Efficiency of Power Distribution Companies in Pakistan (Application of Non Parametric Approach)

International Journal of Disaster Recovery and Business Continuity
Vol.12, No. 1, (2021), pp. 1721–1734
1721
ISSN: 2005-4289 IJDRBC
Copyright ⓒ 2021 SERSC
Efficiency of Power Distribution Companies in Pakistan
(Application of Non Parametric Approach)
Nauman Mushtaq1
,Dr Moghira Badar2
,Dr Faiza Akhtar3
, Dr Fatima Batool4
,Dr Muhammad
Ejaz Sandhu5
,Dr Muhammad Imran Khan6
,Fahad Saddique7
,Salman Sarwar8
,Muhammad
Ahsan Zia9
1
Phd Scholar, The Institute of Management Science Lahore. nauman_mushtaq1@yahoo.com
2
(Ph.D),Salar International University Lahore. moghirab@yahoo.com
3
(Ph.D),BUITEMS Quetta Balochistan. faizaakhtar42@yahoo.com
4
(Ph.D), University of the Punjab,Lahore. fatima.batool@cemb.edu.pk
5
(Ph.D,) Director Operations, Shahid Javed Burki Institue of Public Policy at Netsol. Lahore.
www.sjbipp.org dr.sandhu@sjbipp.org
6
(Ph.D),The Institute of Management Science Lahore. dr.imran@pakaims.edu.pk
7
Phd Scholar,The Institute of Management Science Lahore. fahad.sadique@gmail.com
8
Phd Scholar, The Institute of Management Science Lahore. salmansarwar333@gmail.com
9
University Of South Asia Lahore. ahsan45@gmail.com
ABSTRACT
Electricity is very significant at global level that is used the most useful type of energy in modern
world. We will evaluate the distribution system in DISCO. This paper is focused on grounds regarding
the grid, through this research of distribution network input & output characteristic, dependent about
which is establishing a more objective estimation values and system from the economic aspect and by
using the data envelopment analysis for evaluates their relative efficiencies. Using this way we can
compare the performance of good company. Finally, by the help of this analysis for power distribution
companies, this study provides a range of scientifically evaluation method for the improvements of a
distribution system according to different state. Technical Efficiency is by CRS 97.2% by VRS 98.2%
and Scale Efficiency is 99.0%.
Keywords- [1] DEA [2] DISCO’s
1. INTRODUCTION
Electric Power usage is the very important, for the locally and commercially utilization and the very
much convenient source of energy in modern world. As a specific type of natural resource, electricity
that cannot be stored, and its generation, transmission, supply to consumers and utilization is managed
at the same stage. Along by the rapid growth of national economy and the increasing demand of the
people’s materialistic approach and new living style, social and corporate culture for electricity is
increasing. The basic need of the reliability and quality is increasing at high level, which is engaged in
promoting the quick development of energy industry, grid expansion and technology advancement
developing with continuous flow. The research on the evaluation in construction of grid has vital
practical significance and importance for development of its efficiency and improving economic and
social impacts on Pakistan.

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2. LITRATURE REVIEW
DEA model is a very effective and ideal to calculate the efficiency of multi input & multi output both
decision making units. However, DEA technique is useful in the evaluating about Financial
Institutions, Multilateral Agencies, Educational Institutes, Medical Fields, Universities, Public Limited
Companies, Banking Sector, Tourism Firms and Stock Market. In previous decades, DEA method has
been used to evaluate the efficiency of the power sector. First time this application technique of DEA
technique was used for power system and power field. Luo Daoping and Xiao Di (1996) analyzed the
all factors on production of eight Chinese grids by using the DEA model and researched the
classification and its scale [3]. Some other research scholars Wang Enchuang and Ren Yulong in
(2008) worked on empirical study on the input and output effectiveness of grid of Chongqing by
indirect and direct layer [4]. Zhou Ming and Zhao Wei in (2008) conducted study of the operating
efficiency from the perspectives about the grids enterprise combining DEA and yardstick to compare
competition [5]. Despite for the evaluation of efficiency of distribution companies is more important
from the grid system planning technique aspect, like as to considering the reliable, safe and the quality
of electric power delivery to consumer and industry etc. Even also for the local and international
literature probably is regarding less for the analyzing for the scale to economic, scale appropriate
condition and input & output integration of performance after doing the planning is accomplished and
also converted to operational state.
In all process of electricity industry reform, tackling a lot of uncertain existing factors, about how to
generating and designing suitable index about grid company and how to put forward coordinating
evaluation method or techniques and procedure have vital practically importance about the companies
to make objective, appropriate, clean, fairly and suitable evaluation and for a power distribution
company to improvement the stages of managing, promoting efficiency, investing decision and
inauguration the new project with scientific method and perfect for the benefits and for restraint the
mechanism.
3. THE EVALUATION METHOD OF (DEA)
The DEA stands for data envelopment analysis is actually beneficial decision technique while
estimating the relative performance for the homogeneous department or some unit and that can be
utilized in all segments of life. In year of 1978, the initial DEA model was introduced which is put
forward by many famous operational activities by researchers A. charnes, W.W.Cooper and E.Rhodes
is named C2R model and it was fruitful to calcuate the relative efficiency of decision making units [6]
and Lewin in 1994 [7]. In study of economic, DEA is also a very useful weapon while researching the
boundary manufacturing or productions that have multiple inputs and multiple outputs units. However,
it can be utilized to research and identify the errors and problems which also relevant with multilateral
manufacturing or producing function, like as the rates of progress in technology, the indexes of
productivity and scale, the minimum cost problem with maximum benefits.
Since the DEA method does not need to estimate parameters in advance, it has underestimated
superiority in avoiding subjective factors, simplifying operations and reducing error, etc. Compared
with other methods, the biggest advantage of DEA method is that it is pure technical, need not given
an advance known production function with the parameters, it provides excellent model for the
comparison of efficiency between different distribution network.

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4. DESIGN OF MODEL MATHEMATICALLY
Efficiency of Disco firms has been calculated by non-parametric (Programming) methods. Charnes et
al. (1978-1981) who invented the term DEA apply the same work on multi input and output models. It
is mostly used to find the efficiency in all fields of study. To find out the efficiency it works on
Decision Making Units (DMU) and selects the best one from all of these decision making units
DMUs. The finding of DEA lies between one and zero because it uses the maximum ratio of weighted
input and output if the results are one it means the unit is efficient but on the other hand if results are
zero or less than one then the unit is inefficient. Most of the researchers considered it to be the best for
the small size of observation. P Zhou and Kim Leng Poh in (2008) [8] and jarite and Maria also used
DEA in their study (2010).[9]
According to Asghar and Afza (2010)[10] “The input oriented DEA model is used to estimate
technical efficiency pure technical efficiency and scale efficiency which if given in figure (1)
Min λ0θ0
s.t. Σ λ 0j yrj ≥ y r0 (r = 1…….s) (1
θ 0 xi0 ≥ Σ
n
J=1λ0j xij (i = 1…….n) (2
Σ
n
J=1 λ 0j = 1 (3
λ0j ≥ 0 (j = 1…….n)
1) Σ λ 0j y rj ≥ y r0 (1) is the output constraint.
2) θ 0 x i0 ≥ Σ λ j x 0 is the input constraint.
yrj and xio are the output and input of the nth DMU whereas; λ is the weight. 0 is the DMU which is
to be measured and by solving the non-parametric model, we can get the minimum θ0 which is the
vector of the efficiency score. The index j specifies DMUs for j=1,…,N. yrj is the rth output of the jth
firm for r=1,..,R. xij indicates the ith input of the jth DMU for I = 1,…,I (Mahlberg, 2000).[11] The
third constraint introduces variable return to scale (VRS) into the model and if third constraint is
dropped, the frontier technology converts from VRS to CRS. Moreover, if (Σλ0j ≤ 1) is applied instead
of third constraint, the new model can even determine the reason of scale inefficiency that could be
increasing return to scale (IRS) or decreasing return to scale (DRS)”.
5. INDEX SYSTEM FOR EVALUATION DESIGN & OBJECTIVE OF STUDY
DEA model is perfect and ideal to evaluate the efficiency of multi input & output both decision
making units know which unit is performing better and find potential area to use for implementation of
new reforms.
DETERMINE THE INPUT & OUTPUT INDICATORS (VARIABLES)
Distribution Company is system of supply of electricity to consumer or industry that is consisted of
Power Transformer Substation, Power Distribution Substation, Power Transmission Lines (including
cable) Relays, Breaker, Towers, Panels, Circuit, Meters, Switches, Power Batteries, Alarms of Safety,
Security monitoring Equipments and other Power Supply Equipments & facilities with switch yard
and power house or control room. Grid is the main central point and vital component of a power
system, the flexibility in system and also robustness interpret the reliability for the complete power
system. Operation in Grid fundamentally through the gradually reducing of the voltages and after that
delivered to the relevant industry or consumer, some of this specific process is shown in Figure 1
given below:

Vol.12, No. 1, (2021), pp. 1721–1734
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GENCO’s 500/220KV 120KV
60KV 32KV Terminal
(FIG 1) THE CHART OF POWER FOR DISCO
Figure No 1 showed regarding the different levels of voltage of electric power can be further divided
into parts of transmission level, distribution level, sale of electricity and other related systems in power
sector. 500KV and 220KV in this power supply system are related to part of the NTDC transmission
system while and DISCO’s Level this started with 120KV grids and lower are part of distribution
system, which is mainly consist about 120KV substation and supply lines even 10KV and lower are
for consumer & commercial sector as per their demand..
At the last stage of the power supply system the distribution system connected directly with consumer
including the power generation system, transmission system and distribution system is also very
important link for contacting consumer, supply of power and distribution of electricity. Normally the
system which is stepped down substation second time or the system which is providing power to
consumers after the stepping down is called the distribution system.
The distribution system has the greatest impact on supply for users. In fact, the supply of scale, level
and the degree of rationality can intensively reflect the system of structure and its operational
characteristics. Therefore, this paper will take distribution system as the research object.
Table I Input and Output Variables (Indicators)
Input Variables Output Variables
X1: Purchased Energy Sent
(GWH)
Y1: Energy Sale (GWH)
X2: Demand of Energy (MW) Y2: Distribution Loss (GWH)
Y3:Transmission Loss (GWH)
Regarding to the above principles for setting targets also combined by the real distribution system, and
taking the opinion of experts into account [12][13][14], selected the input & output variables shown in
TABLE I.
Static Descriptive Table (II)
INPUT INPUT OUTPUT OUTPUT OUTPUT
VARIABLES→ X1 X2 Y1 Y2 Y3
YEARS ↓
2014 Mean 873.33 143.62 709.89 141.57 21.21
S D 5049.63 771.148 4474.02 936.137 143.606
2015 Mean 951.93 154.83 777.47 151.58 22.89
S D 5748.08 860.332 5083.77 1042.11 161.21
2016 Mean 1029.23 165.66 843.47 161.14 24.56
S D 6369.66 940.3 5622.38 1146.04 178.801
2017 Mean 1110.73 177.42 913.87 170.57 26.3

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S D 7017.86 1025.45 6192.18 1250.94 196.948
2018 Mean 1191.25 188.95 982.62 179.59 28.05
S D 7627.12 1105.67 6716.51 1349.36 215.14
2019 Mean 1270.99 200.25 1052.51 188.33 29.76
S D 8219.08 1180.22 7241.89 1449.36 233.791
2020 Mean 1350.39 211.47 1122.15 195.72 31.5
S D 8779.18 1249.49 7734.9 1547.36 252.7
2021 Mean 1434.61 222.89 1196.34 204.99 33.3
S D 9533.93 1316.06 8244.78 1644.42 271.644
2022 Mean 1522 235.58 1274.24 213.26 25.16
S D 9969.6 1395.16 8791.34 1741.66 290.836
2023 Mean 1613.57 488.3 1356.04 223.43 37.06
S D 10623.3 7786.05 9364.93 1832.31 310.213
2024 Mean 1726.99 261.64 1438.23 221.54 38.95
S D 11446 1556.31 9939.37 1969.17 330.029
(Power Distribution Companies of Pakistan) Table III
6. DATA ANALYSIS
As per to the input & output variables (indicators) Table I, we have investigated 10 DISCO,s
Electricity supply Companies 11 years real data and averaging for getting a set of raw as data
descriptive Statics. See TABLE II. While Table III displaying The DISCO’S (Power Distribution
Companies of Pakistan)
Table IV shows Power All Annually Input-Output Indicators (Slack) for the period of 2014 to
2024.
N0 DMU NAME
1 Lesco
Stands for LAHORE ELECTRIC
SUPPLY COMPANY
2 Gepco
Stands for GUJRANWALA
ELECTRIC POWER COMPANY
3 Fesco
Stands for FAISALABAD ELECTRIC
SUPPLY COMPANY
4 Iesco
Stands for ISLAMABAD ELECTRIC
SUPPLY COMPANY
5 Mepco
Stands for MULTAN ELECTRIC
POWER COMPANY
6 Pesco
Stands for PESHAWAR ELECTRIC
SUPPLY COMPANY
7 Hesco
Stands for HYDERABAD ELECTRIC
SUPPLY COMPANY
8 Qesco
Stands for QUETTA ELECTRIC
SUPPLY COMPANY
9 Tesco
Stands for TRIBAL AREAS
ELECTRIC SUPPLY COMPANY
10 Sepco
Stands for SUKKUR ELECTRIC
POWER COMPANY

Vol.12, No. 1, (2021), pp. 1721–1734
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Summary of Slacks Distribution Companies of Pakistan (2014 to
2024)
INPUT
SLACKS: OUTPUT SLACKS:
2014
DMU
Name of
DISCO X1 X2 Y1 Y2 Y3
1 LESCO 0.000 80.125 0.000 0.000 80.830
2 GEPCO 0.000 268.661 0.000 0.000 11.424
3 FESCO 0.000 55.595 0.000 0.000 0.000
4 IESCO 0.000 0.000 0.000 34.096 7.350
5 MEPCO 0.000 0.000 0.000 0.000 0.000
6 PESCO 0.000 258.085 0.000 0.000 4.681
7 HESCO 0.000 69.933 0.000 0.000 0.000
8 QESCO 0.000 106.236 0.000 0.000 0.000
9 TESCO 0.000 0.000 0.000 0.000 0.000
10 SEPCO 0.000 0.000 0.000 0.000 0.000
2015
1 LESCO 0.000 106.358 0.000 0.000 78.365
2 GEPCO 0.000 274.491 0.000 0.000 12.893
3 FESCO
77647.179
0.000 0.000 300.780 144.997
4 IESCO 0.000 0.000 0.000 0.000 0.000
5 MEPCO 0.000 0.000 0.000 0.000 0.000
6 PESCO 0.000 156.542 16.059 0.000 3.697
7 HESCO 0.000 77.129 0.000 0.000 0.000
8 QESCO 0.000 105.850 0.000 0.000 0.000
9 TESCO 0.000 0.000 0.000 0.606 0.472
10 SEPCO
228.025
0.000 0.000 18.916 0.000
2016
1 LESCO 0.000 73.275 0.000 0.000 77.026
2 GEPCO 0.000 279.435 0.000 0.000 14.557
3 FESCO 0.000 59.429 0.000 0.000 0.000
4 IESCO 0.000 0.000 0.000 0.000 0.000
5 MEPCO 0.000 0.000 0.000 0.000 0.000
6 PESCO 0.000 79.780 25.587 0.000 3.213
7 HESCO 0.000 83.456 0.000 0.000 0.000
8 QESCO 0.000 104.541 0.000 0.000 0.000
9 TESCO 0.000 0.000 0.000 3.333 0.843
10 SEPCO 93.962 0.000 0.000 46.147 0.000
2017
1 LESCO 0.000 77.542 0.000 0.000 73.712
2 GEPCO 0.000 329.888 0.000 0.000 11.348
3 FESCO 0.000 57.973 0.000 13.070 0.000
4 IESCO 0.000 0.000 0.000 0.000 0.000
5 MEPCO 0.000 0.000 0.000 0.000 0.000
6 PESCO 0.000 0.000 0.000 0.000 0.000

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7 HESCO 0.000 90.171 0.000 0.000 0.000
8 QESCO 0.000 103.599 0.000 0.000 0.000
9 TESCO 0.000 0.000 0.000 5.700 1.364
10 SEPCO 0.000 0.000 0.000 81.627 0.000
2018
1 LESCO 0.000 82.353 0.000 0.000 66.243
2 GEPCO
62826.111
0.000 0.000 301.072 155.050
3 FESCO 0.000 53.052 0.000 41.386 0.000
4 IESCO 0.000 0.000 0.000 0.334 0.000
5 MEPCO 0.000 3.636 0.000 55.906 0.000
6 PESCO 0.000 0.000 0.000 0.000 0.000
7 HESCO 0.000 110.391 0.000 0.000 138.656
8 QESCO 0.000 100.890 0.000 0.000 0.000
9 TESCO 0.000 0.000 0.000 2.813 1.543
10 SEPCO 0.000 0.000 15.508 35.571 0.000
2019
1 LESCO 0.000 77.991 0.000 0.000 58.971
2 GEPCO 0.000 326.579 0.000 0.000 12.371
3 FESCO 0.000 47.106 0.000 64.385 0.000
4 IESCO 0.000 0.262 0.000 0.142 0.000
5 MEPCO 0.000 0.000 0.000 25.686 0.000
6 PESCO 0.000 0.000 0.000 0.000 0.000
7 HESCO 0.000 107.744 0.000 0.000 0.000
8 QESCO 0.000 96.144 0.000 0.000 0.000
9 TESCO 0.000 0.000 0.000 7.082 1.338
10 SEPCO 0.000 8.729 0.000 0.000 0.000
2020
1 LESCO 0.000 68.732 0.000 0.000 50.165
2 GEPCO 0.000 345.900 0.000 0.000 6.470
3 FESCO 0.000 38.500 0.000 87.750 0.000
4 IESCO 0.000 0.000 0.000 0.000 0.000
5 MEPCO 0.000 0.000 0.000 0.000 0.000
6 PESCO 0.000 0.000 0.000 0.000 0.000
7 HESCO 0.000 116.781 0.000 0.000 0.000
8 QESCO 0.000 117.490 0.000 0.000 126.093
9 TESCO 0.000 0.000 0.000 9.480 2.033
10 SEPCO 0.000 21.105 0.000 0.000 0.000
2021
1 LESCO 0.000 55.104 0.000 0.000 40.387
2 GEPCO 0.000 368.595 0.000 0.000 0.838
3 FESCO 0.000 28.466 0.000 127.732 0.000
4 IESCO 0.000 0.000 0.000 0.922 0.000
5 MEPCO 0.000 0.000 0.000 0.000 0.000
6 PESCO 0.000 0.000 0.000 0.000 0.000
7 HESCO 0.000 128.948 0.000 0.000 0.000
8 QESCO 0.000 90.709 0.000 0.000 0.000
9 TESCO 0.000 0.000 0.000 14.813 2.940

Vol.12, No. 1, (2021), pp. 1721–1734
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As
empirically analysis of every DISCO and the changes, and searching out the reason, initially, this
paper used genuine data [15] of input & output oriented model [16] of (win4deap2 by DEAP 2.1
software) introduced by TIM COELLI CEPA to evaluate the 11-year average result of efficiency and
the input redundancy also about the output deficit, which is a type of static analysis. However we used
the Malmquist Model at multistage of the DEAP software to analysis of every DISCO DMU at
average changes for total factor supply which is dynamic analyzing.
10 SEPCO 0.000 19.101 0.000 0.000 0.000
2022
1 LESCO 0.000 38.594 0.000 0.000 28.314
2 GEPCO 0.000 384.532 0.000 0.000 0.000
3 FESCO 0.000 12.465 0.000 163.288 0.000
4 IESCO 0.000 0.000 0.000 2.774 0.000
5 MEPCO 0.000 0.000 0.000 0.000 0.000
6 PESCO 0.000 0.000 0.000 0.000 0.000
7 HESCO 0.000 137.689 0.000 0.000 0.000
8 QESCO 0.000 87.712 0.000 0.000 0.000
9 TESCO 0.000 0.000 0.000 18.322 2.771
10 SEPCO 0.000 17.119 0.000 0.000 0.000
2023
1 LESCO 0.000 20.321 0.000 0.000 14.569
2 GEPCO 0.000 400.241 0.000 0.000 0.000
3 FESCO 0.000 0.000 0.000 0.000 0.000
4 IESCO
0.000
23998.001 0.000 4.508 0.000
5 MEPCO 0.000 0.000 0.000 0.000 0.000
6 PESCO 0.000 0.000 0.000 0.000 0.000
7 HESCO 0.000 147.629 0.000 0.000 0.000
8 QESCO 0.000 85.100 0.000 0.000 0.000
9 TESCO 0.000 0.000 0.000 0.000 0.000
10 SEPCO 0.000 0.000 0.000 0.000 0.000
2024
1 LESCO 0.000 0.000 0.000 0.000 0.000
2 GEPCO 0.000 416.445 0.000 0.000 0.000
3 FESCO 0.000 0.000 0.000 0.000 0.000
4 IESCO 0.000 0.000 0.000 0.000 0.000
5 MEPCO 0.000 0.000 0.000 0.000 0.000
6 PESCO 0.000 0.000 0.000 0.000 0.000
7 HESCO 0.000 158.351 0.000 0.000 0.000
8 QESCO 0.000 80.759 0.000 0.000 0.000
9 TESCO 0.000 0.000 0.000 31.433 3.960
10 SEPCO 0.000 0.000 0.000 0.000 0.000
MEAN MEAN
1279.957
284.521 0.520 13.633 11.268

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7. RESULT & DISCUSSION
(Table V) The DISCO’s Efficiency of Input & Output Variables
Efficiency in Power DISCO'S of Pakistan (2014 to 2024)
2014
DMU
Name of
DISCO CRSTE VRSTE SE RTS
1 LESCO 0.989 0.994 0.995 drs
2 GEPCO 0.988 0.988 0.999 irs
3 FESCO 0.986 0.987 0.999 drs
4 IESCO 0.988 0.991 0.997 irs
5 MEPCO 1.000 1.000 1.000 -
6 PESCO 0.926 0.988 0.937 drs
7 HESCO 0.942 0.947 0.994 drs
8 QESCO 0.949 0.953 0.996 drs
9 TESCO 0.956 1.000 0.956 irs
10 SEPCO 1.000 1.000 1.000 -
2015
1 LESCO 0.990 0.996 0.994 drs
2 GEPCO 0.988 0.988 1.000 -
3 FESCO 0.776 0.804 0.965 irs
4 IESCO 0.999 1.000 0.999 irs
5 MEPCO 1.000 1.000 1.000 -
6 PESCO 0.928 0.995 0.933 drs
7 HESCO 0.943 0.949 0.994 drs
8 QESCO 0.950 0.954 0.995 drs
9 TESCO 0.957 0.999 0.958 irs
10 SEPCO 0.974 0.983 0.991 irs
2016
1 LESCO 0.993 0.997 0.995 drs
2 GEPCO 0.988 0.988 1.000 -
3 FESCO 0.988 0.988 1.000 -
4 IESCO 0.999 0.999 1.000 -
5 MEPCO 0.999 1.000 0.999 drs
6 PESCO 0.936 0.999 0.937 drs
7 HESCO 0.944 0.950 0.993 drs
8 QESCO 0.950 0.955 0.994 drs
9 TESCO 0.957 0.996 0.961 irs
10 SEPCO 0.964 0.968 0.996 irs
2017
1 LESCO 0.994 0.998 0.995 drs
2 GEPCO 0.986 0.987 0.999 irs
3 FESCO 0.989 0.989 1.000 -
4 IESCO 1.000 1.000 1.000 -
5 MEPCO 0.999 1.000 0.999 drs

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6 PESCO 0.948 1.000 0.948 drs
7 HESCO 0.945 0.951 0.993 drs
8 QESCO 0.950 0.956 0.993 drs
9 TESCO 0.958 0.995 0.963 irs
10 SEPCO 0.958 0.959 1.000 -
2018
1 LESCO 0.994 0.999 0.995 drs
2 GEPCO 0.678 0.706 0.960 irs
3 FESCO 0.992 0.992 1.000 -
4 IESCO 1.000 1.000 1.000 -
5 MEPCO 0.995 0.998 0.997 drs
6 PESCO 0.959 1.000 0.959 drs
7 HESCO 0.940 0.950 0.989 drs
8 QESCO 0.950 0.957 0.993 drs
9 TESCO 0.959 0.989 0.970 irs
10 SEPCO 0.958 0.962 0.996 drs
2019
1 LESCO 0.995 0.999 0.996 drs
2 GEPCO 0.988 0.989 1.000 -
3 FESCO 0.993 0.994 1.000 -
4 IESCO 1.000 1.000 1.000 -
5 MEPCO 0.998 1.000 0.998 drs
6 PESCO 0.969 1.000 0.969 drs
7 HESCO 0.947 0.954 0.993 drs
8 QESCO 0.949 0.957 0.992 drs
9 TESCO 0.960 0.989 0.971 irs
10 SEPCO 0.959 0.967 0.991 drs
2020
1 LESCO 0.996 1.000 0.997 drs
2 GEPCO 0.989 0.989 1.000 -
3 FESCO 0.995 0.995 1.000 -
4 IESCO 0.999 1.000 1.000 -
5 MEPCO 1.000 1.000 1.000 -
6 PESCO 0.978 1.000 0.978 drs
7 HESCO 0.949 0.956 0.992 drs
8 QESCO 0.944 0.956 0.988 drs
9 TESCO 0.961 0.987 0.974 irs
10 SEPCO 0.963 0.977 0.985 drs
2021
1 LESCO 0.996 0.999 0.997 drs
2 GEPCO 0.989 0.989 1.000 -
3 FESCO 0.997 0.997 1.000 -
4 IESCO 1.000 1.000 1.000 -
5 MEPCO 1.000 1.000 1.000 -
6 PESCO 0.986 1.000 0.986 drs

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7 HESCO 0.936 0.942 0.993 drs
8 QESCO 0.950 0.959 0.990 drs
9 TESCO 0.962 0.987 0.975 irs
10 SEPCO 0.966 0.985 0.980 drs
2022
1 LESCO 0.998 1.000 0.998 drs
2 GEPCO 0.989 0.990 0.999 drs
3 FESCO 0.999 0.999 1.000 -
4 IESCO 1.000 1.000 1.000 -
5 MEPCO 1.000 1.000 1.000 -
6 PESCO 0.993 1.000 0.993 drs
7 HESCO 0.951 0.959 0.992 drs
8 QESCO 0.950 0.960 0.989 drs
9 TESCO 0.964 0.986 0.978 irs
10 SEPCO 0.970 0.994 0.976 drs
2023
1 LESCO 0.999 1.000 0.999 drs
2 GEPCO 0.989 0.990 0.999 drs
3 FESCO 1.000 1.000 1.000 -
4 IESCO 1.000 1.000 1.000 -
5 MEPCO 1.000 1.000 1.000 -
6 PESCO 1.000 1.000 1.000 -
7 HESCO 0.953 0.961 0.992 drs
8 QESCO 0.950 0.961 0.989 drs
9 TESCO 1.000 1.000 1.000 -
10 SEPCO 0.974 1.000 0.974 drs
2024
1 LESCO 1.000 1.000 1.000 -
2 GEPCO 0.989 0.990 0.999 drs
3 FESCO 0.999 1.000 0.999 drs
4 IESCO 1.000 1.000 1.000 -
5 MEPCO 1.000 1.000 1.000 -
6 PESCO 1.000 1.000 1.000 -
7 HESCO 0.954 0.962 0.992 drs
8 QESCO 0.950 0.962 0.988 drs
9 TESCO 0.966 0.986 0.980 irs
10 SEPCO 0.982 1.000 0.982 drs
MEAN
0.972 0.982 0.990
About TABLE V when assumed that constant returns to scale Crste represents. The Technical Change
(Techch) which is the obtained result depends on the BC 2 Model while not assuming constant returns
to scale the Vrste indicted the Efficiency Change (Effch), which is to be be decomposed to Pure
Efficiency Change (Pech) and Scale Efficiency Change (Sech). Scale states the returns to scale,

Vol.12, No. 1, (2021), pp. 1721–1734
1732
scale=crste / vrste. The Vrste and Scale are the results depending upon C2R Model. And the column at
last, IRS & DRS respectively showed the increased, Constant(-) and decreased returns to scale. They
are evaluated from ∑λ j , ∑λ j < 1 , This indicates the increased returns to scale, ∑λ j = 1 , this
indicates the Constant returns to scale, ∑λ j > 1 , this indicates the decreased returns to scale.
Malmquist index has an advantage, namely it doesn’t need to involve whether to consider constant
returns to scale or not, because when calculating, Malmquist model uses both Crste and Vrste.
Malmquist indexes, namely Tfpch, can be decomposed into Efficiency Change (Effch) and Technical
change (Techch), and Efficiency change (Effch) can be further decomposed into Pure Efficiency
Change (Pech) and Scale Efficiency Change (Sech).
While Effch≥1 meaning about the overall Efficiency has been raised upward, Pech≥1 meaning Pure
Efficiency has been incresed, Sech≥1 meaning Scale Efficiency has been enhanced, Techch≥1
meaning the progress in technology, Total Factor Productivity Tfpch is decomposed into Effch and
Techch, when Effch and Techch combined operate and make Tfpch increase, then the Tfpch≥1.
(FIG 2) Power System in Pakistan
[Note]
CRSTE
Stands for Technical
Efficiency from CRS DEA
VRSTE
Stands for Technical
Efficiency from VRS DEA
SE
Stands for Scale
Efficiency=CRSTE/VRSTE
RTS
Stands for Return to
Scale(DRS IRS CRS)
DRS
Stands for Decreasing Return
to Scale
IRS
Stands for Increasing Return
to Scale
CRS
Stands for Constant Return
to Scale (-)*symbol
Generation
500kV
220kV
500kV
220kV
132 kV
11kV
IPPs
PAEC
K-Electric
WAPDA
Bulk Buyer
Public
Lightening
Agricultural
Consumer
Industrial
Consumer
Commercial
Consumer
Domestic
Consumer
DISCOs
K-Electric
Transmission

Vol.12, No. 1, (2021), pp. 1721–1734
1733
TABLE V explained about the results of efficient and no efficient DMUs as below yearly.
In year 2014 DMU 1 & 10 is high efficient and 6 & 7 is lower efficient with decreasing trend and 2 &
4 increasing.
In year 2015 DMU 4 & 5 is high efficient and 3 & 7 is lower efficient with decreasing trend 4 & 10
increasing.
increasing.
increasing.
increasing.
In year 2019 DMU 4 & 5 is high efficient and 3 & 8 is lower efficient with decreasing trend 9
increasing.
increasing.
increasing.
increasing.
In year 2023 DMU 4 & 5 also 4 & 6 is high efficient and 7 & 8 is lower efficient with decreasing trend
no increasing.
In year 2015 DMU 4 & 5 also 1 &2 is high efficient and 7 & 8 is lower efficient with decreasing trend
9 increasing.
From TABLE V, compared with the 10 DMUs the efficiency of 4 and 5 are the highest DMUs,
namely effective. DMU 9 is increasing while the other 7 and 8 indicated failure to achieve the high
innovation efficiency because of the mainly their respective efficiency to scale are at lower stage and
returns to scale are at decreasing trends.
At average level of 11 years data the result of Technical Efficiency is by CRS 97.2% by VRS 98.2%
and Scale Efficiency is 99.0%.By the achieved result, we can judge the result that each DMU should
focus on improvement regarding the Technical Changes for the purpose to raise the Total Factor
Productivity.
8. CONCLUSION
At this current stage, we know the effectiveness of power generating companies has been paid wide
level attention for research. Also a lot of researchers used the DEA techniques to examination of this
subject of Generation while the distribution companies are rarely selected as main research purpose.
There are still few important fields which are required for new findings. However, by purpose to adapt
to the new reforms and latest development of the electricity distribution sector, this research is a small
try to understand the input & output effectiveness of distribution companies from more critical aspect.
The explained result indicated that Technical Efficiency is by CRS 97.2% by VRS 98.2% and Scale
Efficiency is 99.0%. While the input redundancy existed, so it is necessary for the management to
made better distribution system plans and investing management technology, and to save the excessive
wastage of available precious resources. In specifically as for the ineffective & lower level DMUs,
under the premise of emphasizing its operational procedures and for economic society coordination on
development the management should take general consideration, as per the direction of redundancy
and its amounts for grasp out the direction of DISCO grid system performance. Specially for

Vol.12, No. 1, (2021), pp. 1721–1734
1734
diminishing the line losses (distribution & Transmission) rate & improvement of technology in each
level there has a big space for management should put enough good effort in these potential areas.
9. REFERENCE
[1] Shen Yuzhi1 , Zhangna, “Study of the Input-Output Overall Performance Evaluation of
Electricity Distribution Based on DEA Method”, Energy Procedia 16 (2012) 1517 – 1525.
[2] Soonhu Soh & Md Tamzid Parves, “An Efficiency Analysis of Combine Cycle Power Plants
using DEA Models: A case study in Bangladesh” International Journal of Mechanical and Production
Engineering Research and Development (IJMPERD) ISSN (P): 2249-6890; ISSN(E): 2249.
[3] Luo Daoping and Xiao Di, “The application of data envelopment analysis (DEA) in electric
power industry”, System Engineering Theory and Practice, Apr.1996, pp.60-65.
[4] Wang Enchuang, Ren Yulong and Liu Zhen, “Input-output efficiency assessment of
Chongqing distribution network by using DEA”,East China Power, vol.36,Jun.2008, pp.34-37.
[5] Zhou Ming, Zhao Wei, Wang Peng and Li Gengyin, “A hierarchical yardstick competition
approach to assessing operation performance of distribution utilities”, Electrical power system
automation, vol.32, Apr.2008, pp.20-24.
[6] Charnes A, Cooper W W and Rhodes E, “Measuring the efficiency of decision making units”,
European Journal of Operational Research, Feb.1978, pp.429-444.
[7] Charnes A, Cooper W W and Lewin A, “Data envelopment analysis: theory, methodology and
application”, Kluwer Acdemic, 1994.
[8] Zhou, P., Ang, B. W., & Poh, K. L. (2008). A survey of data envelopment analysis in energy
and environmental studies. European Journal of Operational Research, 189(1), 1–18.
https://doi.org/10.1016/j.ejor.2007.04.042.
[9] Jarait́e, J., & Di Maria, C. (2012). “Efficiency, productivity and environmental policy: A case
study of power generation in the EU. Energy Economics, 34(5).
https://doi.org/10.1016/j.eneco.2011.11.017
[10] Afza, T., & Asghar, M. J. A. (2012). “Financial reforms and efficiency in the insurance
companies of Pakistan. African Journal of Business Management”, 6(30), 8957–8963.
https://doi.org/10.5897/AJBM11.1821
[11] B Mahlberg, M Luptacik system, European Journal of Operational Research 234 (3), 885-897.
[12] Wang Enchuang, Ren Yulong and Zhu Chunbo, “The overall efficiency study of distribution
network based on fuzzy DEA method”, Industrial Engineering and Management, vol.14, Feb.2009,
pp.81-87.
[13] Wang Enchuang, Ren Yulong and Zhu Chunbo, “The evaluation study of electrical energy-
environment coordinated development based on DEA”, Technology Management Research,
Mar.2009, pp.164-166.
[14] Teng Fei and Wu Zongxin, “Performance Analysis of China Electric Power Enterprises”,
Quantitative and Technical Economics Research. Jun.2003,pp.127-130.
[15] Survey Reports of DISCO’s in Pakistan published by Ministry of Energy Power Division
Pakistan.
[16] Mushtaq, N & Saddique,F, “Efficiency of Power Generation Companies in Pakistan:
Application of Non-Parametric Approach” Ilkogretim Online - Elementary Education Online, 2020;
Vol 19 (Issue 4): pp. 3486-3504 http://ilkogretim-online.orgdoi::10.17051/ilkonline.2020.04.764735.

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