Estimation of Radial Distribution Feeder Sections Average Failure Rate for Different Located Future Forecasted EV Charging Stations

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
© 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 422
Estimation of Radial Distribution Feeder Sections Average Failure Rate
for Different Located Future Forecasted EV Charging Stations.
V Swarna Rekha1, E Vidyasagar2
1Research Scholar, Department of Electrical Engineering, University College of Engineering, Osmania University,
India.
2Professor, Department of Electrical Engineering, University College of Engineering, Osmania University, India.
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Abstract - The number of Electrical vehicles increasing day by day and this increased load results in increase in the magnitude
of currents passing through different line sections of the distributionfeedercircuit. Dueto theincreaseofthemagnitude ofcurrent,
the resistive losses increase. As a result, the increase in temperature of the feeder section moderates its average failure rate and
reliability. In this paper the average failure rate of different line sections of a feeder is calculated for future increasedEV charging
stations for different possible locations and the total load of Electrical vehicles assumed to be charged from equal rated two
charging stations. The four possible locations of charging stations are considered based on the voltage sensitivity factor which
indicates strength of load buses. The average failure rate of all sections of the feeder are evaluated and compared for all four
different locations of charging stations to meet the future EV demand. This analysis is carried out on IEEE33 test bus system.
Key Words: Electrical Vehicle, Voltage Sensitivity Factor, Future Load Forecast, Average Failure rate.
1. INTRODUCTION
The analysis of a distribution system is an important area of activity, as distribution systems providethefinal link betweenthe
bulk power system and the consumers. Such a radial system needs adequate planning so that it can operate efficiently and
achieve the greatest possible incremental reliability[1],[2].Themaingoal ofanelectrical distributionnetwork'soperationisto
maintain an appropriate operational state of its elements to supply reliable power to its customers. The reliability of the
distribution network depends on the average failure rate and repair time of its feeder sections. The reliability of distribution
systems is measured in terms of load point indices and system indices. The load point reliability indices are (i) average failure
rate, (ii) average outage time, rs and (iii) average annual outage time, Us.. The indices are normally used to predict orassessthe
reliability of a distribution system. [1] The reliability of the distributionsystemalsodependsontheloadingofthebuses.Oneof
the loads on the distribution system is the transportation sector, i.e., integrating electrical vehicle (EV) charging stations.
The transportation industry is a significant contributor to CO2 emissions, causing global warming and climate change.EVsare
being introduced to reduce this emission, however the rising number of EVs has demanded the development of a sustainable
charging infrastructure. The installation of chargingstationsincreasesthe burdenontheelectrical system.Thechargingloadof
EV charging stations will decrease the distribution network's operational parameters, such as voltage stability, failure rate,
power loss, etc. A large number of studies reports the adverse effects of EV charging load on various distribution network
parameters. [3]-[9]. To evaluate the reliability of the future EV load, it is Forecasted using Holt’s model, and the impacts of EV
charging station load on the failure rate of the feeder section analysed on Fig-1 IEEE33 standard test.
Fig -1: IEEE 33 Standard Test Bus System.
Volume: 10 Issue: 02 | Feb 2023 www.irjet.net p-ISSN: 2395-0072

The rest of the paper is organised as follows: Section II presents a brief review of distribution network load point parameters
[1]. Section III reports the numerical analysis and discusses the key findings of this work. Section IV concludes the work.
2. METHODOLOGY
To calculate the voltages and currents of radial distribution system load flow analysis is considered by using
Backward/forward sweep method.
2.1 Load flow analysis.
Load flow analysis is used to study balanced and unbalanced power systems. Distribution systems are unbalanced. The
backward/forward sweep [10] is a classical algorithm that determines the bus voltages, currents passing through each line.
Backward sweep (BS) is the process of solving for the currents with the provided voltages, while forward sweep (FS) is the
process of solving for the voltages with the provided currents [11]. Fig-2. represents the radial distribution system with N
number of Nodes. Figure 2 depicts the algorithm for forward and reverse load flow.
Fig -2: Radial distribution system
Algorithm
Step 1: Initialize voltages at each node.
(1)
Step 2: Initialize Iteration Count, k=1
Step 3: Calculate Load current.
(2)
Load currents at each Node is calculated by using Equation (5)
Step 4: Backward Sweep used to calculate Branch currents.
(3)
Where In=Load current at nth node calculated at kth iteration. m=1,2,3…(N-1), n=2,3,4…N
Step 5: Forward sweep: Calculating Voltages at each Node so voltage at nth node.
(4)
Where calculated from step 4.
for all n =2,3,4…N, m=1,2,3…(N-1)

Step 6: Calculate the Error.
Error at any jth node is given by.
(5)
Step 7: Calculating Maximum error.
(6)
Step 8: Compare error values with Tolerance.
(7)
Then converge and print the results; else update the iteration count k = k+1 and go to step (3).
If the voltage difference is smaller than the stated tolerance, or 0.001, convergence can be achieved. At first, it is expected that
all nodes will have a flat voltage profile, or 1.0 p.u. The updated voltages at each node are used to repeatedly calculate the
branch currents. The IEEE33 radial bus network uses the Backward Forward sweep method to calculate voltages in the node
and currents in the branches [12]. This test network's line and branch data were taken from references [13]
This section explains the method of calculating the strength of buses using VSF, calculating the future EV load forecast using
Holt's model, and applying the future forecasted EV load to different possible cases of buses obtained from VSF. Further, the
new failure rate of feeder sections is calculated by considering the change in currents after applying load in different possible
cases.
2.2 Voltage sensitivity factor (VSF).
The voltage sensitivity factor (VSF) is the ratio of the change in the voltage at a particular bus to the change in the real power
load at the bus. Equation (1) gives the VSF for the nth bus during the kth interval.
(8)
Where and are the change in voltage and real power load at nth bus. The node voltages are calculated using Equation
(7). This index is used to determine the strength of bus. It is preferable if the VSF at a bus in each time interval is low. A high
VSF value shows that the voltage drops significantly even fora slightchangeinloading, whichsuggeststhe busisweak [14]The
loading margin of the system is defined as the loading for which all bus voltages fall within an acceptable range [14].
2.3 Holts Model for EV load forecast
Holt's two-parameter model [15], also known as linear exponential smoothing. It is a popularsmoothingmodel forforecasting
data with a trend. Holt's model consists of three distinct equations that collaborate to provide a final forecast. The basic
smoothing equation (5) adjusts the latest smoothed value directly for the trend of the previous period. Equation (6) is used to
update the trend over time, where the trend is given as the difference between the two mostrecentsmoothedvalues.Equation
(4) is then used to get the final forecast.
The Holt model employs two parameters, one for the overall smoothing Lt and another for the trend smoothing equation bt.
The equation for forecast
(9)
(10)
(11)

where Lt and bt represents estimate of the level and trend of time series respectively at time t.
Ft+1is the forecast for next year.
Assumptions
α and β are smoothing constants for level and trend respectively whose values lie on the interval between 0 to 1.
Initial estimates are needed for L1 and b1. Simple choices are L1 = Y1 and b1 = 0.
2.4 New Average failure rate of feeder sections.
The percentage change in average failure rate ( of a feeder section is assumed to be directly proportional to the
percentage change in current passing through that component due to addition of EV charging stations to meet the future EV
demands. The current in each branch is calculated using load flow analysis using Equation 3.
The new average failure rate is calculated as
(12)
Where is the increase in average failure rate due to addition of EV charging stations
3. CASE STUDY
This section explains how to find the nature of the bus using VSF. After calculating VSF, the strengths of the buses are
confirmed, and the EV load is forecasted and applied to the test bus by assuming different cases, for these different cases, the
average failure rate is calculated and analysed.
3.1 Finding the strength of the Bus using VSF.
VSF of each bus is calculated using (1) with loading factor 2, and thebusesarecategorisedintostrongest,strong,moderate,and
weak buses [16]. The VSF of bus 14 for loading factor 2 is 0.1163 and is higher in comparison with other buses. Thus, bus 14
was regarded as the weakest bus of the system. Similarly, the VSF of bus 2 was least making it the strongest bus of the system.
The VSF values also signify that bus 15 and bus 19 were the second weakest and second strongest bus respectively.
Representation for Table 1: *Strongest-SG, Strong-S, Moderate-M, Weak-W
Table -1: Strength of buses according to VSF values
Bus VSF Nature Bus VSF Nature Bus VSF Nature
- - 12 0.0986 M 23 0.0234 M
2 0.0034 SG 13 0.1064 M 24 0.0305 M
3 0.0196 S 14 0.1163 WK 25 0.0341 M
4 0.0284 S 15 0.1156 WK 26 0.0617 M
5 0.0372 S 16 0.1129 W 27 0.0647 M
6 0.0593 S 17 0.1112 W 28 0.0786 M
7 0.0636 M 18 0.1093 W 29 0.0886 M
8 0.0803 M 19 0.0039 SG 30 0.0930 M
9 0.0882 M 20 0.0076 S 31 0.0981 M
10 0.0956 M 21 0.0083 S 32 0.0993 M
11 0.0967 M 22 0.0089 S 33 0.0996 M

3.2 Forecasting EV load.
From the data about number of EVs given upto the year 2020 [17] and then the Holt’s Model is used for forecastingtheEVload
upto 2030 by assuming alpha(α) and beta (β) as 0.2 and 0.3 respectively. The results obtained (percentage of increased in
number of EV’s) is used in estimating the number of EV’s increased in case of IEEE33bustestsystemandvalidated asshownin
Table 2. The load for the IEEE 33-bus system is calculated for the years 2023, 2025, 2030 considering 5 kW for 2 wheeler, 30
kW for 3 wheeler, 50 kW for 4 wheeler and results are validated.
Representations for Table.2: *2 wheeler-(2-W), 3 wheeler -(3-W), 4 wheeler-(4-W)
Table -2: Forecasted 2,3 and 4-wheeler EV vehicles in Millions.
Year 2-W 3-W 4-W Year 2-W 3-W 4-W
2011 7.892 0.132 1.953 2021 18.093 0.645 3.386
2012 9.452 0.156 2.062 2022 18.966 0.692 3.469
2013 10.568 0.205 2.485 2023 19.838 0.740 3.551
2014 11.720 0.380 2.870 2024 20.711 0.788 3.633
2015 12.447 0.394 2.871 2025 21.584 0.835 3.715
2016 13.298 0.412 2.902 2026 22.457 0.883 3.798
2017 14.150 0.423 2.935 2027 23.330 0.931 3.880
2018 15.071 0.441 3.015 2028 24.203 0.978 3.962
2019 16.186 0.490 3.158 2029 25.076 1.026 4.044
2020 17.314 0.566 3.305 2030 36.796 1.074 4.126
The EV forecasted loads from Table.2 are applied equally on 2 assumed locations of the buses on the IEEE33 test system is
shown in Table 3.
Representation for Table 3: *Strongest-SG, Strong-S, Moderate-M, Weak-W
Table-3: Different locations of charging stations with forecasted EV load.
Case Bus No Nature Load (kW) (2023) Load (kW) (2025) Load (kW) (2030)
2
2 SG 1375 1466 1943
19 SG 1375 1466 1943
3 2 SG 1375 1466 1943
15 WK 1375 1466 1943
4 28 M 1375 1466 1943
8 M 1375 1466 1943
5 15 WK 1375 1466 1943
14 WK 1375 1466 1943
Table 4 represents the results of change in average failure rate of the feeder sections for the year 2023 after applying the load
on assumed cases 2 to 5. The values obtained in Table 4, Table 5, Table 6 are rounded to three decimals.
Representations for Table 4, Table 5, Table 6: *Case 1(Base case)-C1, Case 2-C2, Case 3-C3, Case 4-C4, Case 5-C5, *Section-(S)

Table-4: Average failure rate of feeder sections with increasing EV load in 2023 for different charging station
locations.
2023 2023
S C1 C2 C3 C4 C5 S C1 C2 C3 C4 C5
1 0.05 0.079 0.103 0.085 0.085 17 0.04 0.040 0.058 0.046 0.043
2 0.04 0.041 0.088 0.058 0.072 18 0.04 0.177 0.040 0.040 0.040
3 0.06 0.062 0.159 0.098 0.126 19 0.04 0.040 0.040 0.040 0.040
4 0.03 0.031 0.082 0.050 0.065 20 0.04 0.040 0.040 0.040 0.040
5 0.03 0.031 0.084 0.051 0.066 21 0.05 0.079 0.103 0.085 0.085
6 0.09 0.090 0.414 0.215 0.203 22 0.04 0.041 0.088 0.058 0.072
7 0.03 0.030 0.162 0.081 0.076 23 0.06 0.062 0.159 0.098 0.126
8 0.03 0.030 0.199 0.095 0.032 24 0.03 0.031 0.082 0.050 0.065
9 0.02 0.020 0.143 0.067 0.022 25 0.03 0.031 0.084 0.051 0.066
10 0.03 0.030 0.232 0.108 0.032 26 0.09 0.090 0.414 0.215 0.203
11 0.03 0.030 0.250 0.115 0.032 27 0.03 0.030 0.162 0.081 0.076
12 0.06 0.060 0.560 0.254 0.065 28 0.03 0.030 0.199 0.095 0.032
13 0.03 0.030 0.319 0.143 0.032 29 0.02 0.020 0.143 0.067 0.022
14 0.03 0.030 0.252 0.198 0.033 30 0.03 0.030 0.232 0.108 0.032
15 0.03 0.030 0.044 0.034 0.033 31 0.03 0.030 0.250 0.115 0.032
16 0.03 0.030 0.044 0.034 0.033 32 0.06 0.060 0.560 0.254 0.065
Table 5. presents the results for new average failure rates of the feeder sections for the cases 2 to 5 after applying the load for
the year 2025.
locations.
2025 2025
S C1 C2 C3 C4 C5 S C1 C2 C3 C4 C5
1 0.081 0.108 0.087 0.088 0.081 17 0.04 0.040 0.046 0.046 0.044
2 0.041 0.092 0.060 0.074 0.041 18 0.04 0.186 0.040 0.040 0.040
3 0.062 0.169 0.101 0.131 0.062 19 0.04 0.040 0.040 0.040 0.040
4 0.031 0.088 0.052 0.068 0.031 20 0.04 0.040 0.040 0.040 0.040
5 0.031 0.089 0.052 0.068 0.031 21 0.05 0.081 0.108 0.087 0.088
6 0.090 0.438 0.225 0.211 0.090 22 0.04 0.041 0.092 0.060 0.074
7 0.030 0.170 0.085 0.079 0.030 23 0.06 0.062 0.169 0.101 0.131
8 0.030 0.212 0.101 0.033 0.030 24 0.03 0.031 0.088 0.052 0.068
9 0.020 0.152 0.071 0.022 0.020 25 0.03 0.031 0.089 0.052 0.068
10 0.030 0.248 0.114 0.033 0.030 26 0.09 0.090 0.438 0.225 0.211
11 0.030 0.269 0.122 0.033 0.030 27 0.03 0.030 0.170 0.085 0.079
12 0.060 0.604 0.270 0.065 0.060 28 0.03 0.030 0.212 0.101 0.033

Table 6. gives the results for new average failure rate of feeder sections for the forecasted load of 2030 for cases 2 to 5.
locations.
2030 2030
S C1 C2 C3 C4 C5 S C1 C2 C3 C4 C5
1 0.05 0.091 0.110 0.102 0.103 17 0.04 0.040 0.062 0.050 0.045
2 0.04 0.041 0.094 0.069 0.086 18 0.04 0.235 0.040 0.040 0.040
3 0.06 0.062 0.174 0.119 0.155 19 0.04 0.040 0.040 0.040 0.040
4 0.03 0.031 0.090 0.061 0.080 20 0.04 0.040 0.040 0.040 0.040
5 0.03 0.031 0.091 0.062 0.082 21 0.05 0.091 0.110 0.102 0.103
6 0.09 0.090 0.460 0.284 0.249 22 0.04 0.041 0.094 0.069 0.086
7 0.03 0.030 0.181 0.109 0.094 23 0.06 0.062 0.174 0.119 0.155
8 0.03 0.030 0.224 0.132 0.034 24 0.03 0.031 0.090 0.061 0.080
9 0.02 0.020 0.160 0.094 0.022 25 0.03 0.031 0.091 0.062 0.082
10 0.03 0.030 0.261 0.151 0.034 26 0.09 0.090 0.460 0.284 0.249
11 0.03 0.030 0.282 0.162 0.034 27 0.03 0.030 0.181 0.109 0.094
12 0.06 0.060 0.631 0.361 0.067 28 0.03 0.030 0.224 0.132 0.034
13 0.03 0.030 0.360 0.204 0.034 29 0.02 0.020 0.160 0.094 0.022
14 0.03 0.030 0.283 0.289 0.034 30 0.03 0.030 0.261 0.151 0.034
15 0.03 0.030 0.047 0.037 0.034 31 0.03 0.030 0.282 0.162 0.034
16 0.03 0.030 0.047 0.037 0.034 32 0.06 0.060 0.631 0.361 0.067
From Table.4, Table.5, Table.6 the percentage change of failure rate for theyears2023,2025,2030 withrespecttothebasecase
is calculated. The effected feeder sections for cases 2 to 5 after application of EV load are shown in Table.7.
Table-7: Average failure rate affected greater than 20%, greater than 50%, greater than 100% on sections.
Case > 20% No. of
sections
> 50% No. of
sections
100% No. of
sections
2 1, 18,30,31 4 18 1 18 1
3 1 to 17, 30,31 19 1 to 14 14 1, 4 to 14 11
4 1 to 7, 25 to 27, 30,31 12 1 to 7, 25 to 27, 31 11 25,26,27 3
5 1 to 17, 25 to 31 23 1 to 17, 31 18 1 to 14 14
13 0.030 0.346 0.152 0.033 0.030 29 0.02 0.020 0.152 0.071 0.022
14 0.030 0.269 0.211 0.033 0.030 30 0.03 0.030 0.248 0.114 0.033
15 0.030 0.035 0.035 0.033 0.030 31 0.03 0.030 0.269 0.122 0.033
16 0.030 0.035 0.035 0.033 0.030 32 0.06 0.060 0.604 0.270 0.065

4. CONCLUSION
Average failure rate is the basic index used to calculate the reliability parameter SAIFI and is expressedastheaveragenumber
of outages experienced by a system customer during a year (or time period under study). Fromtable7,theaveragefailurerate
of the sections for the year 2030 increased. It is noted that case 5 is the most affected case, where 23 feeder sections are
affecting greater than 20%, 18 feeder sections are affecting greater than 20%, and 14 feedersectionsareaffectinggreaterthan
100% of the average failure rate. It is mandatory that the engineers need to compensate the increase in current in the
distribution feeder sections so that the increase in losses compensates and the effect on the failure rate of the components
corresponding to that section is also reduces. This paper concludes that itisnecessarytoconcentrateonweakersectionsof the
distribution system and observes that choosing proper locations for placing charging stations is necessary to maintain
reliability.
Future work.
The current passing through the lines are reduced by placing distributed generators, reconfiguring the network, optimal
placement of capacitors etc., thereby the average failure rate of the distribution system reduces.
REFERENCES
[1] Roy Billinton, Ronald N.Allan“Reliabilityevaluationof powersystem”-Second edition,publishedby PlenumPress,NewYork
in (1986).
[2]Jeenia Bhadra , Tapan Kumar Chattopadhyay “Analysis of distribution network by reliability indices”. International
Conference on Energy, Power and Environment: Towards Sustainable Growth (ICEPE), shillong , India,(2015).
[3] Wang, Z.; Yang, L. “Delinking indicators on regional industry development and carbon emissions”,:Beijing–Tianjin–Hebei
economic band case. Ecol. Indic.(2015), 48, 41–48.
[4] Wang, Q.; Rongrong, L.; Rui, J. “Decoupling and Decomposition Analysis of Carbon Emissions from Industry: A Case Study
from China”. Sustainability (2016), 10, 1059–1076.
[5] Blesl, M.; Das, A.; Fahl, U.; Remme, U.” Role of energy efficiency standards in reducing CO2 emissions in Germany”: An
assessment with TIMES. Energy Policy (2007), 35, 772–785.
[6] Geske, M.; Komarnicki, P.; Stötzer, M.; Styczynski, Z.A. “Modeling and simulation of electric car penetration in the
distribution power system”,—Case study. In Proceedings of the International Symposium on Modern Electric PowerSystems,
Wroclaw, Poland, 1 September (2011); pp. 1–6.
[7] Zhang, C.; Chen, C.; Sun, J.; Zheng, P.; Lin, X.; Bo, Z. “Impacts of electric vehicles on the transient voltage stability of
distribution network and the study of improvement measures”. In Proceedings of the IEEE Asia Pacific Power and Energy
Engineering Conference (APPEEC), Hong Kong, China, 7–10, December (2014); pp. 1–6.
[8] Staats, P.T.; Grady, W.M.; Arapostathis, A.; Thallam, R.S. “A statistical analysisoftheeffectof electricvehiclebatterycharging
on distribution system harmonic voltages”. IEEE Trans. Power Deliv. (1998), 13, 640–646.
[9] Basu, M.; Gaughan, K.; Coyle, E. “Harmonic distortioncausedbyEVbatterychargers inthedistributionsystemsnetwork and
its remedy”. In Proceedings of the 39th International Universities Power Engineering Conference, Bristol, UK, 6–8 September
(2004); pp. 1–6.
[10] “NPTEL’s_load flow analysis-Backward/forward sweep”, Electrical Distribution SystemAnalysis byDr.Ganesh Kumbhar,
Department of Electrical Engineering, IIT Roorkee or
https://www.youtube.com/watch?v=kxm0Prghn64&ab_channel=IITRoorkee. July, (2018).
[11] Dharmakeerthi, C.H.; Mithulananthan, N.; Saha, T.K. “Impact of electric vehicle fast charging on power system voltage
stability”. Int. J. Electr. Power Energy Syst. (2014), 57, 241– 249.
[12] Ul-Haq,A.;Cecati,C.;Strunz,K.;Abbasi,E.“Impactofelectricvehiclechargingonvoltage unbalancein anurbandistribution
network”. Intell. Indus. Syst. (2015), 1, 51–60.

[13]J.A.Michline Rupa, S. Ganesh “power flow analysis for radial distribution system using forward backward method.”World
Academy of Science, Engineering and Technology International Journal of Electrical and ComputerEngineering, (2014)Vol:8,
No:10.
[14] Rahman, M.M.; Barua, S.; Zohora, S.T.; Hasan, K.; Aziz, T. “Voltage sensitivity-basedsiteselectionforPHEVchargingstation
in commercial distribution system”. In Proceedings of the Asia Pacific Power and Energy Engineering Conference, HongKong,
China, 8–11 December (2013); pp. 1–6.
[15] “NPTEL’s lecture on” Holt’s Model for forecasting” by Prof. G. Srinivasan, Department of ManagementStudies,IITMadras,
can be found at Swayam Central or YouTube, https://www.youtube.com/watch?v=e1yUVLKhcko&list=WL&index=36.
[16] Sanchari Deb , Kari Tammi , Karuna Kalita I and Pinakeshwar Mahanta “Impact of Electric Vehicle Charging Station
Load on Distribution Network” Energies 2018, 11(1), 178; https://doi.org/10.3390/en11010178.
[17] Deutshce Gesellschaft fur ,“Status quo analysis of various segments of electric mobility and low carbon passenger
road transport in India” conducted by Deutshce Gesellschaft fur Internationale Zusammenarbeit (GIZ) GmbH, Federal
Ministry for the Environment, Nature Conservation and Nuclear Safety, and NITI Ayog, India.
BIOGRAPHIES
V Swarna Rekha Research Scholar in
Electrical & Electronics Engineering
Department, University college of
engineering, Osmania University, Hyderabad.
He has done B.tech. in Electrical Engineering
from DVR college of Engineering and
Technology (JNTU(H)). and M. E. from
University College of Engineering and
Technology,Osmania University.Her research
includes Power Quality, Distribution System.
She can be contacted at email:
swarna.vaddemoni@gmail.com
Dr. E Vidyasagar currently working Professor
in Electrical & Electronics Engineering
Department, University college of
engineering, Osmania University, Hyderabad.
He has done his Bachelor’s Degree in
Electrical and Electronics Engineering.
Master’s Degree in Electrical Engineeringand
Doctorate Degree from J.N.T. University,
Hyderabad. His main research directions
include Distribution Reliability, Power
Quality, Deregulated Power Systems, Smart
Grid, DistributionSystem. He canbecontacted
at email: vidyasagar.e@uceou.edu
hor
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