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Energy Procedia 61 (2014) 904 – 909

The 6th International Conference on Applied Energy – ICAE2014

Analysis of Energy Efficiency for Coal-fired Power Units based on

Data Envelopment Analysis Model
Chenxi Song, Mingjia Li, Fan Zhang, Yaling He, Wenquan Tao
Key Laboratory of Thermo-Fluid Science and Engineering, Ministry of Education, Xi’an Jiaotong University, Xian Ning West road, Xi’an, Shaanxi
710049, PR China

Abstract

In this article, the non-parametric data envelopment analysis method˄DEA˅is employed to evaluate energy efficiency (EE) of
coal-fired power units in China. The data set contains inputs and outputs of 34 coal-fired power units with the capacity of 600MW.
Input-oriented CCR ˄Charnes-Cooper-Rhodes˅is employed. The value of CCR efficiency calculated in this paper is based on two
input parameters: fuel consumption and auxiliary power consumption. The relations between EE and factors including, parameter
of main steam, cooling method, capacity utilization rate and annual utilization hours are analysed. Some relation curves are fitted.

2014 The
© 2014 The Authors.
Authors.Published
Publishedby
byElsevier
ElsevierLtd.
Ltd.This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/3.0/).
Selection and/or peer-review under responsibility of ICAE
Peer-review under responsibility of the Organizing Committee of ICAE2014

Keywords: Coal-fired power unit; data envelopment analysis (DEA); energy efficiency (EE)

1. Introduction

The rising of the global temperature due to the greenhouse gas, especially carbon dioxide (CO2) emission is
threatening the life of human being. Statistics show that energy consumption accounts for over 80% of the global
anthropogenic CO2 emission [1], and traditional fossil energy resources like natural gas, oil and coal take a large
proportion of total energy use in many countries. Power industry consumes considerable amount of fossil fuels. China
is the largest power generator which accounts for 21.9% of world total power generation [2]. Statistics by China
Electricity Council indicate that coal-fired thermal power plants takes 65.8% in total installed capacity in China [3].
Therefore, improving energy efficiency (EE) of coal-fired power units can contribute a lot for the ease of carbon
emission in China.
The DEA approach has been employed to analyse EE of power units by many researchers [4-11]. Among them,
total factor EE evaluation which labour is included as an input was conducted by many researchers [4-6, 8, 9]. To
analyse influencing factors of EE, regression method[4, 5, 8, 12] rather than the DEA method [7] is widely used. In
this paper, EE analysis is conducted especially for coal-fired power units. The input and output parameters selected are
only related to energy flow. From the respect of energy utilization, DEA models are used to analyse influencing
factors of EE. Although the DEA method can not exclude the influence of other factors when analysing one factor, it

Corresponding author. Tel.: +86-298-266-9106; fax: +86-298-266-9106.

E-mail address: wqtao@mail.xjtu.edu.cn.

1876-6102 © 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/3.0/).
Peer-review under responsibility of the Organizing Committee of ICAE2014
doi:10.1016/j.egypro.2014.11.992
 Chenxi Song et al. / Energy Procedia 61 (2014) 904 – 909 905

is effective to find predominant influencing factors. What’s more, results obtained in this paper can help to verify the
feasibility of seeking predominant influencing factors of EE based on DEA models.

1.1. Model description

In China, the most commonly used EE indicator of power units is called Standard Coal Consumption per Unit
Power Supplied. However, this indicator can only depict overall EE of power units without showing any details.
Efficiency evaluation based on data envelopment analysis (DEA) model is one of the methods which can help to solve
this problem. In this paper, the input-oriented CCR ˄Charnes-Cooper-Rhodes˅model is used to calculate EE [13, 14].
The input-oriented version is adopted because the amount of electricity generated by all power plants is arranged and
dispatched by state-owned group companies rather than determined by power plants. The software of DEAP (version
2.1) developed by Coelli is employed for solving equations of DEA models.

1.2. Selection of input and output parameters

From the economic aspect, managers of coal-fired power plants concern a lot about coal and electricity
consumption. The indicator of special EE here is defined as EE value based on DEA models with coal and electricity
as two input parameters. Input and output parameters in our research are shown in Table1.

Table 1. Selection of input and output parameters

Input / Output Parameter Unit Definition

Input 1 Coal consumption 105kgce Coal consumption by boiler during operational stage
Input 2 Auxiliary electricity consumption 106kWh Electricity consumption by auxiliaries for power units
Output 1 Electricity generated 106kWh Total electricity generated by power units
Output 2 Capacity utilization rate % Utilization of the rated capacity of power units

2. Data description

The data in our analysis are took from the public notification data of EE Competition of Fossil Power Units [15].
Thirty-four coal-fired power units with the rated capacity of 600MW are selected in this article. Different power units
are identified with serial numbers. According to Raab and Lichty [16], the minimum number of decision making units
(DMUs) should be greater than three times the number of inputs and outputs. This requirement is satisfied since here
34>3(2+2). Original data is list in the table of Appendix A. Statistical period for the data is a year.

3. EE analysis and discussion

The general production flow chart of coal-fired power units is demonstrated in Fig. 1. The dotted lines demonstrate
the statistical boundary. The right boundary lines marked with 1 and 2 indicate different statistical range. The indicator
of Standard Coal Consumption per Unit Power Supplied is defined as the ratio of net electricity supplied over coal
consumption. Right statistical boundary of this indicator is demarcated by the line marked with “1” (see Fig. 1). This
is an effective indicator to evaluate the overall EE of power units. However, it treats the production process as a
completely black box. We do not know which part affect EE of a power unit. The authors try to solve this problem by
going a little deeper into the inner production process. As shown in Fig. 1, the right boundary is moved from “1” to
“2” in our research.
Several factors may affect EE of power units. In the following, affection of four factors including parameter of
main steam, cooling method, capacity utilization rate and annual utilization hours on EE of power units are analysed.
906 Chenxi Song et al. / Energy Procedia 61 (2014) 904 – 909

Fig. 1. Schematic flow chart for electricity generation

3.1. Parameter of main steam

The pressure of main steam is an important parameter for a power unit. The 34 power units are divided into two
groups: supercritical power units and subcritical power units.
Calculation results show that the average values of EE based on CCR model are 0.959 and 0.928 for supercritical
power units and subcritical power units respectively. It indicates that increasing main steam pressure can increase EE
of power units. Detailed EE distribution of power units in these two groups is demonstrated in Fig. 2. It can be found
that some power units in the group of supercritical power units have lower EE than units in the group of subcritical
power units. For example, power units “11” and “12” are such units. The authors analyse these two power units and
found two reasons. First, condensers of these two power units are cooled by air directly. This is the type of cooling
with the lowest efficiency. Second, capacity utilization rates of these two power units are 68.8 % and 69.79%, which
are lower than other power units in the data set. Low capacity utilization rate can also lead to low EE. These two
factors will be discussed later. Therefore, it can be said that the pressure of main steam has a positive affection on
special EE based on CCR efficiency. Some power units which do not obey this rule are mainly due to the affection of
other disadvantageous factors.

3.2. Cooling method

The affection of cooling types on EE is shown in Fig. 3. There are four cooling types for condensers, namely water
cooling with closed circulation (marked as “BS”), water cooling with open circulation (marked as “KS”), indirect air
cooling (marked as “JK”) and direct air cooling (marked as “ZK”). The influence of cooling types is not obvious since
there are uneven numbers of power units in each group. However, we can still find that power units cooled by water
have larger EE compared with those cooled by air. In Fig. 3, the symbol of square represents the statistical average
value, and the horizontal lines in the four bars represent the locations of 25%, 50% and 75% percentiles.

Fig. 2. EE comparison between supercritical Fig. 3. Statistical diagram of EE distribution for power
and subcritical power units units with different cooling styles

3.3. Capacity utilization rate and annual utilization hours

The affection of capacity utilization rate and annual utilization hours on EE is demonstrated in Fig. 4 and Fig. 5,
respectively. These two factors are normalized by the maximum value and minimum value among the 34 power units.
 Chenxi Song et al. / Energy Procedia 61 (2014) 904 – 909 907

Relationships between the special EE and these two factors are obvious except for power units “29” and “30”. These
two units are special due to two reasons. First, they are supercritical power units. Second, they are located in Liaoning
province, where the temperature is relatively low in China. Low temperature in the cold side is a favourable factor for
EE improvement in the Rankine cycle on which the electricity production is based. What’s more, condensers of these
two units are cooled by water with open circulation. All these factors are favourable for EE. Therefore, these two
power units are excluded when fitting the lines between EE and these two factors in Fig. 4 and Fig. 5.
It can be found that EE and these two factors are positive related. The slope of the fitted line in Fig. 4 is larger than
that in Fig.5, which indicates that the increment in capacity utilization rate will lead to larger increment of CCR
efficiency compared with annual utilization hours.

29 30 y=0.120*x+0.863
1.00 y=0.092*x+0.875
R2=0.538 1.00
R2=0.336
0.98
0.98
CCR efficiency

0.96

CCR efficiency
0.96

0.94
0.94

0.92
0.92

0.90
0.90

0.88
0.88

0.86 0.86
0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0
Capacity utilization rate (non-dimensional) Annual utilization hours (non-dimensional)
Fig. 4. The relation between EE and capacity utilization rate Fig. 5. The relation between EE and annual utilization hours

4. Conclusion

We analyse EE of coal-fired power units based on CCR efficiency of DEA method in this paper. Compared with
the conventional indicator of Coal Consumption per Unit Power generated, this indicator can show the EE of inner
production process, which is helpful for the EE improvement. From the analysis of the relation between EE and five
factors, it can be concluded that: 1) Increasing main steam pressure can help to improve EE of power units. 2) Water
cooling is an efficient cooling style for power units. 3) The slope value of capacity utilization rate has larger influence
on EE compared with annual utilization hours.
Based on the conclusion, our advances for the design and operation of coal-fired power plants are as follows. 1)
Higher parameters of steam should be selected as long as the material of power units can bear it. 2) Water cooling
should be selected preferentially if the natural condition allows. Indirect air cooling should be selected rather than
direct air cooling in water-deficient area. 3) High capacity utilization rate and annual utilization hours are very
beneficial for EE of power units. To obtain high annul utilization hours, safety and reliability of power units should be
enhanced by power plants. To obtain high capacity utilization rate, reasonable power dispatching should be conducted
by Chinese grid companies. What’s more, since the effect of capacity utilization rate is larger than utilization hours,
power units with low EE can be shut down for a while to maintain high capacity utilization rate of power units with
high EE.

Acknowledgements

This work is supported by China National Energy Conservation Centre.

908 Chenxi Song et al. / Energy Procedia 61 (2014) 904 – 909

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First Author Biography

Chenxi Song is a PhD candidate of Prof. Wenquan Tao in the department of Thermal Engineeringˈ
Xi’an Jiaotong University, and a researcher in the Key Laboratory of Thermo-fluid Science and
Engineering, Ministry of Education. Her Research mainly focuses on energy efficiency evaluation in
industry.

Corresponding Author Biography

Wen-Quan Tao is currently a professor of Xi’an Jiaotong University and a member of Chinese
Academy of Sciences. He is an associate editor of International Journal of Heat & Mass Transfer,
International Communications in Heat & Mass Transfer, and ASME Journal of Heat Transfer. His
research mainly focuses on heat transfer enhancement theory, mechanisms and engineering
applications.

Appendix A. Original data for energy efficiency calculation in this paper

Table A.1.Original data for energy efficiency calculation

Power Electricity Capacity Coal consumption Oil consumption Fresh water Electricity
 Chenxi Song et al. / Energy Procedia 61 (2014) 904 – 909 909

units generated utilization consumption consumption

6 4 3
10 kWh % 10 t t m 106kWh

1 3382.41 71.88 100.37 13.86 70015.89 180.27

2 3722.86 73.78 112.04 11.45 77063.20 199.47
3 3745.89 77.21 111.18 0.01 8990.14 178.46
4 3872.97 74.97 114.74 0.01 9295.13 191.33
5 4069.00 77.50 121.96 48.00 83414.50 226.00
6 3899.00 74.89 117.37 65.00 81879.00 194.00
7 2906.36 78.50 89.69 183.00 6684.62 232.54
8 3636.32 79.69 113.04 171.38 8363.53 284.88
9 3252.15 76.74 96.82 80.20 66018.65 182.31
10 3819.53 77.73 111.05 19.50 77536.46 193.75
11 3087.24 68.80 98.33 434.02 8644.26 204.50
12 3174.63 69.79 101.11 280.77 8888.97 194.51
13 2906.26 71.52 92.86 91.88 11334.40 162.27
14 3074.31 70.11 98.19 86.62 11989.81 169.93
15 3438.43 71.57 108.29 601.16 6189.18 281.63
16 2496.48 68.76 80.19 1400.20 4493.67 209.09
17 3209.40 71.03 100.12 0.47 9949.14 254.29
18 3040.70 68.92 94.98 1.07 9426.17 241.71
19 3660.85 75.20 114.11 289.70 9884.28 296.69
20 3101.29 73.83 96.44 283.34 8373.49 254.88
21 3186.83 71.02 99.38 136.62 10516.55 250.29
22 3594.09 70.31 109.38 76.85 11860.49 275.78
23 3460.70 76.17 100.79 58.00 69214.00 180.70
24 3790.74 75.01 111.09 203.00 75814.80 199.67
25 3124.25 78.69 93.34 286.76 65609.25 165.68
26 3784.30 80.84 113.98 185.39 77578.15 199.94
27 3578.63 79.77 101.94 154.12 66204.66 190.20
28 3805.45 82.36 107.63 159.20 76109.00 199.74
29 2235.59 62.75 64.76 72.05 7601.01 99.41
30 2769.82 66.16 79.81 57.78 9417.39 115.87
31 3513.38 75.21 106.45 325.63 14053.51 168.24
32 3333.51 75.32 101.51 270.56 13334.02 148.46
33 3651.30 74.91 111.86 84.64 14605.20 180.64
34 3418.77 74.01 105.70 49.51 13675.08 159.03