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342 IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 15, NO. 3, SEPTEMBER 2000

Electric Power System Reliability CriteriaDetermination in a Developing Country—An

Investigation in NepalMohan K. Pandey and Roy Billinton

 Abstract—This paper examines the problems associated withusing power system reliability cost/worth techniques in a devel-oping country, and presents methods to develop planning criteriaincorporating explicit recognition of reliability worth. The devel-oped methods are illustrated by application to theNepalIntegratedElectric Power System.

I. INTRODUCTION

RELIABILITY of electric power supply is virtually taken

for granted in most developed countries. This is not thecase in developing countries, where resources are scarce and

many basic development projects compete for the limited funds.

A critical issue faced by most developing countries is that the

demand for electric power is high and growth in supply is re-

strained by technical, environmental, and most importantly by

financial constraints. Many power projects are being canceled

or postponed due to a lack of resources or economic justifica-

tion. The investment burden associated with the electric power

sector has already led some developing countries into serious

debt problems [1].

There are many developing countries, specifically in Asia and

Africa, where resources are scarce and developments in other

basic sectors such as education, health and agriculture are ata subsistence level. Increasing demands for electric power and

the resulting large capital investment requirement has created a

tremendous economic burden. The issue is not that power de-

velopment should assume decreased importance, or that this

capital intensive sector will not continue to merit significant

resources in the future, but rather that it has to be scrutinized

carefully and greater justification is required to implement fu-

ture power projects. One approach to overcome these difficulties

is to explore and implement effective system planning criteriabased on fundamental principles of power system reliability and

economics.

Power system planning criteria used in developing countries

are usually extrapolated from similar criteria adopted by moredeveloped countries. This is usually done in the absence of basic

system data, and with little recognition of the explicit worth as-

sociated with the reliability of electric power supply. This paper

examines the fundamental problems associated with using a re-

liability cost/worth technique to develop planning criteria in a

Manuscript received November 6, 1998.The authors are with the Power Systems Research Group, University of 

Saskatchewan, Saskatoon, Saskatchewan, Canada S7N 0W0.Publisher Item Identifier S 0885-8969(00)08442-4.

TABLE IPERTINENT SALIENT FEATURES OF NEPAL [2], [3]

developing country, and presents methods to incorporate ex-

plicit recognition of reliability worth in the planning process.

The Nepal Integrated Electric Power System (NPS) is used as a

surrogate developing country system.

II. THE NEPAL INTEGRATED ELECTRIC POWER SYSTEM (NPS)

Nepal is a small developing country located in South Asia. It

borders with China in the north and India in the south, east and

west. Some of the pertinent salient features of the country are

shown in Table I [2], [3]. More than 80% of the population de-

pends on agriculture and lives in the rural areas of the country.

Industries and businesses are mostly located in the major cities

and towns. Tourist industries are the main sources for interna-

tional currency income in the country. Hydro power is the main

resource for electricity generation. Major development projects

have been financed by international development agencies such

as the World Bank.

The single line diagram for the NPS is shown in Fig. 1.

The system has 11 generator buses, 33 load buses, and 2 tiebuses. There are 30 generating units in the system having

capacities ranging from 2.5–30 MW with a total installed

capacity of 274 MW. The 46 bus locations are connected by 44

transmission lines and 21 transformers. The transmission lines

are at the two voltage levels of 132 kV and 66 kV. There are

two 132/66 kV tie stations in the system. The system peak load

is expected to be 300 MW in 1998 [3].

Some observations can be made on the NPS performance in

the resent past [3]. The operating ratio (i.e. operating cost as a

fraction of revenue) increased from 0.66 in 1987 to 0.97 in 1994.

0885–8969/00$10.00 © 2000 IEEE

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PANDEY et al.: ELECTRIC POWER SYSTEM RELIABILITY CRITERIA DETERMINATION IN A DEVELOPIN G COUN TRY 343

Fig. 1. Single line diagram of the NPS.

The system network loss has been more than 25% for the past

several years. The total long term loans from various interna-

tional development agencies has increased from US $65 million

in 1987 to over 300 million in 1994. In addition, financial indi-

cators such as the rate of return on assets, self financing ratio,

etc., have declined over the years.The NPS has made significant growth in terms of electrical

energy generation and customers served. The average energy

growth rate is approximately 10% per year and the average

growth in number of consumers served is 12% per annum. The

peak demand has increased from 126 MW in 1987 to 244 MW

in 1995, an average annual increase of approximately 10%.

The growth in demand required large investment and high rates

of expansion, which has given the NPS a tremendous economic

burden.

Many utilities in developing countries are facing similar diffi-

culties in their power systemplanning andoperation as those ob-

served by the NPS. Many new power projects are being canceled

or postponed due to a lack of resources, environmental prob-

lems or other societal concerns. A more rational and consistent

evaluation approach is therefore required to justify future power

projects in developing countries. This paper explores the possi-

bility of using such an approach considering the NPS as a sur-

rogate power system for developing countries.

III. SYSTEM EVALUATION FRAMEWORK

The evaluation framework used in the investigation is shown

in Fig. 2.

The object in the approach is to select an expansion scheme

having the minimum overall cost. The overall cost is the sum

of investment, operating and customer costs. If the proposed al-

ternative cost is not an overall minimum, the expansion scheme

is modified or a new scheme is considered and assessed again.

This procedure is repeated until the least overall cost alternative

is obtained. The newly developed system configuration which

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344 IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 15, NO. 3, SEPTEMBER 2000

Fig. 2. A reliability cost/worth based system evaluation framework.

produces the optimum reliability level is then used in future de-

velopment of the power system. The corresponding set of in-dices obtained in the evaluation can be used as planning criteria

for evaluation of future power projects. The overriding objec-

tive is to ensure that the system expansion is implemented only

when the resulting costs do not exceed the societal benefit.

IV. RELIABILITY DATA

The information required for reliability studies are the gen-

eration, transmission and load data. Investment, operating and

customer interruption cost data are also required for cost/worth

studies. The interruption cost data is obtained using customer

surveys.

One of the main reasons frequently cited for not using proba-bilistic methods in power system evaluation is the lack of data.

The increasing popularity of applying probabilistic techniques

in system evaluation has created considerable demand for the

collection of outage data and other relevant information. Sub-

stantial work has been done in developed countries to estab-

lish and implement suitable data collection schemes. Most util-

ities in developed countries now have reasonably adequate re-

liability data bases [4]–[6]. This is not the case in developing

countries where quantitative reliability assessment techniques

are virtually new and therefore most data required for their ap-

plication are not available. This situation required an investiga-

tion of the availability of the required data in the NPS, and to

develop a methodology to derive unavailable data using a prac-tical approach.

The initial work in Nepal began with visits to the Nepal Elec-

tricity Authority (NEA) headquarters in the capital city of Kath-

mandu. The objective was to familiarize the NEA with the re-

search objectives and to gain support from the key personnel in

the utility. Suitable data collection forms were developed and

used to collect available data and information. It was found

that most data, specifically probabilistic data, required for re-

liability studies are not available in the NPS. The deterministic

data available were assembled from various previous studies, re-

ports and information provided by the NEA. The required prob-

abilistic data were derived using the information provided and

some data pooling from external sources [5], [6]. A method-

ology adopted to derive the unavailable data required for thereliability studies is briefly described below.

Deterministic data pertinent to individual generating units

existing in the system are available and were obtained from

Ref. [2]. The required stochastic data are not available at the

present time. The required data were derived using the informa-

tion given in Ref. [5] and the actual generating unit character-

istic information obtained from the NEA. Ref. [5] lists average

outage data in terms of generating unit type, maximum contin-

uous rating (MCR), years of service and operating factor. For ex-

ample, the Forced Outage Rates (FOR) for a 30 MW hydro unit

with 20 years in service and an operating factor of 90%, [units

connected at the Kulekhani I power station (bus 11)], are 4.51%,

3.81%, and 1.11% respectively. The average of these values is3.1%, and therefore 3% was selected as the FOR. The FOR for

other units were similarly derived.

Similarly, deterministic data such as line impedances and sus-

ceptances, current carrying capacities etc.,are available from the

past studies and reports [2], [3]. The required probabilistic data

are not available and therefore these data for lines and trans-

formers were derived using the practical data given in Ref. [6]

and the actual transmission component information obtained

from the NEA. Other data were similarly derived from external

sources and used in the analysis.

The required cost data such as thecapital, operatingand main-

tenance costs for generating units and lines are available and

were provided by the NEA. A load model similar to that pro-

posed in the IEEE-RTS [7] was created for the NPS using the

actual annual system load data collected during the data investi-

gation in Nepal. The developed load model was utilized to gen-

erate all the required daily and hourly load data for the study.

V. CUSTOMER SURVEYS

The explicit recognition of the worth of electric service

reliability in the planning process requires an estimate of 

the customer costs or monetary losses resulting from power

outages. This is normally done using customer surveys. Most of 

the customer surveys conducted in the past were in developed

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PANDEY et al.: ELECTRIC POWER SYSTEM RELIABILITY CRITERIA DETERMINATION IN A DEVELOPIN G COUN TRY 345

Fig. 3. Sector customer damage functions for the NPS.

countries, such as Sweden, Finland, France, UK, USA and

Canada [8]. The Power System Research Group of the Uni-

versity of Saskatchewan has conducted extensive surveys inCanada since 1980 [9]. The Research Group conducted surveys

in Nepal in 1996 [10], [11]. One of the main objectives of 

this research work was to extend the survey approach to a

developing country and to examine the-problems associated

with incorporating the approach in such a system. The survey

methodology and the results are given in detail in the references

cited above.

The data obtained from customer surveys were used to create

sector customer damage functions (SCDF). Fig. 3 shows the

SCDF for the residential, commercial and industrial sectors of 

Nepal. The costs are aggregated average, country-wide inter-

ruption costs in 1996 Nepalese Rupees (Rs). The SCDF can be

weighted in proportion to their energy utilizationwithin a partic-ular service area to create a composite customer damage func-

tion (CCDF) for the service area of interest [12].

VI. SYSTEM EVALUATION AND PLANNING CRITERIA

DETERMINATION

Composite system evaluation considers both generation and

transmission facilities in the analysis. This assessment process

is also known as “bulk power system” evaluation. The trans-

mission facilities represent a significant portion of the overall

power system cost. In addition, a failure in the transmission net-

work may cause wide-spread outages, which may result in con-

siderable costs to customers. It is therefore important that thetransmission network be included in addition to the generation

system in the reliability cost/worth evaluation.

The fundamental concept underlying composite system

assessment is the ability to evaluate the impact at each indi-

vidual load bus due to modification(s) in the system and to

aggregate the individual bus impacts to evaluate the overall

system. Composite system evaluation therefore requires system

network topology details in addition to the individual load bus

and overall system information.

The cost of interruptions obtained through the customer sur-

veys were used to develop CCDF for each individual bus using

the sector energy demand composition of the relevant service

TABLE IIINDIVIDUAL MAJOR NPS LOAD BUS IEAR IN Rs/kWh

TABLE IIIINDIVIDUAL LOAD BUS IEAR AND THE SYSTEM IEAR

area. The individual bus CCDF were then used in conjunctionwith the expected energy not supplied (EENS) to calculate the

individual bus interrupted energy assessment rate (TEAR). This

requires an evaluation program that can generate for each in-

dividual bus, the variables such as the magnitude, frequency

and the duration of load curtailment due to the various pos-

sible system outage contingencies. The detailed formulation for

the IEAR determination in a composite power system can be

found in Ref. [13]. The composite system evaluation program

COMREL developed at the University of Saskatchewan was

used in the research work.

The COMREL program was modified to incorporate indi-

vidual load bus CCDF and then used to evaluate the IEAR for

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346 IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 15, NO. 3, SEPTEMBER 2000

TABLE IVOPTIMUM OVERALL CONFIGURATION DETERMINATION FOR THE NPS FOR 1995–1999 PERIOD

the NPS load buses. The individual load bus CCDF was de-

veloped using the SCDF shown in Fig. 3 and the sector load

composition at the bus. The sector load composition data for

all the NPS load buses were not available due to a lack of ad-

equate record keeping system in the NEA. However, data for

some major system load buses were available. These load buses

carry more than 55% of the system load.

The developed CCDF and the NPS generation and transmis-

sion data were used to evaluate the individual bus IEAR for the

major NPS load buses. The results are shown in Table II. Buses

5, 7, 9, 14, and 16 serve the important load centers in the Kath-

mandu region (see Fig. 1). Bus 24 serves the major industrial

town of Hetaunda, whereas Bus 29 serves the major city of Bir-

gunj, both in the central development region of the country. Bus

32 serves the major city of Biratnagar in the eastern develop-

ment region, and Bus 40 serves the major load center in the

western region of the country.

It can be seen from Table II that the individual bus IEAR

differs from bus to bus. Bus 16 in the Kathmandu region has

the highest IEAR value at Rs 68.54/kWh whereas Bus 40 of 

the western development region has the lowest value at Rs

13.48/kWh. As noted earlier, the sector load demand data for

all the load buses in the NPS were not available. The following

approach was used to estimate appropriate values for those loadpoints with inadequate data.

It is generally understood that the IEAR in a particular service

area depends on the way electricity is used, or in other words,

the socio-economic status of that area. Based on this concept,

it is reasonable to assign a known service area IEAR to other

areas in its vicinity, or if required to a region represented by that

particular service area. In order to apply the approach, the NPS

service area was divided into four distinct regions. These are the

Kathmandu, central, eastern and western regions (see Fig. 1).

The evaluated MAR of the major buses in a particular region,

whose sector composition data were available, were averaged

and rounded to a two digit Rs value. The standardized regional

IEAR was then assigned on a regional basis to the buses whose

IEAR could not be directly evaluated due to the lack of data.

Table III presents the resulted individual bus IEAR for all the

load buses in the NPS and the system IEAR. The system IEAR is

obtained by summing the weighted individual bus IEAR, where

the weight used is the load at a particular bus as a fraction of 

the system load. The composite system IEAR calculated for the

NPS is 25.93 or approximately Rs 26/kWh.

The individual bus IEAR and the system IEAR can be used

in conjunction with the EENS at each load point and the overall

systemto predict load point and systeminterruption costs for theexisting system and for changed conditions due to load growth

and/or system modifications. The predicted interruption costs

are then used to determine the optimum overall expansion in the

form of additional generation and transmission facilities. These

evaluation concepts were applied to the NPS to determine the

planning criteria.

The expansion plan and reinforcement scheme proposed by

the NEA for the period 1995–1999 can be found in Ref. [2].

The proposed plan was used in the analysis. An expansion plan

based on the reliability cost/worth approach was derived and

compared with the NEA plan. The 1995 system configuration

without the addition of generating units and lines is considered

as the base case. The generating units and the transmission linesproposed by the NEA were considered as available generating

units and lines in the analysis. The transmission lines proposed

for addition in 1995 and 1998 are referred to as L1, L2, L3, and

L4 respectively in this evaluation. The available generating units

and lines were sequentially added for each year in the planning

period 1995–1999 and the total annual cost for each addition

calculated. The total cost is the sum of the investment cost, op-

erating cost and the customer cost of interruption. Each addition

sequence was considered as an expansion alternative, and the al-

ternative having the least annual societal cost was selected. The

results are shown in Table IV.

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PANDEY et al.: ELECTRIC POWER SYSTEM RELIABILITY CRITERIA DETERMINATION IN A DEVELOPIN G COUN TRY 347

TABLE VOPTIMUM OVERALL CONFIGURATION AND CORRESPONDING RELIABILITY CRITERIA FOR THE NPS

It canbe seen from Table IV that thebase case configuration is

adequate for both 1995 and 1996. One 26 MW unit is required in

each year 1997 and 1998, and two 26 MW units are required in

1999. The proposed NEA expansion is also shown in Table IV.

It can be seen that the additional transmission lines proposed

by the NEA for the 1995–1999 period are not justified on the

basis of the reliability cost/worth analysis. It can also be seen

from Table IV that the reliability cost/worth approach limits the

reserve margin to an average of about 7.5%, whereas the NEAplan proposes to maintain an average of 15%.

The optimum system configuration derived for the period

1995–1999 was further analyzed to evaluate the corresponding

risk indices. The evaluation was performed and the risk indices

of some selected major load points and the overall system were

calculated. The results are shown in Table V. These results were

used to develop appropriate criteria for the NPS.

It can be seen from Table V that the risk index corresponding

to the optimum configuration varies in each year in the plan-

ning period 1995–1999. The maximum system risk index of 

1783 UPM occurs in 1999, and for the load points the maximum

risk indices indicated by failure frequencies occur in different

years for different load buses. It is normal practice in power

system planning to maintain the risk at or below a maximum

permissible level when evaluating the various possible expan-

sion alternatives. The maximum optimum risk indices obtained

in this evaluation can be considered as the permissiblerisk limits

for the selected load points and the overall system and used as

probabilistic criteria for the NPS.

VII. CONCLUSION

This paper examines some of the problems associated with

using a reliability cost/worth technique to develop suitable plan-

ning criteria in a developing country. Using the Nepal IntegratedElectric power System as a surrogate system, the paper con-

siders the likely available data, the data that could be realis-

tically estimated from external sources and those data which

are specific to the system under study, and shows that relia-

bility cost/worth evaluation is both possible and practical in a

developing country. The principal contribution of this paper is

the recognition of the overall problems associated with deter-

mining power system reliability criteria and methodologies for

a developing country, and the creation of a practical framework 

to incorporate explicit recognition of reliability worth in the

criteria and methodologies. The developed concepts are illus-

trated by application to the NPS. The overall approach, however,

can be used with relevant modifications by utility planners in

similar developing countries to formulate reliability criteria and

methodologies in order to justify future power projects.

REFERENCES

[1] M. Munasinghe, “Power sector issues and policy in developing coun-tries,” in Fifth Latin American and Caribbean Conference on ElectricPower and Energy, Caracas, Venezuela, Sept. 1988.

[2] Nepal Electricity Authority, “Ten year transmission and distributionplan, Part A—Expansion plan,” EDF International, France, Reportprepared by, December 1990.

[3] , A Year in Review 1994/95, August 1995.[4] R. N. Allan, R. Billinton, A. M. Breipohl, and C. H. Grigg, “Bibliog-

raphy on the application of probability methods in power system reli-ability evaluation 1992–1996,” A Paper Approved by the IEEE Power 

 Engineering Society for Publication in the IEEE Transactions on Power System.

[5] “Generation Equipment Status,”, Oct. 1992.[6] Canadian Electrical Association, “Forced Outage Performance of Trans-

mission Equipment,” , Dec. 1991.[7] IEEE Committee Report, “IEEE reliability test system,” IEEE Trans-

action on Power Apparatus and Systems, vol. PAS-98, pp. 2047–2054,1979.

[8] R. Allan, R. Billinton, and L. Salvaderi, Applied Reliability Assessment 

in Electric Power Systems. New York, NY: IEEE Press, IEEE Inc.,

1991.[9] R. Billinton, G. Wacker, and G. Tollefson, “Assessment of ReliabilityWorth in Electric Power Systems in Canada,”, Final Report for NSERCStrategic Grant STR0045005, June 1993.

[10] R. Billinton and M. Pandey, “Reliability worth assessment in a devel-oping country—Residential survey results,” A Paper Approved by the

 IEEE Power Engineering Society for Publication in the IEEE Transac-tions on Power System..

[11] , “Reliability worthassessment in a developingcountry—Commer-cial and industrial survey results,” A Paper Approved by the IEEE Power 

 Engineering Society for Publication in the IEEE Transactions on Power System.

[12] G. Wacker, R. Billinton, and R. K. Subramaniam, “Using cost of electricservice interruptions survey in determination of a composite customerdamage function,” in Proceedings of the International Association of Science and Technology for Development, IASTED, Energy Symposia,San Francisco, CA, June 4–6, 1984.

[13] R. Billinton, Evaluation of Reliability Worth in an Electric Power System: Elsevier Science Limited, 1994, pp. 15–23.

Mohan K. Pandey, a native of Kathmandu, Nepal obtained a B.Sc. in electricalengineering from the Regional Engineering College, Rourkela, India, a M.S. inTechnical Education from the Oklahoma State University, USA, and M.Sc. andPh.D. degrees in electrical engineering from the University of Saskatchewan,Canada. He is a ProfessionalEngineerin theprovince of Saskatchewan, Canada.

Roy Billinton obtained B.Sc. and M.Sc. degrees from the University of Manitoba and Ph.D. and D.Sc. degrees from the University of Saskatchewan.Presently Associate Dean, Graduate Studies, Research and Extension of theCollege of Engineering at the University of Saskatchewan. He is a professionalengineer in the province of Saskatchewan, Canada