National Development and Reform Commission (NDRC) of People’s Republic of China, et al., 2003. The management regulations of charging standard for disposing pollutants (in Chinese). Saaty, T.L., 1990. How to make a decision: the analytic hierarchy process. European Journal of Operational Research 48, 9–26. Saaty, T.L., 1980. The Analytic Hierarchy Process. McGraw-Hill, New York. Vaidya, O.S., Kumar, S., 2006. Analytic hierarchy process: an overview of applications. European Journal of Operational Research 169, 1–29. Wang, J.J., et al., 2008. Integrated evaluation of distributed triple-generation systems using improved grey relational approach. Energy 33, 1427–1437. Working Group III of IPCC, 2005. IPCC Special Report on Carbon Dioxide Capture and Storage. Cambridge University Press, Cambridge, United Kingdom. Xu, G., et al., 2010a. A novel coal-based hydrogen production system with low CO2 emissions. ASME Journal of Engineering for Gas Turbines and Power 132, 031701 (1–9). Xu, G., et al., 2010b. A comprehensive techno-economic analysis method for power generation systems with CO2 capture. International Journal of Energy Research 34, 321–332. Yuan, C.Q., et al., 2010. The relation between Chinese economic development and energy consumption in the different periods. Energy Policy 38, 5189–5198.
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Energy Policy journal homepage: www.elsevier.com/locate/enpol
Comprehensive evaluation of coal-fired power plants based on grey relational analysis and analytic hierarchy process Gang Xu a,n, Yong-ping Yang a, Shi-yuan Lu a, Le Li a, Xiaona Song b a
Key Lab of Condition Monitoring and Control for Power Plant Equipment of Ministry of Education, School of Energy Power and Mechanical Engineering, North China Electric Power University, Beijing 102206, China b Electromechanical Practice Center, Beijing Information Science and Technology University, Beijing, China
a r t i c l e i n f o
abstract
Article history: Received 25 August 2010 Accepted 26 January 2011 Available online 26 February 2011
In China, coal-fired power plants are the main supplier of electricity, as well as the largest consumer of coal and water resources and the biggest emitter of SOx, NOx, and greenhouse gases (GHGs). Therefore, it is important to establish a scientific, reasonable, and feasible comprehensive evaluation system for coal-fired power plants to guide them in achieving multi-optimisation of their thermal, environmental, and economic performance. This paper proposes a novel comprehensive evaluation method, which is based on a combination of the grey relational analysis (GRA) and the analytic hierarchy process (AHP), to assess the multi-objective performance of power plants. Unlike the traditional evaluation method that uses coal consumption as a basic indicator, the proposed evaluation method also takes water consumption and pollutant emissions as indicators. On the basis of the proposed evaluation method, a case study on typical 600 MW coal-fired power plants is carried out to determine the relevancy rules among factors including the coal consumption, water consumption, pollutant, and GHG emissions of power plants. This research offers new ideas and methods for the comprehensive performance evaluation of complex energy utilisation systems, and is beneficial to the synthesised consideration of resources, economy, and environment factors in system optimising and policy making. & 2011 Elsevier Ltd. All rights reserved.
Keywords: Multi-objective evaluation Improved grey relational analysis Coal-fired power plant
1. Introduction In China, coal-fired power plants are the main supplier of electricity, as well as the largest consumer of coal and water resources. Besides, these power plants discharge a large amount of pollutants and greenhouse gases (GHGs). For instance, in 2008, the electricity generated by coal-fired power plants accounted for about 81.2% of the total production of the country; at the same time, approximately 60% coal and 20% industrial water out of the total consumption in the country were expended by these plants. Moreover, the corresponding SO2, NOx, and CO2 emissions by these coal-fired power plants reached 45.8%, 50%, and 48%, respectively, of the total amount discharged in the country (China Electricity Council, 2009). To respond to this challenge, China has introduced various types of pollutant treatment equipment to a large number of power plants to alleviate the negative impacts on the environment (China Electricity Council, 2009). For example, various environmental protection equipments, such as dedusting, desulfurization, and denitration equipments are getting more and more applications in China’s power plants. Numerous dry cooling coal-fired power
n
Corresponding author. E-mail addresses: [email protected] (G. Xu), [email protected] (Y.-p. Yang). 0301-4215/$ - see front matter & 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.enpol.2011.01.054
generation units that can decrease water consumption rate by approximated 70% have been constructed in water-deficient regions. Moreover, CO2 capture technologies have drawn increased attention; consequently, several small-scale CO2 capture demonstration equipments have already been put into operation in power plants. Pollutant treatment and CO2 capture, however, may consume extra energy and water, inevitably increasing coal and water consumption rate to a certain extent. Compared with wet cooling power generation units, the coal consumption rate of a dry cooling unit will increase by 5–15%. Thus so far, the most commonly used evaluation criterion for coal-fired power plants in China is coal consumption rate, which denotes the standard coal consumption amount per kWh electricity. Although it can satisfactorily indicate the primary techno-economic performance of power plants, it can hardly reflect performance levels for other areas of power plant operations, including environmental pollution and water consumption (Greening and Bernow, 2004). Thus, as a single criterion, coal consumption rate cannot comprehensively reflect plant’s performance in its entirety. Even worse, in adopting a single criterion, power plants with various environmental protection and water conservation equipment installed cannot obtain a fair evaluation; this hinders the improvement of environmental protection measures and pollutant control in power plants. Therefore, establishing a scientific, reasonable, and feasible comprehensive evaluation system for coal-fired power plants is imperative.
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In view of these, many scholars have studied on the evaluation methods of power plants. Chatzimouratidis and Pilavachi (2009) used the analytic hierarchy process (AHP) to evaluate the technological, economic, and sustainability performance of a power plant, and presented the advantage of using AHP in the analysis of a complex problem, such as the multi-objective performance of various power plants. However, AHP is a subjective evaluation method; thus, the evaluation of power plant performance using this approach lacks objectivity and reliability. Montanari (2004) used a new evaluation method called ‘‘Technique for Order Preference by Similarity to Ideal Solution’’ (TOPSIS), a combination of an ideal solution method and AHP method, to classify the environmental performance of a power plant based on several single-objective performance indices, including SO2, NOx, CO2 emissions rates, and so on. Through this method, highly precise results can be obtained. However, the calculation for TOPSIS is highly complex and requires a large amount of operating data. Kim (2007) analysed neoclassical and institutional economics methodologies to evaluate the environmental and social effects of electricity generation, in which social cost analysis and the multicriteria decision method were adopted in the evaluation of the effects of various forms of power generation on the environment from an economic perspective. However, this method laid particular emphasis on economic theory and is quite difficult to understand. Generally, in the comprehensive evaluation of the performance of a complex system, such as a power plant, the indicators used for the evaluation should not be independent of each other, but correlated; such a condition accords with the characteristics of a ‘‘grey system’’ (Liu et al., 2000). The grey relational analysis (GRA) method is a comprehensive evaluation approach for the grey system. Since its introduction in 1982, it has been widely used in various fields of science because of its advantages in modelling, control, prediction, and decision-making (Lin et al., 2007; Yuan et al., 2010). For example, Wang et al. (2008) used the GRA method to achieve a comprehensive evaluation of the distributed triple-generation systems from the perspectives of technical, economic, environmental, and social performance. However, the objective index weight determination method adopted in the paper is complex and incomprehensible. In addition, although the GRA method is a widely used evaluation method suitable for the grey system; thus so far, no open literatures have reported the use of this method in the performance evaluation of coal-fired power plants. This paper presents a novel comprehensive evaluation method, which is based on the combination of GRA with AHP, to assess the multi-objective performance of power plants. Using the novel assessment method, several typical Chinese 600 MW power generation power plants are evaluated and compared. Besides, the relationships between the comprehensive performance of the power plants and the primary single indices are also discussed in the paper.
2. Evaluation indices of power plant performance The comprehensive performance of a power plant is affected by various factors that cannot be neglected. At present in China, three key types of indices currently used should be taken into account in power plant’s evaluation. These indices are discussed briefly in the following sections. 2.1. Resource consumption index 2.1.1. Coal consumption rate Coal consumption rate is one of the most important indices representing power plant performance. Thus, it is widely used in techno-economic comparisons among various power plants.
The rate represents the amount of standard coal consumed per kWh and can be deduced from net efficiency, according to the following equation: b ¼ 3600=ðHstd Znet Þ
ð1Þ
where b is the coal consumption rate, g/kWh (which means ‘‘gram standard coal per kWh’’); Hstd denotes the heat value of the standard coal, which is equal to 29.308 kJ/g; and Znet is the net efficiency of the power plant. As a whole, coal consumption rate is an important technoeconomic index that can reflect the rationality, operating status, maintenance quality, and management level of a power plant. 2.1.2. Water consumption rate In the past, the importance of water consumption has always been neglected in China. However, upon realizing that water resources are limited and valuable, water consumption has been considered a key index for reflecting the environmental performance of an enterprise, particularly in water-deficient regions, such as Northwest China. Therefore, a comprehensive evaluation of power plants should include the water consumption index. 2.2. Pollutant index Pollutant emissions from coal burning are a major contributor to environmental pollution in China. Therefore, incorporating pollutant emissions into the evaluation system is necessary. In general, pollutant emissions from power plants are composed mainly of exhausts (gaseous pollutants), water pollutants, and solid contaminants. 2.2.1. Exhausts In this study, exhausts refer to gaseous pollutants; generally, the main exhausts of power plants include SO2, NOx, and particulate matter (PM10). Since 2004, detailed rules for pollutant emissions have been issued by NDRC et al. (2003). The charging standards of exhausts are shown in Table 1. 2.2.2. Water pollutants Discharged wastewater from coal-fired power plants consists of sluicing water, circulating cooling water, foul drainage, chemical wastewater, oily wastewater, coal-bearing wastewater, desulfuration wastewater, and sanitary drainage. The main pollutants in the wastewater of power plants include chemical oxygen demand (COD), suspended substances (SS), oil, and fluoride. Since 2004, detailed rules for pollutant emissions have been issued by NDRC et al. (2003) and their pollution equivalent values are shown in Table 2. 2.2.3. Solid contaminants The solid contaminants from power plants include mainly ash and slag. Ash discharge accounts for about 80% of solid waste Table 1 Charging standards of exhausts. Pollutants
SO2
NOx
PM10
Charging standards (CNY/t)
630
630
220
Table 2 Pollution equivalent value of water pollution. Pollutants
COD
SS
Oil
Fluoride
Pollution equivalent value
1
0.25
10
2
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3.1. Establishing the hierarchical structure of evaluation
Table 3 Charging standards of solid contamination. Pollutants
Ash
Slag
Charging standards (CNY/t)
30
25
discharge. Ash falling into water causes river siltation, and the toxic chemicals in ash bring forth serious harm to creatures and humans alike. Slag discharge is considerably less copious compared with that of ash, but still requires proper treatment because of its heavy metal component. In general, slag can be utilised as construction material. The charging standards of solid contamination are displayed in Table 3 (NDRC et al., 2003).
2.3. GHG index Climate change continues to develop at an alarming rate so that an increasing number of countries and international organisations have been exerting joint efforts to address environmental issues. One of the primary measures for alleviating climate deterioration is reducing GHG emissions (Xu et al., 2010a; Working Group III of IPCC, 2005). Among the known GHGs, CO2, whose accumulation is caused mainly by human activities, has the highest discharge amount and contributes the most to the greenhouse effect (Working Group III of IPCC, 2005; Xu et al., 2010b). China is one of the biggest CO2-emitting countries, accounting for approximately one fifth of the world’s total CO2 emission (Jin et al., 2008). The GHG discharged from coal-fired power plants includes CO2 and N2O, but the amount of the former is far higher than that of the latter. In China, coal-fired power plants are the largest CO2-emitting resources and account for nearly half of the country’s total CO2 emission. In comparison, the amount of N2O discharged from coal-fired power plants is minimal.
3. Methodology of the comprehensive evaluation combined GRA with AHP Grey system theory was put forward in 1982. Since then, it has been widely used in various fields of science because of its advantages in evaluating complex systems with various correlated indicators. However, although the GRA method can easily reflect the preferential order of different investigated objects according to a certain performance index, distinguishing the relative significance among different types of indices is difficult. In comparison, AHP is a classic method used for evaluating relative significance among indices. Therefore, integrating the two methods enables the maximisation of the advantages of both. This combination also facilitates the multi-objective performance evaluation of a complex system. Hence, a novel comprehensive evaluation method combining GRA and AHP is proposed to achieve multi-hierarchy and multi-criteria evaluation. The proposed method uses GRA to determine the preferential order of each index and AHP to obtain the weights of the evaluation indices. The hierarchical structure of evaluation is established first and then the grey relational coefficient matrix is obtained through the GRA methodology. Subsequently, the weights of various indices are calculated through the AHP method. Eventually, the grey relational degree is obtained based on the grey relational coefficient matrix and the weights of the indices. The methodology is described in detail as follows.
According to the proposed method, the evaluation of a complex problem can be decomposed into a hierarchical structure of several layers (Saaty, 1990). After construction of the hierarchical structure, we can carry out the comprehensive evaluation of the indices of each layer by GRA and AHP methodology, conducting the evaluation from the bottom to the top layer. Once the evaluation of a layer is completed, the obtained results are then used to proceed with the evaluation of the succeeding layer until the final result is obtained. The hierarchical structure of a traditional coal-fired power plant can be decomposed into 3 index layers (Table 4). 3.2. Establishing the grey relational coefficient matrix The basic thought of grey relational theory is to ascertain the correlation degree according to the similarity among the sequence curves, that is, the more similarity between two curves, the higher the correlation degree. Thus, in each index layer, we assume an ideal or optimal index sequence as reference and then consider the similarity between the indices of the object to be evaluated and that of the reference sequence. The more similarity between the two index sequences, the better the comprehensive performance of the object investigated. Therefore, when evaluating a system using the grey relational method, an ideal object with optimal indices should first be introduced as the reference sequence (parent sequence), then the grey relational coefficients between the reference (or optimal) sequence and each object (sub sequence) are calculated to establish the grey relational coefficient matrix. 3.2.1. Normalising the evaluation indices Provided that there are m kinds of evaluation indices called fi(1rirm) and n projects (for example, n power plants) to be evaluated called aj(1rjrn), then they can form matrix X¼(xij)m n, called ‘‘the decision matrix’’, composed of original data. The evaluation indices in the decision matrix cannot be compared directly because they have different dimensions and magnitudes. Therefore, normalisation is necessary to transfer an original sequence to a comparable sequence. In this paper, we use the linear scale transformation method to normalise the evaluation indices. In X¼(xij)m n, when the characteristic of the index is ‘‘higher is better’’, the original sequence can be normalised as follows: yij ¼
xij ximax
ð1r i rm,1 rj rnÞ
ð2Þ
When the characteristic of the index is ‘‘lower is better’’, the original sequence can be normalised as follows: yij ¼
ximin xij
ð1r i rm,1 rj rnÞ
ð3Þ
In Eqs. (1) and (2), ximax and ximin refer to the maximum and minimum values of the ith row of the decision matrix, respectively. After transformation, the obtained matrix Y¼(yij)m n is called the linear scale standardized matrix, in which the direction of each indicator reaches unanimity, that is, all of the indices can be ‘‘higher is better’’, with consideration for the diversity of indices. 3.2.2. Calculating the grey relational coefficients According to grey system theory, the optimal value of each row in matrix Y ¼(yij)m n can form a reference sequence with m elements; here, the ith (1rirm) elements of the reference sequence can be denoted as yi,opt ¼ maxfyij gð1 rj rnÞ, where yi,opt
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Table 4 Evaluation indices for power plant assessment. First hierarchy index
Second hierarchy index
Resource consumption index (B1)
Coal consumption index (C11) Water consumption index (C12)
Environmental index (B2)
Exhausts (C21)
Third hierarchy index
SO2 emissions (C211) NOx emissions (C212) PM emissions (C213) y. COD emissions (C221) SS emissions (C222) Oil emissions (C223) Fluoride emissions (C224) y. Ash emissions (C231) Slag emissions (C232) y.
Water pollutant (C22)
Solid contaminants (C23)
Greenhouse gas index (B3)
CO2 emissions (C31) N2O emissions (C32)
is the maximal value of ith index in all n projects. Calculating the difference between the elements of the reference sequence and the elements of each column of matrix Y ¼(yij)m n through the following formula, we can obtain matrix Z ¼(zij)m n zij ¼ 9yi,opt yij 9
ð1 rj r nÞ
ð4Þ
Then, based on the GRA method, we can obtain the grey relational coefficient xij from zij through Eq. (5) (Lu et al., 2008), and these grey relational coefficients (xij) can form the grey relational coefficient matrix X ¼(xij)m n
Table 5 Analytical hierarchy process scale. Importance intensity
Definition
Meaning (X compared with Y)
1 3
Equal importance Moderate importance
5
Strong importance
7
Very strong importance Extreme importance
X is as equally important as Y X is moderately more important than Y X is strongly more important than Y X is very strongly more important than Y X is extremely more important than Y
9
min
xij ¼
min ðzij Þmn þ r max
1 r i r m1 r j r m
max ðzij Þmn
1 r i r m1 r j r m
zij þ r max
max ðzij Þmn
ð5Þ
1rirm 1rjrm
where xij is the grey relational coefficient between the ith index of the jth project to be evaluated and the ith element of the reference (or optimal) sequence; zij is the absolute difference between yi,opt and yij; min1 r i r m min1 r j r m ðzij Þmn denotes the minimum absolute difference between yi,opt and yij; max1 r i r m max1 r j r m ðzij Þmn represents the maximum absolute difference between yi,opt and yij; and r is the distinguishing coefficient, rA[0,1] and usually r ¼0.5. Grey relational coefficient xij reflects the correlation degree between two compared sequences. For example, when zij ¼ min1 r i r m min1 r j r m ðzij Þmn , then xij ¼1; and when zij ¼ max1 r i r m max1 r j r m ðzij Þmn , then xij ¼0. For other cases, when min1 r i r m min1 r j r m ðzij Þmn ozij omax1 r i r m max1 r j r m ðzij Þmn , then 0o xij o1. Therefore, the range of the relational coefficient is 0r xij r1.
3.3. Weighting through the AHP method After calculating the relational coefficient, we need to decide the weight of each coefficient/index. AHP is an effective method, commonly used for weighting calculation. In this paper, it is used to weigh indices. AHP was developed by Saaty at the University of Pittsburgh in 1971, (Saaty, 1990). It is a systematic analysis method for quantitatively treating complex and multi-criteria systems, and can decompose a complex problem into multi-layers and multi-factors, as well as expediently compare and calculate weights. Since its proposal, it has become quite popular in scientific research, (Vaidya and Kumar, 2006; Chatzimouratidis and Pilavachi, 2007; Lee and Hwang, 2010).
3.3.1. AHP methodology The basic thought of AHP is to decompose the research object into various factors by several levels according to the characteristics of the object and the research target. An ordered hierarchical decision system is then composed by their dominance relationship. Within the same level, the weights among different indices are determined through pairwise comparison. Different objects are sorted and the decision can be well made by comparing the magnitude of the weights and characteristics of various objects. Generally, the AHP method reflects people’s basic characteristics of thinking: decomposition, judgment, and synthesising. Essentially, AHP is a subjective weighting method. The element value of the judgment matrix reflects the relative importance between two comparative factors. Generally, the measurement scale of 1–9 is used to represent such relative importance, as shown in Table 5 (Saaty, 1980). Table 5 shows the importance intensity of X compared with Y. For the case of Y compared with X, importance intensity can take the reciprocal of that of X compared with Y. This method can be used to convert qualitative data acquired from expert opinions or relevant professional knowledge into quantitative data. However, when the objective data used for deducing the importance among various factors are available, the elements of judgment matrix can also be determined by these objective data.
3.3.2. Elaboration on the analytic process of AHP The analytic process of AHP should comply with the following steps: 1) The pairwise comparison matrix (also called the judgment matrix) is constructed according to the above-mentioned
G. Xu et al. / Energy Policy 39 (2011) 2343–2351
method and Table 4 0 a11 a12 B B a21 a22 A ¼ ðaij Þmm ¼ B B ^ ^ @ am1 am2
3.4. Calculating the grey relational evaluation result vector ^
1 a1m C a2m C C ^ C A amm
ð6Þ
k¼1
ði,j ¼ 1,2, . . ., mÞ
akj
ð7Þ
Then, normalisation matrix A can be obtained A ¼ ðaij Þmm
ð8Þ
3) Adding the elements of the same line/row of normalisation matrix A, we can obtain
oi ¼
m X
aij ,
The final result vector of the comprehensive evaluation is based on AHP and GRA methodology. R ¼ ðr1 ,r2 , . . ., rn Þ can be deduced by the following equations: R ¼ ðr1 ,r2 , . . ., rn Þ ¼ W X
where aij is the importance intensity of the ith factor compared with the jth factor. 2) The elements of matrix A are normalised according to the following formula: aij aij ¼ Pm
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ði ¼ 1,2, . . ., mÞ
ð9Þ
ð14Þ
or ri ¼
m X
xij oj
ð15Þ
j¼1
where R is the comprehensive evaluation result vector of n objects; X ¼(xij)m n denotes the grey relational coefficients matrix of indices, which can be calculated by Eq. (3); and W ¼ ðo1 , o2 , . . ., om Þ is the weight vector, which can be deduced by Eq. (8). Finally, the evaluation among various projects (that is, different power plants) can be carried out by sorting the grey relational degree ri of these projects. Obviously, the larger the ri, the better the project i will be, indicating that project i is closer to the reference index sequence.
j¼1
4) The weight vector W ¼ ðo1 , o2 , . . ., om Þ is then obtained through the following formula:
o oi ¼ P m i
k¼1
ok
,
ði ¼ 1,2, . . ., mÞ
ð10Þ
P Clearly, m i ¼ 1 oi ¼ 1 5) The maximum value lmax is calculated as follows:
lmax ¼
m 1X ðAWÞi m i ¼ 1 oi
ð11Þ
6) Finally, a consistency check is carried out by calculating the consistency ratio (CR) CR ¼ CI=RI
ð12Þ
where RI is the random index. The values of RI, which change with variations in the dimensions, are shown in Table 6. CI is the consistency index, and can be calculated by CI ¼
lmax m
ð13Þ
m1
In Eq. (11), m is the dimension of the comparison matrix. Generally, if CR o0.1, the calculation results are passed through the consistency check, whereas if CR Z0.1, it is advisable to adjust the judgment matrix and recalculate until the results can pass through the consistency check. Table 6 RI values. Dimension
1
2
3
4
5
6
7
8
9
RI
0.00
0.00
0.58
0.90
1.12
1.24
1.32
1.41
1.45
4. Multi-criteria evaluation of the performance of 600 MW power plants In this sector, the comprehensive evaluation of four typical 600 MW pulverised coal-fired power plants in China is conducted, based on the above-mentioned combined GRA and AHP evaluation method. Among the plants, Plant C adopts dry cooling condensation, whereas the rest are wet cooling power plants (Table 7). In addition, all of the plants have dedusting equipment. Each plant has a desulfurization as well as wastewater treatment equipment except for Plant A, and only Plant D is equipped with denitration equipment. Table 8 shows the original performance data of the four plants. From one side of the coal consumption, Plant A exhibits the best performance at only 311.83 g/kWh, even though it is not equipped with desulfurization, denitration, and wastewater treatment equipment, leading to its high pollutant emission. In comparison, Plant D takes environmental protection into serious consideration and has installed four major environmental protection equipment. Thus, although Plant D has high coal consumption (320.42 g/kWh), it exhibits low pollutant emission. In addition, because of the use of dry cooling, Plant C exhibits the highest coal consumption of up to 337.05 g/kWh, which is approximately 30 g/kWh higher than that of the other plants; its water consumption rate, however, is only 0.884 m3/MWh. Each plant obviously performs differently in respect of resource consumption, as well as pollutant and GHG emissions; thus, to make a reasonable evaluation of the merits and demerits of various power plants, a comprehensive and multiple evaluation index instead of a single one is essential. Based on the evaluation indices detailed in Table 8, we make a comprehensive assessment of the four power plants. The hierarchical structure of the comprehensive evaluation is shown in
Table 7 Equipment information of the four power plants. Equipment
Plant A
Plant B
Plant C
Plant D
Desulfurization Denitration Dedusting Wastewater treatment Condensation mode
– – O – Wet cooling
O – O O Wet cooling
O – O O Dry cooling
O O O O Wet cooling
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Table 8 Features of the four power plants. Index
Units
Plant A
Plant B
311.83 2.981
315.45 3.371
337.05 0.884
320.42 3.158
8.731
0.418
0.452
0.521
2.483 0.207 685.71 347.76 222.04 105.34
2.276 0.178 30.01 1.50 2.85 2.27
2.673 0.221 31.56 1.87 2.62 3.34
Resource consumption
Coal consumption Water consumption
g/kWh m3/MWh
Pollutant emissions
Exhausts SO2 NOx PM10 Water pollutant COD SS Oil Fluoride Solid contaminates Ash Slag
g/kWh
t/MWh t/MWh
0.381 0.044
CO2 emissions N2O emissions
g/kWh g/MWh
953.753 5.234
Greenhouse gas
g/kWh g/kWh mg/MWh mg/MWh mg/MWh mg/MWh
Destination layer
Principal layer
Index layer
Solution layer
Resources
0.3537 0.0381
946.75 5.48
Pollutant index
B2
C12
C21
Water consumption
SO2
NOx
PM1
C211
C212
C213
CO C221
Power plant B
B3
C22
Exhausts
Coal consumption
Power plant A
1.2019 0.181 32.43 1.34 2.54 1.27 0.3537 0.0358
1016.48 5.809
948.249 5.602
Performance
consumption index
C11
0.3104 0.0408
Plant D
Power Plants Comprehensive
A
B1
Plant C
SS
C23 Solid contaminant
C222
C223
Power plant C
C224
C32 CO2
Water
Fluoride
gas index C31
pollutant
Oil
Greenhouse
Ash C231
Power plant D
N2O
Slag C232
……
Fig. 1. Hierarchy tree of comprehensive evaluation.
Fig. 1. In the comprehensive evaluation, the combined method of AHP with GRA is adopted and conducted from the bottom to the top layer. After accomplishing the evaluation of one layer, we use the obtained results in the evaluation of subsequent layers until the final result is eventually obtained. Since there are so many indices considered in the comprehensive evaluation, some kinds of influences may exist among these indices, such as the influence of coal consumption index (C11) on CO2 emission index (C31). On one hand, such influences are the reflection of the objective relationship among indices. On the other hand, such influence may lead to the double counting of the weight coefficient of some indices. Therefore, the setting of the weight among indices should be careful and reasonable, so as to control such ‘‘double counting’’ to an acceptable degree.
4.1. Multi-criteria evaluation of resource consumption According to the grey relational evaluation method, the reference (or optimal) sequence should first be defined. The grey relational degree of resource consumption can be calculated based on the data provided in Table 8 and Eqs. (1)–(3), as shown in Table 9. To obtain the subjective weights of coal and water consumption assessed by experts within the scale of 1–9, AHP is adopted in weight determination. According to this method, coal
Table 9 Grey relational degree of power plant resource consumption. Index
Plant A
Plant B
Plant C
Plant D
C11(0.75) C12(0.25) B1
1.0000 0.3440 0.8360
0.9698 0.3333 0.8107
0.8316 1.0000 0.8737
0.9322 0.3387 0.7839
consumption, which has a score of 3, is moderately important compared with water consumption. During weight calculation, Eqs. (4)–(11) are used, and the weight of coal and water consumption is 0.75 and 0.25, respectively. Finally, we obtain the grey relational degree of resource consumption using Eq. (13). The results are also shown in Table 9. Table 9 shows that Plant C exhibits the best performance in terms of resource consumption. Plant C is a dry cooling plant, whose coal consumption rate is approximately 10% higher than those of other plants; its water consumption rate, however, is about 70% lower than those of other wet cooling plants. Although water consumption is considered slightly less important than coal consumption, Plant C still has the best comprehensive performance in terms of resource consumption, indicating that the decrease in its water consumption is more remarkable than the increase in its coal consumption.
G. Xu et al. / Energy Policy 39 (2011) 2343–2351
The other three plants have similar water consumption rates and all use wet cooling condensation. Their resource consumption characteristics are mainly determined by the coal consumption rate. Hence, for the grey relational degree of resource consumption, Plant A ranks second, followed by Plants B and D. The ranking corresponds to the order of coal consumption of the plants from low to high.
4.2. Multi-criteria evaluation of pollutant emissions The method for calculating grey relational coefficients of pollution indices is almost the same as that for the resource indices because the indices of pollutant emissions are also ‘‘smaller is better’’. Therefore, the minimum values of various indices make up the optimal reference sequence. The grey relational coefficients of power plant pollutant emissions are calculated according to Eq. (3). The results are shown in Table 10. As shown in Tables 1–3, for various power plant emissions, there are clear rules and penalties. Thus, in determining the weights of pollutant emissions by AHP, we can directly introduce the charging fee or pollution equivalent value into the pairwise comparison matrix to obtain the relatively objective weights (as shown in Table 11). Such a weighting process, that is, calculating weights by reliable and objective data directly, is a useful attempt to reduce the impact of the subjectiveness brought by AHP method.
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According to this method, we can easily obtain the weights of various exhausts, water pollutants, and solid contaminants. However, obtaining accurate objective data is difficult when determining the weights among the three kinds of pollutants. Thus, a received weight has to be based on the subjective method, that is, the weight scale of l–9 depending on the subjectivity of the decision-makers. The approach combining subjectivity and objectivity is, therefore, substantially adopted to determine weights of pollutant emissions. The final results of weighting are presented in Table 12. Finally, we can obtain the grey relational degree of pollutant emissions according to Eq. (13). The results are shown in Table 13. Table 13 shows that Plant D has the best environmental performance, and a grey relational degree of pollutant emission of as high as 0.9847. By contrast, Plant A exhibits the lowest value in this aspect at only 0.3902 because it has no desulfurization, denitration, and wastewater treatment equipment. The scores of Plants B and C are 0.7596 and 0.6248, respectively, which are less than that of Plant D, but much higher than that of Plant A. The almost similar results from the two plants is attributed to the fact that in both plants similar environmental protection apparatus have been installed, including dedusting, desulfurization, and wastewater treatment equipment, representing the typical configuration of a Chinese conventional coal-fired power plant. 4.3. Multi-criteria evaluation of GHG emissions
Table 10 Grey relational coefficients of power plant pollutant emissions. Index
Plant A
Plant B
Plant C
Plant D
C21 C211 C212 C213
0.3333 0.4799 0.7726
1.0000 0.5022 1.0000
0.8636 0.4638 0.7099
0.7066 1.0000 0.9664
C22 C221 C222 C223 C224
0.3425 0.3333 0.3350 0.3352
1.0000 0.8236 0.8208 0.5307
0.9083 0.6370 0.9419 0.4456
0.8692 1.0000 1.0000 1.0000
C23 C231 C232
0.3333 0.3346
0.4319 1.0000
0.6069 0.6040
1.0000 0.4322
Table 11 Pairwise comparison matrix of exhausts.
SO2 NOx PM10
SO2
NOx
PM10
1 630/630 220/630
630/630 1 220/630
630/220 630/220 1
Table 12 Weight values of pollutant emissions. Index (weight) C21: exhausts (0.6694)
C211: SO2 (0.4257) C212: NOx (0.4257) C213: PM10 (0.1486)
C22: water pollutant (0.2426)
C221: C222: C223: C224:
C23: solid contaminates (0.0880)
COD (0.07547) SS (0.01887) oil (0.75472) fluoride (0.15094)
C231: ash (0.5454) C232: slag (0.4546)
The severe threat of global warming has made it necessary to introduce the index of GHG emission to the evaluation system. GHGs emitted by power plants include CO2 and NO2, and the amount of the former is far more than that of the latter. Thus, the weight coefficient relevant to NO2 of CO2 is set to 9 when evaluating the characteristics of the GHG emission of plants. This indicates that CO2 is extremely important in GHG emission evaluation. The grey relational degree of GHG emissions can be calculated based on the data in Table 7 and Eqs. (1)–(13). The results are shown in Table 14. Table 14 shows that Plant B has the best performance in terms of GHG emissions. The GHG emissions indices of Plants D and A are slightly lower than those of Plant B. As a whole, Plant C exhibits the worst GHG emission index at 0.4105—a considerable disparity from those of the other three plants. This performance is attributed mainly to Plant C being a dry cooling plant and its higher coal consumption compared with the rest of the plants. Thus, its CO2 and NO2 emissions per kWh are remarkably higher than those of the other three plants.
Table 13 Grey relational degree of pollutant emission. Index
Plant A
Plant B
Plant C
Plant D
C21 C22 C23 B2
0.4610 0.3356 0.3340 0.3902
0.7881 0.7906 0.7418 0.7596
0.6705 0.8587 0.6053 0.6248
0.8701 0.9901 0.6903 0.9847
Table 14 Grey relational degree of GHG emissions. Index
Plant A
Plant B
Plant C
Plant D
C31(0.9) C32(0.1) B3
0.8708 1.0000 0.8837
1.0000 0.5244 0.9524
0.4191 0.3333 0.4105
0.9690 0.4297 0.9151
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Table 15 Grey relational degree of the comprehensive performance of the four power plants. Index
(Weights)
Plant A
Plant B
Plant C
Plant D
Resource consumption index (B1) Environmental index (B2) Greenhouse gas index (B3)
(0.6370) (0.2583) (0.1047)
0.8360 0.3902 0.8837
0.8107 0.7596 0.9524
0.8737 0.6248 0.4105
0.7839 0.9847 0.9151
Grey relational degree (A)
–
0.7279
0.7658
0.7901
0.8261
4.4. Multi-criteria evaluation of comprehensive performance The comprehensive evaluation results of the four power plants, denoted as the grey relational degree of comprehensive performance, can be deduced from the various indicators at the principal level (i.e., B1, B2, and B3) by employing the novel comprehensive evaluation method discussed in Section 3. The grey relational degree represents the similarity of the comprehensive performance between the plant to be evaluated and an ideal reference power plant. The ideal reference power plant is a virtual plant, whose performance indices are composed of the optimal reference index values. The higher the grey relational evaluation value of a plant, the better its comprehensive performance. Table 15 provides a summary of the indices of various items and the final calculation results of the grey relational degree. The weights of the indices are obtained according to the AHP method introduced in Section 3.3, based on the assumption that resource consumption index (B1) is moderately more important than environmental index (B2), but strongly more important than greenhouse gas index (B3), and environmental index (B2) is moderately more important than greenhouse gas index (B3). It means that the importance intensity of B1–B2, B1–B3, and B2–B3 is 3, 5, and 3, respectively. Such assumption can pass through the consistency check and also reflect the importance of these performance factors in China. As shown in Table 15, Plant D has the most outstanding comprehensive performance because of its remarkable environmental index (B2) that stems from having the most comprehensive environmental protection measures. Moreover, its resource consumption index (B1) and GHG index (B3) are as high as 0.7839 and 0.9151, respectively; these are not the highest values but still at acceptable levels. These results indicate that Plant D has not only the optimal environmental performance, but also other superior properties that provide guarantees for the plant to achieve an excellent performance. Plant C ranks closely after Plant D in comprehensive performance mainly because of its outstanding resources consumption index (B1) stemming from its low water consumption. Compared with other wet cooling plants, Plant C, as a dry cooling plant, exhibits somewhat higher coal consumption, but its water consumption is only about 30% of that of the other three plants (Table 8). The environmental index (B2) of Plant C is acceptable on account of its good environmental protection measures. Plant B shows the highest GHG index (B3), as well as a good resources consumption index (B1) and the environmental index (B2), because of its relatively low coal consumption and relatively good environmental protection measures. The final grey relational degree of Plant B reaches up to 0.7658. By comparison, Plant A deserves quite a high resources consumption index (B1) because it has the lowest coal consumption; however, its large amount of pollutant emissions render the Plant an environmental index (B2) of only 0.3902. The low score of Plant A can be attributed to its lack of environmental protection equipment. As a result, the overall performance of Plant A declines to such a
considerable extent that its final grey relational degree is only 0.7279, reflecting the worst comprehensive performance and the farthest deviation from the benchmark power plant. Compared with the traditional single-index evaluation, the comprehensive evaluation based on the novel AHP and GRA method can reveal the various performance levels of power plants, covering areas such as resource consumption, pollutants, and GHG emissions. Moreover, the comprehensive evaluation can assess the influence of environmental protection measures on the comprehensive performance of the systems. All these advantages indicate that the comprehensive evaluation method will encourage power plants to strive for environmental improvement and pollutant control.
5. Conclusions Scientific and reasonable evaluation is the precondition and foundation of policy making. This paper proposes a comprehensive evaluation method for large-scale coal-fired power plants, which is beneficial to the comprehensive consideration of resources, economy and environment factors in optimising the design and operation of a power plant, as well as in determining industrial policies. In this method, the weights of evaluation indices are determined by the AHP method and the performance levels of the plants depend on the criteria of grey relational degree. To verify the effectiveness of the proposed method, the comprehensive analysis of typical 600 MW units in China is conducted. In summary, the following conclusions are drawn: a) The new comprehensive evaluation method can take full account of the grey system characteristics of complex systems and consider a variety of subjective and objective factors in a more scientific and complete manner. For example, during the process of determining the weight of indices, the objective information, such as development status, price, relevant parameters, etc., are fully considered within the framework of AHP method. b) The case study reveals that the new method can facilitate a better evaluation of the multi-objective performance of coalfired plants, covering resource consumption, environmental protection, and greenhouse effect. The proposed method can also encourage plants to retrofit their environmental and energy-saving equipment. c) The method emphasises objective data and unites with the subjective flexibility of AHP, which enables the subjective and objective characteristics to be synthesised to a certain extent. We believe that the novel comprehensive evaluation method can play an active role in guiding power plants in adjusting their operations or carrying out technical reformation to obtain satisfactory results, as well as in making sustainable development policies of China’s power industry with fully consideration of environment protection and resources conservation.
G. Xu et al. / Energy Policy 39 (2011) 2343–2351
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