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Distributed Generation and Distribution System Reliability

Y.G.Hegazy M.M.A.Salama A.Y.Chickhani,Dept. of Electric Power & Machines

Ain Shams University,1 Sarayat St.

Abbasia, Cairo, Egypt.

Electrical and Computer Engineering

Dept., University of Waterloo,

Waterloo, Ontario, Canada N2L 3G1.

Electrical & Computer Engineering Dept.

Royal Military College of Canada,

P.O.Box 17000, Station Force, Kingston,

Ontario, Canada K7K 7B4.

Abstract-This paper presents a general assessment

of the impact of distributed generation (DG) on the

distribution system reliability. A typical case study

is presented where distribution system reliability

indices are calculated for an existing system without

DG and compared to those calculated for the samesystem with some DG units running in parallel with

the system. The state duration sampling technique

and Monte Carlo simulation are used to perform

the required reliability analysis.

I. IntroductionDistributed generation (DG) is anticipated to become amajor source of electric energy in distribution systemsin the near future [1,2]. The main reasons behind theexpected widespread of DG are: [3,4] The deregulation in the power market, which

encourages public investment to sustain thedevelopment in the power demand. Thisdevelopment has led to the breaking up ofinvestments (small generating units).

The emergence of new generation techniques withsmall ratings, ecological benefits, increased

profitability, and which can be combined withheat generation.

The saturation of the existing networks and thecontinuous growth of the demand.

Several publications addressed the technical meritsassociated with the implementation of distributedgeneration [4-6]. Barker [4] analyzed the impact ofDG on the voltage regulation, losses and short circuitlevels of radial distribution systems. Farls et. al [5]highlights the use of DG as a viable solution formanaging electric energy costs. Willis [6] discussedthe use of DG to shave the peak load and to providemore capacity to the system. The main conclusionsdrawn from these studies are DG can provide voltagesupport, reduce the energy loss, and release the systemcapacity. However, the difficulties in the operationand control of distribution systems are still the mainchallenges associated with the parallel operation ofDG with these systems. Girgis et al [7] investigatedthe effect of DG on protective device coordination in

distribution systems and pointed out the need for non-conventional schemes to protect systems with DG.Hadjsaid et al. [8] presented a discussion on theincrease of the complexity of controlling and

protecting the distribution systems with DG. Theislanding phenomenon and the method used to detectit and protect the system against its consequences has

been the subject of several publications among thesepapers are Kim et al. [9] and Usta et al [10].This paper focuses on the assessment of the impact of

DG on the distribution system reliability. The maingoal of this study is to examine the existence ofcontinuity between the supply points and the load

points when DG units are running in parallel with thesystem and to calculate the indices used to predictoverall system reliability. The state duration samplingtechnique and Monte Carlo simulation areimplemented in this paper for the adequacyassessment of the system and the calculation ofreliability indices.

II. Reliability Indices

The predictive reliability assessment of distributionsystems requires the evaluation of two groups ofindices namely, load point indices and system

performance indices [11]. The load point indices are,

the average load point failure rate ( failures/year), theaverage load point outage rate (r hr/failure) and theaverage annual load point outage time (U hr/year).Analytically, these indices are calculated using thefollowing equations [11]:

= is (1)

=

i

ii

s

rr

(2)

sss rU = (3)

Where i is the number of feeder sections (main orlaterals) connecting the load point to the supply and sis the name of this load point.The system performance indices are the weightedaverages of the load point indices. The most commonsystem indices are the system average interruptionfrequency index (SAIFI), the average service


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availability index (ASAI), the system averageinterruption duration index (SAIDI) and the customeraverage interruption duration index (CAIDI). Thedefining equations for these indices are as follows:

=

==R

iiN

R

ii

Ni

SAIFI

1

1

(4)

=

==

R

i

ii

R

i

ii

N

NU

CAIDI

1

1

(5)

=

=

=

=R

ii

N

R

i

R

ii

Ni

Ui

N

ASAI

1

8760

1 1

8760

(6)

=

==R

ii

N

R

ii

Ni

U

SAIDI

1

1 (7)

Where, andNi are the failure rate and the number ofcustomers at load point i respectively, R is the set ofload points in the system and Mi is the number ofcustomers affected at load point i.

III. Simulation Procedure The time sequential Monte Carlo simulation is adoptedin this paper as an efficient method for thedetermination of the system reliability indices. In timesequential simulation, an artificial history that showsthe up and down times of each element of the system isgenerated in chronological order using random numbergenerators and the probability distribution of theelement failure and restoration parameters [11]. Asequence of operating-repair cycles of the system isobtained from the history of the components using therelationship between the element states and the systemstates. Figure 1 shows an example of the operating-repair history for two independent components of asystem of two components for any sample year and thecorresponding system state transition for this sampleyear. In this figure the duration of the up state is timeto failure (Ton) and the duration of the down state isthe time to repair (Toff). The sampling values for thesetwo parameters can be calculated by drawing a randomvariable following the exponential distribution suchthat:

UMupTon ln.= (8)

'ln. UMdwnToff = (9)

Where U and U are two uniformly distributed randomnumber sequences between [0,1] andMup is the meantime to failure andMdwn is the mean time to repair.

The procedure carried out in this paper to calculate thereliability indices using the adopted simulation methodis summarized as follows:

1. Obtain artificial up-down operating histories of allline sections by generating a random number andconvert this number into Tup and Tdown usingequations 8 and 9.2. Examine the effects of the operating state of eachline section on a load point state using the methoddiscussed in reference [12].

Figure 1: System state transition process3. Calculate the three basic load point indices caused

by each line section operating history using thefollowing equations:

=

up

iT

N (10)

N

Tr

d

i

= (11)

Where i refer to the line section and N is the number oftransitions between up and down states during the totalsample years.4. Evaluate the system indices using equations 4-7This procedure is implemented using MATLAB andthen utilized to assess the reliability of radialdistribution system. The analys is of the system understudy and the calculated reliability indices are

presented in the next section.


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IV. Case StudyThe structure of the distribution system under study isshown in figure 2. This system comprises 2 radialfeeders with 5 main sections, 6 laterals and 6 load

points. Each load point of the A, B and C load points isassumed to supply 100 customers and the other load

points D, E and F are supplying 80 customers giving a

total of 540 customer in the system. Two customercontrolled DG units located at load point C and Frespectively are running in parallel with the system andcan be used as an alternate supply. The component datarequired for the calculations of reliability indices areassumed to be as follows:Main feeder: 0.1 failures/km/year with 2 hours averagerepair time.Lateral: 0.3 failures/km/year with 1-hour averagerepair time.Switches are automatically operated and therefore theswitching time is negligible.

Figure 2. Radial distribution system under studyThe impact of the distributed generators DG1 andDG2 on the reliability of the system under study is

investigated as follows:First, the load point indices and the overall systemindices are evaluated with both the DG units are notavailable (Case I). The procedure described in the

previous section is used to perform this task.Then, the same analysis was performed with either ofthe two generators is running and the second isunavailable (Case II).Finally, the system analysis is performed with both thetwo DG units running in parallel with the system(Case III).

IV.1 Simulation Results

The simulation was performed for the highlightedstudy cases for 10,000 sample years. For each casestudy the load point indices and the system reliabilityindices are determined. The results obtained for thethree case studies are presented as follows:

Case I: Radial system with no DGThe load point indices are calculated for load points A,B, C, D, E and F. Figure 3 shows the convergence

process of the calculated load point indices of point Aof the system under study. The convergence process of

Monte Carlo simulation for the other load points issimilar to those of points B and D but with differentmagnitudes. Table1 summarizes the calculated load

points indices for all the system load points. Thesystem reliability indices for this case study are

presented in table 2.

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000

2.05

2.1

2.15

2.2

2.25

2.3

sample years

failures/year

0 2000 4000 6000 80001.4

1.5

1.6

1.7

1.8

1.9

2

2.1

smaple years

hours/year

A

UA

Figure 3: Reliability indices for load point A

Table 1: Load pint indices for the system understudy

Index A B C D E F

(failures/year) 2.25 2.1 1.8 1.8 1.8 1.8

r (hr/failure) 0.87 1.43 2 0.88 1.67 1.67

U (hr/year) 1.95 3 3.6 1.6 3 3

Table 2: System Reliability indices for case I

Index Case I

SAIFI 1.9389 (Interruptions/system customer

/year)CAIDI 1.3973 (hr/customer interrupted)

ASAI 0.9997

SAIDI 2.7090 (hr/system customer/year)

Case II: Radial system with one DGIn this case the DG connected to bus C is assumed to

be available. The average switching time for this DG is1 hour. Similar calculations were conducted to evaluatethe load pint indices and the system reliability indicesin this case. The obtained load points indices andsystem reliability indices are presented in tables 3 and4 respectively.

Table 3: Load pint indices for case study II

Index A B C D E F

(failures/year) 2.25 2.1 1.8 1.8 1.8 1.8

r (hr/failure) 0.87 1.14 1.33 0.88 1.67 1.67

U (hr/year) 1.95 2.4 2.4 1.6 3 3

Table 4: System Reliability indices for case II

Index Case II

SAIFI 1.9389 (Interruptions/system customer /year)

CAIDI 1.2250 (hr/customer interrupted)

ASAI 0.9997



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Case III: Radial system with two DGIn this case both the system available DG units areassumed to be available. The average switching timefor each DG is 1 hour. The calculated load pointsindices and system reliability indices for this case are

presented in tables 5 and 6 respectively.

Table 5: Load pint indices for case study IIIIndex A B C D E F

(failures/year) 2.25 2.1 1.8 1.8 1.8 1.8

r (hr/failure) 0.87 1.14 1.33 0.89 1.38 1.0

U (hr/year) 1.95 2.4 2.4 1.6 2.5 1.8

Table 6: System Reliability indices for case III

Index Case III

SAIFI 1.9389 (Interruptions/system customer

/year)

CAIDI 1.0950 (hr/customer interrupted)

ASAI 0.9997


IV.2 Discussion of the ResultsThe comparison between the results obtained for thedifferent case studies indicates that the load pointfailure rates are not affected by the availability of oneor two DG units in the studied system. The load pointfailure rates are dependent only on the main feederand laterals failure states and durations. In addition,the two system indices which are based on the load

points failure rate namely, system average interruptionfrequency index SAIFI and average serviceavailability index ASAI remained the same for the

three case studied.The main contribution of the DG to the systemreliability is the improvement in both the systemaverage interruption duration index (SAIDI) and thecustomer average interruption duration index(CAIDI). Although the frequency of interruption ofthe system is the same for all cases, the duration ofthese interruptions become shorter as the number ofDG units in the system increases. Both the customersand the utilities are benefiting from this improvementin the duration of interruptions.

V. ConclusionsIn this paper the impact of distributed generation onthe distribution system reliability was investigated.The state duration sampling technique and MonteCarlo simulation were implemented in this paper tocalculate the different reliability indices for a radialdistribution system with DG units running in parallelwith the system. The analysis of the studied systemhas proven that the presence of DG is enhancing thesystem reliability. This improvement in the system

reliability is reflected on the duration of interruptionsper customer per year and the duration of systeminterruptions per year.

VI. References

[1] T. Ackermann, G. Andersson and L. Soder

Distributed Generation: A definition Electric

Power System Research, Vol. 57, 2001 pp. 195-204.[2] R.H.Lasseter, Control of Distributed Resources,

Proceedings of Bulk Power Systems Dynamics and

Control IV, organized by IREP and National

Technical Umiversity of Athens, Santorini, Greece,

Aug, 1998, pp323-329.

[3] T. Ackermann, G. Andersson and L. Soder,

Electricity Market Regulations and their Impact on

distributed Generation, Proceeding of the

International Conference on electric Utility

Deregulation and Restructuring and Power

Technologies, City University, London, April 2000,

pp.608-613.

[4] P. P. Barker, Determining the Impact of Distributed

Generation on Power Systems: Part 1-Radial

Distribution Systems Proceedings of the IEEE PES

Summer meeting, 2000, pp1645-1656.

[5] J.R.Farls, R.D.Hartzel and L.G. Swanson, CaseStudies in Managing Distributed Assets

Proceedings of the IEEE PES Summer meeting,

2000, 1825-129.

[6] H.L.Willis, Energy Storage Opportunities Related

to Distributed Generation Proceedings of the IEEE

PES Summer meeting, pp.1517-1518.

[7] A. Girgis and S. Brahma, Effect of Distributed

Generation on Protective Device Coordination in

Distribution System IEEE., PES Summer meeting

2000, pp. 115-1119.[8] N.Hadjsaid, J. Canard and F. Dumas, Dispersed

Generation Increases the Complexity of Controllingand Maintaining the Distribution Systems IEEE

Computer Applications in Power, April 1999, pp.23-

28.

[9] J.E.Kim and J.S.Hwang, Isalanding Detection

Method of Distributed Generation Units Connected

to Power Distribution System Proceedings of the

IEEE Summer meetings 2001 pp 643-647.

[10] O.Usta and M.Redfem, Protection of Dispersed

Storage and Generation Units Against Islanding,

IEEE., PES summer meeting 2000, pp. 976-979.

[11] R.Billinton and W. Li, Reliability Assessment ofElectric Power Systems Using Monte Carlo

Methods Plenum Press, N.Y. 1994.

[12] R.Billinton and P.Wang, Teaching Distribution

System Reliability Evaluation Using Monte Carlo

Simulation IEEE Transactions on Power Systems,

Vol. 14, No.2 May 1999.


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VII. Biographies

Y.G.Hegazy received the B.Sc and M.Sc from Ain Shams

University, Cairo, Egypt and his Ph.D. from the university

of Waterloo, Waterloo, Ontario, Canada all in electrical

engineering in 1986, 1990 and 1996 respectively. At

present, he is an assistant professor in the department of

electrical power and machines at Ain Shams University,

Egypt and a visiting assistant professor to the University ofWaterloo. His interests include, power distribution systems,

power quality and probabilistic analysis of power systems.

M.M.A.Salama received the B.Sc and M.Sc from Cairo

University, Egypt and his Ph.D from the University of

Waterloo, Waterloo, Ontario, Canada all in electrical

engineering in 1971, 1973 and 1977 respectively. Presently,

he is a professor in the electrical and computer engineering

department at the university of Waterloo, Canada. Hisinterests include, the operation and control of electric

distribution systems, power quality analysis, and insulation

systems. He has consulted widely with government

agencies and electrical authority. Dr. Salama is a registered

professional engineer in the province of Ontario and he is afellow member of the institute of electrical and electronic

engineers IEEE.

A.Y.Chikhani received the B.Sc. degree in electrical

engineering from Cairo, University, Cairo, Egypt, in 1971.

He received the M.Sc. and Ph.D. degrees in the electrical

engineering from the University of Waterloo, Waterloo,

Ontario, Canada in 1976 and 1981,respectively. He joinedthe department of electrical engineering of the Royal

Military College in Kingston, Ontario in 1980 and became

head of the department in 1990. In 1994 he became dean of

the engineering at RMC. Dr. Chikhani ia a past chairman of

the IEEE Kingeston section. His interests include, the

operation and control of power distribution systems, powerquality analysis, cables and microprocessor applications to

power systems. Dr. Chikhani ia a registered professional

engineer in the province of Ontario.

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