## Journal of Green Engineering

Vol: 8    Issue: 3

Published In:   July 2018

### Assessment and Enhancement of Distribution System Reliability by Renewable Energy Sources and Energy Storage

Article No: 2    Page: 219-262    doi: https://doi.org/10.13052/jge1904-4720.832

 1 2 3 4 5 6 7 8 9 10

Assessment and Enhancement of Distribution System Reliability by Renewable Energy Sources and Energy Storage

Yishak Kifle1, Baseem Khan1,* and Pawan Singh2

1School of Electrical and computer Engineering, Hawassa University Institute of Technology, Hawassa, Ethiopia

2School of Informatics, Hawassa University Institute of Technology, Hawassa, Ethiopia

E-mail: yishakkifle1985@gmail.com; baseem.khan04@gmail.com; pawansingh3@yahoo.com

Corresponding Author

Received 21 January 2018; Accepted 12 June 2018;
Publication 19 July 2018

## Abstract

Distribution reliability mainly relates to the equipment outages and customer interruptions. In this work, authors attempt to identify the causes of power interruptions and customer’s dissatisfaction. Additionally, the authors discuss design, maintenance, reliability and operation of Hawassa distribution feeder number 10, Ethiopia. Reliability worth is highly significant in power system planning and operation. A distributed generation (DG) ensures reliability improvement and it is used to increase the reliability worth. Hence in this work authors present the study of a radial distribution system and illustrate the impact of placing DG (solar photovoltaic (PV), wind turbine (WT)) and battery energy storage on the reliability worth. The reliability improvement is measured by different reliability indices that include SAIDI, CAIDI, EENS and ASAI. The analyzed and calculated distribution reliability indices values have been compared with standard benchmark values. Additionally, authors evaluate reliability of distribution networks by including islanded micro grid cases. The network includes two types of DG sources (PV, WT) and energy storage as back up. The distributed generators contribute to supply fraction of the load during grid-connected mode, but supply the heavy loaded area during islanded micro grid operation. The studies performed are supported with the Power Management System Software ETAP.

## Abbreviations

 AENS Average Energy Not Supplied Index ANSI American National Standards Institute ASAI Average Service Availability Index ASUI Average Service Unavailability Index BES Battery Energy Storage CAIDI Customer Average Interruption Duration Index CAIFI Customer Average Interruption Frequency Index DG Distributed Generation ENS Energy not supplied HRC High Rupturing Capacity IEC International Electro technical Commission kVAR Kilovolt Ampere Reactive kVA Kilovolt Ampere MW Mega watt MVA Mega volt ampere PV Photo Voltaic SAIDI System Average Interruption Duration Index SAIFI System Average Interruption Frequency Index SOL System over load WTG Wing Turbine Generator

## Keywords

• Renewable energy sources
• Battery energy storage
• Reliability indices
• Electrical Transient Analyzer Program (ETAP)

## 1 Introduction

In recent years, a number of studies are conducted in the field of power systems engineering with a great interest to implement renewable energy resources in power networks. The interest is motivated by environmental issues and rising fossil fuel prices. Since greenhouse gas emissions are the main cause of global warming, using technologies that do not produce green house gases emission would naturally eliminate their effects. Rising fossil fuel prices made the renewable resources more competitive and encourage technologies to compete in the power market. For example, plug-in hybrid electric vehicles that are predicted to reshape the transportation future could interact with power grids as a means of energy storage. Alternative energy can be used to supply these vehicles, and this would reduce the dependency on fossil fuels. Distributed energy sources are such as wind, solar, geothermal etc.

Power quality is a great concern in electric distribution system. Customers require high quality service for more sensitive electrical and electronic equipments. The effectiveness of a power distribution system is measured in terms of efficiency, reliability and service quality. Currently, the Ethiopian Electric Power Corporation has 400 kV, 230 kV and 132 kV as primary transmission systems and 66 kV, 45 kV as sub transmission system. Further, 33 kV and 15 kV as distribution system. For 66 kV and 45 kV substations, the power transformers of various ratings like 25/12/6.3/3 MVA are installed to step down the voltage at 15 kV for distribution system. Mostly, at 33 kV and 15 kV overhead conductors are used for supplying distribution transformers. The voltage is then further stepped down by distribution transformers for domestic users. In the context of Ethiopia, electric power interruption has become a daily event. Also power interruption occurs at both low and medium voltage distribution level. The voltage fluctuation at the residential loads is a main reason for early failure of equipment, blackening of bulbs and decreased efficiency of domestic appliances.

Power system reliability assessment is primarily focused on the analysis of the healthy and failure states of a power system. Power system reliability can be subdivided in to two classes. Adequacy assessment takes into account the computation of sufficient facilities within the systems, satisfy the customer load and static conditions in the power system. Power system security has the goal to respond to disturbance arising within the system and therefore, deals with the dynamic conditions in the system. A power system is a complex network, highly integrated and very large. The reliability evaluation of the entire configuration at a time is complex, if it considers the power systems as an entire entity. Despite the evaluation complexity, the need for reliability assessment is ever increasing and more utilities are investing time in reliability analysis. Thus, to reduce the complexity of an overall power system, there are methodologies that divide whole power system into three functional zones. The first is generation facility and its ability to satisfy the system demand; the second is composite generation and transmission systems and its ability to deliver energy to the bulk power points; finally the third refers to the complete system including the distribution to satisfy the individual costumer’s demand [1].

To meet customer demand, the power utility should be evolved and the distribution system should be upgraded, operated and maintained accordingly [2]. Energy losses in distribution systems are normally estimated rather than measured based on some thumb rule. In [3], a joint investigation is undertaken in association with a local utility to study this issue. Based on the data collected from feeders, true losses in primary and secondary feeders are obtained. The measured values by two new schemes for estimating losses are used to highlight the reliability of the system. In [2], authors provide a framework of a predictive, condition-based, and cost effective maintenance optimization program for transmission and distribution systems. In [4], the results of a power quality survey in a distribution system are presented and discussed. Further, power quality indices are calculated, which are based on IEEE and IEC Standards. In [5], the authors discussed experimental design for the analysis of electrical power distribution systems, which is useful to construct and empirically verify the qualitative model of a distribution system. The reliability benefits are appreciated from a costumer point of view. A properly located, installed and operated DG improves the reliability of energy supply. It is essential to those places, where interruption of service is unacceptable, economy, health and safety is impacted [6]. Each customer decides his own reliability.

Fang et al. [7] introduced Sequential Monte Carlo simulation method for assessing reliability of micro-grid. Moreover for simulating the hourly wind speed of a site a Weibull distribution wind speed model is developed. Billinton et al. [8] presented the model for evaluating overall power system reliability by using a test system. Furthermore extension of the existing system is done by introducing sub-transmission and distribution lines. To enhance the advantages of battery storage system in distribution feeder, Nagarajan et al. [9] developed a generalized framework for strategic placement of battery energy system. For that purpose convex optimization problem is specifically developed. Bass et al. [10] developed a methodology to determine the capacities of BES for accommodating a PV penetration inside a distribution feeder. To investigate the role of municipal parking lot for enhancing the distribution system reliability during outages, Farzin et al. [11] incorporated vehicle-to-grid (V2G) programs. These parking lots treated as distributed storage system and are probabilistically modeled for different reliability studies. Chen et al. [12] discussed a group of methodologies such as Markov model based analytical approach and Monte Carlo simulation approach to assess and verify the reliability of mobile BESS in distribution system, respectively. Firuzabad et al. [13] investigated the impact of DG installations on distribution system reliability. For this purpose an analytical probabilistic approach with reliability model is proposed. Yun et al. [14] proposed the different methods of evaluating distribution system reliability by considering the momentary interruptions. Falaghi et al. [15] computed the various reliability indices such as SAIFI, SAIDI and CAIDI for DG’s reliability assessment. Borges et al. [16] presented a method to evaluate the effect of DG placement on voltage profile, reliability and system losses of distribution system. Adefarati et al. [17] proposed a Markov process based reliability assessment model of the distribution system that incorporated PV, BES and WTG. To measure the effects of placement of BESs, network switching and reinforcement on renewable power interaction in distribution system, Santos et al. [18] presented a novel methodology. A multi-objective stochastic model based on mixed integer linear programming is derived. To find the impact of BES, WTG, DG and PV on distribution system reliability assessment, Adefarati et al. [19] presented an analytical method. Kjolle et al. [20] proposed a specific reliability model for radially operated distribution systems. For reliability calculation analytical approach is adopted, which connected component failure rates to load point outages. Andoni et al. [21] discussed various curtailment rules generally utilized in renewable power project in United Kingdom. The impacts of these rules on the investment of renewal power production are also discussed. Moreover a novel curtailment rule is proposed for fair allocation of curtailments among different generators. Haddadian et al. [22] evaluated the synchronization between electric vehicles and renewable energy sources to alleviate imbalance of energy. Further impacts of such integrations for social and environmental sustainability are also investigated. Ghatikar et al. [23] presented a cost effective solution technique for leveraging the existing DER infrastructure and technology as well as cost optimization.

In this paper authors evaluate the reliability of Hawassa distribution feeder line 10. Further designing of Hawassa distribution feeder line is done with distributed energy sources like solar, wind and battery storage. Authors also examine the impact of grid connected distributed generation sources on existing distribution network reliability assessment for customers power delivery. Further, measures for enhancing the reliability of Hawassa distribution feeder line 10 are presented. Section 2 described the various distributed generation models. In Section 3, a case study of Hawassa distribution feeder 10 is discussed. Section 4 provided reliability analysis of the Hawassa distribution system feeder 10 in the base case environment. Section 5 presented reliability analysis with various solutions of improving the reliability of distribution feeder 10. Section 6 provided the results and discussion followed by the conclusion.

## 2 Distributed Generation Model

In this work, the reliability of distribution system containing distributed energy resources such as wind and solar energy is assessed. It is well known fact that solar irradiance and wind speeds are both intermittent; hence the output powers of PV and WT systems are not deterministic. That brings up the need for a stochastic model to simulate PV and WT outputs. The stochastic model is a simulation-based technique to describe a non-deterministic behavior and the randomness of the system. The probability distribution, therefore, can be used to predict the output power of PV and WT. In order to find statistical data of the wind speed and solar insolation, meteorological data of a variety of weather conditions at one location should be measured.

### 2.1 Modeling of PV Power Output

The building block of PV arrays is the solar cell, which is basically a p-n junction that directly converts solar energy into electricity. It has an equivalent circuit as shown in Figure 1:

Figure 1 Equivalent circuit of a PV cell.

The current source Iph represents the cell photo current; Rj is used to represent the non-linear impedance of the p-n junction; Rsh and Rs are used to represent the intrinsic series and shunt resistance of the cell, respectively. Usually, the value of Rsh is very large and that of is very small, hence they may be neglected to simplify the analysis. Irs is the cell reverse saturation current, which is assumed zero. PV cells are grouped in larger units called PV modules, which are further interconnected in series-parallel configuration to form PV arrays or PV generators [24]. The PV mathematical model used to simplify our PV array is represented by the Equations (1) to (5):

(Assuming Rs = 0, Rsh = ∞ and Irs = 0, for simplifying the study) and applying Kirchhoff law; The electrical powers generated by a PV array consist of modules is computed using the following equations:

$TC=TA+(Noct−20)S80 (1)I=Iph=[Isc+Ki(Tc−25)]S100 (2)V=VOC−KvTc (3)FF=Vmpp∗ImppVocIsc (4)Pout=N∗FF∗I=ns∗np∗FF∗V∗I (5)$

Where, I is the PV array output current, V is the PV array output voltage, ns, np is the number of cells in series and in parallel, N is number of module, TC,TA are the cell effective and ambient temperatures in C, respectively, Isc is the cell short-circuit current, V oc is the open circuit voltage, Impp, Vmpp are the current and voltage at maximum power point, respectively, Ki is the short circuit current temperature coefficient, Kv is the open circuit voltage temperature coefficient, S is the solar radiation in mW/cm2 and FF is the fill factor.

### 2.2 Modeling of WT Power Output

The output power of a wind turbine depends on wind velocity. If the wind velocity is below the cut-in speed, there is insufficient wind energy to generate power, and the wind turbine would be turned off. If the wind velocity is between the cut-in and rated speed, the output power would be variable. If the wind velocity is between the rated and cut-on speed, the output power would be constant. In case, if wind speed goes above cut-on speed, the wind turbine would be turned off because it exceeds the mechanical safety limit. The relationship between the output power and wind velocity is shown in Figure 2, and to model the wind system performance, its power curve must be formulated in the form of polynomials as given below [25]. The output of wind turbine generator is provided by the Equation (6).

Figure 2 Output characteristics curve of wind turbine.

$PWTG={ 0A+BVt3Pr00≤Vt≤VciVci≤Vt≤VrVrVco (6)$

Where, PWTG and Vt represent the output of wind turbine and actual wind velocity, respectively, at time t; Vci, Vr and Vco represent the cut-in wind velocity, rated wind velocity and cut-off wind velocity, respectively; Pr represents the rated power of the wind turbine. and are the parameters, which can be calculated by the Equations (7) and (8):

$A=Pr∗Vci3Vco3−Vr3 (7)B=PrVr3−Vco3 (8)$

Although wind is random and intermittent, distribution of wind velocity in most districts still follows some rules and certain distributions is adopted to represent the probability distribution of wind velocity. Consequently, a probabilistic method should be implemented to simulate the uncertainty of the wind speed. Statistical data has shown that probability distribution of wind speed follows the Weibull distribution. It is considered to be a simple function suitable to describe the wind [26]; it’s a single-peak and two-parameter function, whose distribution and probability density functions are expressed by Weibull distribution as shown in Equation (9) [19]:

$f(Vt)=kc(Vtc)k−1exp[ −(Vtc)k ] (9)$

Where, Vt represents wind speed, C is Scale parameter, and K represents Shape parameter. Both parameters are calculated from the average wind velocity (μ) and the standard deviation (σ), as shown in Equations (10) and (11).

$k=(σμ)−1.086 (10)c=μΓ​(1+1k) (11)$

Since the Weibull probability function of the wind speed is very much sensitive to any change in c and k, statistical data of the wind speed at the desired location should be collected for several years.

### 2.3 Battery Storage System Model

Storage device is usually configured with intermittent generations like wind power and photovoltaic in order to minimize the fluctuation of these DGs’ output, to improve the power quality and power supply reliability in micro grid. In islanded mode, when DGs’ output is greater than load, residual energy is stored in storage device; when DGs’ output is less than load, the stored energy is released to supply customers. Assume that the combined DGs and storage energy system is autonomous and controllable; neglecting the influence of the time constant of power regulation. It is considered that output of the combined DGs and battery energy storage system is in equilibrium with load, all the time. DGs’ output is insufficient when the wind or sunlight is not/less available. At that time the released energy by storage device is greater than the stored energy and then the operation time of storage system is constrained by its storage capacity. In addition, battery storage is associated with the distributed energy resources to supply the load when the main source is not available. In this work, a generic battery storage system is developed, which serves the main purpose of this study.

The total system energy interrupted capacity and the converter capacity are 2400 kWh and 400 kW, respectively for 24 hours. Figure 3 shows the hourly charge and discharge profile of the battery storage. Because there is no output power of the PV during the night, the battery storage is charged by the main grid. During interruptions in the power network, the load is drawn up to 2400 kWh energy for 6 hours, if the battery is fully charged. Batteries used in all solar systems are sized in 0 Ampere hours, under the standard test conditions (Temp: 25C). Equation (12) represents the capacity of battery bank for a specified discharge rate, in ampere hours.

Figure 3 400 kW PV hourly charge and discharge profile of the storage (P>0 charging and P<0 discharging).

$Cx=EtotVdc∗GftDODmax (12)$

Where, Cx represents the battery capacity, for a specified discharge rate in ampere hours, provide the total energy in watt hours, Etot which to be supplied by battery bank during grid failure, Gft is the number of days that battery bank needs to supply during grid failure DODmax and represents the design for maximum depth of discharge.

## 3 Case Study

### 3.1 Meteorological Data

Table 1 presents the calculated and measured values of basic parameters for Hawassa city.

Table 2 presents the wind speed at different height.

Table 1 Calculated and measured values of basic parameters for Hawassa city

 Month HoAverage Declination Angle (δ) SunsetHourAngle(ωS) MonthlyAverageDailyMaximumBrightSunshine (S) Average Monthly Day Length (So) Estimated Monthly Average Irradiation (Hest) Measured Monthly Average Irradiation,Hmeas. (NASA) Jan. 9.87 -17.78 87.73 10.15 11.698 6.59 6.02 Feb. 9.72 -8.67 88.92 9.76 11.856 6.33 6.41 Mar. 9.52 3.62 90.45 9.11 12.0596 5.93 6.35 Apr. 9.26 14.59 91.84 8.05 12.2452 5.36 6.04 May. 8.96 21.90 92.84 6.63 12.379 4.64 5.95 June. 8.94 23.19 93.03 6.22 12.404 4.45 5.42 July. 8.99 18.17 93.03 6.58 12.404 4.62 4.83 Aug. 8.92 8.11 91 7.18 12.13 4.98 5.01 Sep. 9.12 -3.82 89.53 7.11 11.94 5.00 5.64 Oct. 9.16 -15.06 88.09 7.29 11.747 5.15 6.04 Nov. 9.48 -21.97 87.15 8.65 11.6198 5.87 6.25 Dec. 9.48 -23.09 86.99 8.65 11.5983 5.88 6.10 Aver. 7.95 5.4 5.84

Figure 4 Monthly average global irradiation of Hawassa city.

Table 2 Wind speed at different height

 Monthly averaged wind speed at 50,100,150 and 300m above the surface of the Earth(m/s) Vegetation type “Airport”; flat rough grass Lat. 7.03 Lon.38.29 Jan. Feb. Mar. Apr. May Jun. Jul. Aug. Sep. Oct. Nov. Dec. Annual Average 50m 4.49 4.05 3.84 3.93 3.72 3.67 3.19 2.96 3.05 3.66 4.20 4.47 3.76 100m 4.98 4.50 4.26 4.36 4.12 4.07 3.53 3.28 3.38 4.06 4.66 4.95 4.18 150m 5.29 4.78 4.52 4.63 4.38 4.32 3.76 3.49 3.59 4.31 4.95 5.27 4.44 300m 5.87 5.31 5.02 5.14 4.86 4.80 4.17 3.87 3.99 4.78 5.49 5.84 4.93

### 3.2 Component Selection

All solar systems are designed to solve a particular power problem; the grid connected system with a battery backup has two main functions:

• To supply power to all the loads when the grid has failed for a specified period
• To supply AC power to the national grid when there is excess power

The selected configuration for grid connected PV systems with battery backup has the charge controller and the inverter as a unit; Figure 5 shows the block diagram of a system.

Figure 5 System configuration with charge controller and the inverter.

Block diagram shows a design configuration for supply and storage system. When the demand is high, the system will deliver energy from inverter current. But when the demand is low, the battery will store energy by solar panel through charge controller. The stored energy is used as backup for gloomy day or at night [27].

### 3.3 Module Selection

PV module selection criteria are as follows:

• The performance warranty in case of any problems
• Module replacement ease
• Compliance with natural electrical and building codes
• Manual should be available to see the quality and characteristics of module Table 3 describes the typical electric characteristics of generic poly 250 W module.

Table 3: Typical Electric Characteristics of Generic poly 250 W Module

Table 3 Typical electric characteristics of generic poly 250 W module

 Type Polycrystalline Power (max) 250 W Voltage @ Max. power point 30 V Current @ MPP 8.33 A Voc (open circuit voltage) 36.4 V Isc (Short circuit current) 8.63 A Conversion Efficiency(per module area) 17 % Area (dimension) 1.627 m2 Nominal voltage 24 V Max. system voltage 600V DC Max. series fuse rating 750V DC Temperature derating factor 0.06%/∘C

### 3.4 Inverter Selection

Table 4 presents the electrical characteristics of IG PLUS 150 V-3 inverter.

Table 4 Electrical characteristics of IG PLUS 150 V-3 inverter

 Continuous output power 400 kW Weighted efficiency(CEC) 95.5 % Maximum DC input voltage 800 V DC peak power tracking range (Vmpp min – Vmpp max) 500–750 V DC max. current (A) 700A AC nominal voltage (V) 400 V AC frequency (Hz) 50 Hz

Based on the data available, the characteristics of PV, WT and battery storage system are calculated in Tables 5, 6, 7, respectively.

Table 5 Calculated PV generator characteristics

 S.no Nameof RDG Prated [kW] Numberof Module InverterVmppmax ArrayMppDCVoltage ArraymppDCCurrent Number ofModule inSeries NumberofModule inParallel Actual Power Output Pout, [kW] 1 PV 400 1600 600 600 492 20 80 228.5

Table 6 Calculated storage battery basic parameters data

 S.no Model of Battery Total Number of Cells Connected Total Energy Supplied to the Grid Battery Capacity 1 JC DYNASTY 300 400*6 kWh = 2400 kWh 1428.6Ah

Table 7 Calculated wind turbine generator wind characteristics data

 S.no Nameof RDG Prated [kW] Average Wind speed at 100 m Vci [m/s] Vr [m/s] Vco [m/s] Output Power 1 WGT1 100 4.18 2 m/s 11m/s 50m/s 65.53

## 4 Reliability Analysis of the Hawassa Distribution System Feeder 10

### 4.1 Existing Structure of Hawassa Distribution System Feeder 10

The area chosen for the current study is Hawassa substation distribution system feeder 10 line. The power distribution has only one substation, with 132kV/33kV primary distribution voltage level (medium voltage), as shown in Figure 6.

Figure 6 Single line diagram of existing structure of Hawassa substation 33 kV feeder 10 line.

### 4.2 Data Collected from Hawassa Substation 33 kV Feeder Line

Hawassa town has only one substation, 13 outgoing feeders out of which 3 are 33 kV, around 50 MW peak load, and approximately 29,906 customers (i.e. industrial, commercial and residential). The collected data for 33 kV feeder line of Hawassa Substation are shown in Table 8. The frequency of interruption and duration of interruption for Hawassa 33kV distribution system for 11 months are analyzed and interpreted as shown in Figures 7 and 8, respectively. The collected data is recorded for eleven months.

Table 8 Hawassa substation of 33 kV lines data

 SubstationName/Feeder Name 33kV PowerTransformerCapacity Line MV(33kV) Transformer Qty.(kVA) Location/Name Hawassa/F9 2 2*1250 Hawassa BGI Hawassa/F10 16/8/8MVA 120.7 1*25,8*50,6*100, 1*200,1*500 Arbegona,kokosa,boro, Hududa,Bokore so on Hawassa/F11 7 5*630,1*500 Hawassa university

Figure 7 Frequency of Interruption for Hawassa sub-station 33kV feeder line 10.

Figure 8 Duration of interruption for Hawassa sub-station 33kV feeder line 10.

From the analysis, it is observed that majority (80.30%) of the faults in Hawassa substation feeder 10 distribution network are due to short circuit, open circuit and earth faults. The remaining faults are due to black out, operation and system over load (when generated power is below the total demand other than black out), as shown in Table 9.

Table 9 Summarizing the interruption data on 33kV feeder line 10

 No. Type of the Fault Frequency Duration Percentage of Fault (%) 1 Distribution fault (open, short, earth) 269 515.05 80.30 2 Line and transformer Overload fault 14 53.14 4.18 3 Generation failure or system overload (Shading) 52 104.01 15.52

### 4.3 Reliability Evaluation of Hawassa 33kV (feeder 10) Distribution System

One objective of this work is to provide a more acceptable method for determining distribution network reliability. This part of the work uses IEEE 1366 indices to evaluate the reliability indices of Hawassa 33 kV feeder 10. The availability of power for customers from this substation is calculated on the medium voltage side of customer transformers (33kV). The customer-oriented indices (SAIFI, SAIDI, CAIDI, ASAI, ASUI) for Hawassa substation feeder 10 is calculated using ETAP (Electrical Transient Analyzer Program) software [28].

### 4.4 Algorithm for Reliability Indices Evaluation Using ETAP

#### 4.4.1 Single line diagram

The starting point of any power-flow problem is the development of a single-line diagram of the power system, from which computer solutions is obtained. The single line diagram of the 33kV Hawassa substation feeder line 10 is drawn using the ETAP Power Station platform for this study. All the organized data is fed to a single-line diagram. Partial view of the single-line diagram is given in Figure 9, for both editing and running modes of ETAP.

Figure 9 Single line diagram of Hawassa substation 33kV feeder 10 distribution system.

#### 4.4.2 Inputs data based on the type of fault

##### 4.4.2.1 Distribution line fault (open circuit, short circuit, earth fault)

Table 10 presents the reliability indices related to distribution line fault interruption.

Table 10 Distribution line fault interruption reliability indices

 No. Location Name/ Node Points/ Distancefrom SourcePoint Frequency Annual Outage Duration (hr/yr) Us AverageOutage Rate(f/yr/km) fs AverageOutageDuration(hr/yr/km) Mean Time toRepair (hr) rs 1 A/Hogiso 1,2/ 46 km 269 515.05 0.85 11.2 1.91 2 B/Hududa/ 51 km 269 515.05 0.94 10.1 1.91 3 C/Homba/ 58 km 269 515.05 1.07 8.88 1.91 4 D/Boro/ 63 km 269 515.05 1.16 8.18 1.91 5 E/Arbegona 1, 2/ 74 km 269 515.05 1.37 6.96 1.91 6 F/Arbegona 3,4,5,6/ 76.5 km 269 515.05 1.41 6.73 1.91 7 G/Kokosa 1,Kokosa tele,Kokosa hospi. / 82 km 269 515.05 1.51 6.28 1.91 8 H/Kokosa 2/ 82.7 km 269 515.05 1.53 6.23 1.91 9 I/ Gerba hurufa/ 96.7 269 515.05 1.79 5.33 1.91 10 H/Bokore/ 120.7 269 515.05 2.23 4.27 1.91
##### 4.4.2.2 Distribution line and power transformer overload

Table 11 provides the overload interruption reliability indices for distribution line and power transformer.

Table 11 Distribution line and power transformer overload interruption reliability indices

 No. Location Name/ Node Points/ Distancefrom SourcePoint Frequency Annual Outage Duration (hr/yr) Us AverageOutage Rate(f/yr/km) fs AverageOutageDuration(hr/yr/km) Mean Time toRepair (hr) rs 1 A/Hogiso 1,2/ 46km 14 53.14 0.044 1.16 3.8 2 B/Hududa/ 51km 14 53.14 0.049 1.04 3.8 3 C/Homba/ 58km 14 53.14 0.056 0.92 3.8 4 D/Boro/ 63km 14 53.14 0.06 0.84 3.8 5 E/Arbegona 1, 2/ 74km 14 53.14 0.071 0.72 3.8 6 F/Arbegona 3,4,5,6/ 76.5km 14 53.14 0.073 0.69 3.8 7 G/Kokosa 1,Kokosa tele,Kokosa hospi. / 82km 14 53.14 0.078 0.65 3.8 8 H/Kokosa 2/ 82.7km 14 53.14 0.08 0.64 3.8 9 I/ Gerba hurufa/ 96.7 14 53.14 0.09 0.55 3.8 10 H/Bokore/ 120.7 14 53.14 0.12 0.44 3.8

Table 12 provides the load point interruption reliability indices for generation failure and system overload.

Table 12 Generation failure and system overload load point interruption reliability indices

 No. Location Name/ Node Points/ Distancefrom SourcePoint Frequency Annual Outage Duration (hr/yr) Us AverageOutage Rate(f/yr/km) fs AverageOutageDuration (hr/yr/km) Mean Time toRepair (hr) rs 1 A/Hogiso 1,2/ 46km 52 104.01 0.15 2.26 2.0 2 B/Hududa/ 51km 52 104.01 0.17 2.04 2.0 3 C/Homba/ 58km 52 104.01 0.19 1.79 2.0 4 D/Boro/ 63km 52 104.01 0.21 1.65 2.0 5 E/Arbegona 1, 2/ 74km 52 104.01 0.25 1.41 2.0 6 F/Arbegona 3,4,5,6/ 76.5km 52 104.01 0.26 1.36 2.0 7 G/Kokosa 1,Kokosa tele,Kokosa hospi. / 82km 52 104.01 0.28 1.27 2.0 8 H/Kokosa 2/ 82.7km 52 104.01 0.29 1.26 2.0 9 I/ Gerba hurufa/ 96.7km 52 104.01 0.34 1.08 2.0 10 J/Bokore/ 120.7km 52 104.01 0.43 0.86 2.0

The failure rates and repair duration of different components such as power transformers, feeder, breakers, and bus bars are presented in Table 13. 132/33 kV transformers are considered with the rating of 16MVA.

Table 13 Component reliability data

 Component Type Average Outage Rate (f/yr/km), fs Mean Time to Repair (hr), rs Switching Time (hr) Power transformer 0.075 10 1 Circuit breaker 0.04 4 1 Main bus bars 0.02 4 1 Utility grid 0.02 24 1 Feeder 0.3 6 1

### 4.5 Base Case Reliability Analysis

The base case test system used for the reliability analysis is discussed in this section and shown in Figure 9. The ETAP generated individual load point system reliability indices report are shown in Figure 10. As a radial system, with no meshed connections, the average outage rate (λ) increases as theload point are farther from the supply point; however, the average duration time tends to be smaller. The mean time to repair (rs) and annual outage duration are assumed to be constant value.

Figure 10 Load point output report for base case Hawassa substation feeder line 10.

Different system reliability indices calculated for 33kV feeder line 10 are shown in Table 14.

Table 14 System indices of 33kV feeder line 10

 System Indices SAIFI System Average Interruption Frequency Index 47.5205 f/cust.yr SAIDI System Average Interruption Duration Index 85.4837 hr/cust.yr CAIDI Customer Average Interruption Duration Index 1.799 hr/cust.inter ASAI Average Service Availability Index 0.9902 ASUI Average Service Unavailability Index 0.00976 EENS Expected Energy Not Supplied 93.706 MW hr/yr

### 4.6 Comparison of Reliability Indices with Benchmarks

Reliability benchmarks are the standards against which analyzed or measured reliability is judged. The purposes of reliability benchmarks are to define minimum average reliability performance, by feeder type, for a distribution network. It provides a basis against which a distribution network service provider’s reliability performance is assessed. The benchmarks were calculated using the IEEE Guide for electric power distribution reliability indices – IEEE Standard 1366-2003. A benchmark of SAIDI, CAIDI, SAIFI and ASAI for nine countries is shown in Table 15. From the calculation and analysis considered in reliability evaluation of Hawassa substation 33kV feeder line 10, distribution system has an average value of SAIDI, SAIFI, CAIDI, ASAI and ASUI are 85.4837 hr/customer (5105 min.), 47.5205 interruptions/customer, 1.799 hr/customer (107.4 minutes), 99.02% and 0.976%, respectively. A higher SAIDI, SAIFI and CAIDI index number indicates worse performance. Lower number for SAIDI, SAIFI and CAIDI index indicates better reliability performance; i.e., a lower frequency of outages or shorter outage duration. Comparing the average value of SAIDI, SAIFI, CAIDI and ASAI for feeder line 10 of Hawassa distribution with the benchmark shows that it has the worst performance.

Table 15 Benchmarks for Reliability Indices [27]

 No Country SAIDI(Minutes/year) SAIFI(Interruptions/Customer) CAIDI(Minutes/outage) ASAI (%) 1 United States 240 1.5 123 99.91 2 Austria 72 0.9 112 99.97 3 Denmark 24 0.5 70 99.981 4 France 62 1 58 99.97 5 Germany 23 0.5 50 99.9999 6 Italy 58 2.2 106 99.9991 7 Netherlands 33 0.3 75 99.97 8 Spain 104 2.2 114 99.968 9 UK 90 0.8 100 99.964

### 4.7 Loss of Revenue Due to Power Interruption

A common approach used in quantifying the worth or benefit of electric-service reliability is to estimate the customer costs associated with power interruptions. The customer interruption cost, when an electric supply failure occurs, depends on many factors. The absence of sufficient data sets, required in a detailed evaluation of the customer outage costs, makes it difficult to estimate precise individual customer outage costs due to a specific failure event. This part of work estimates only the loss of cost by the utility due to interruptions in Hawassa feeder 10. Table 16 presents the electricity tariff of Ethiopian Electric Power Corporation (EEU) since July 8, 2006 [27].

Table 16 Electricity tariff of ethiopian electric power corporation

 TariffCategory Block Identification Monthly consumption (kWh) Birr (kWh) Domestic 1st Block 0-50 0.273 2nd Block 51-100 0.3564 3rd Block 101-200 0.4993 4th Block 201-300 0.55 5th Block 301-400 0.5666 6th Block 401-500 0.588 7th Block Above 500 0.6943 General 1st Block 0-50 0.6088 2nd Block Above 50 0.6943 Low Voltage Time of Peak - 0.5085 Day Industry Off – Peak - 0.3933

## 6 Result and Discussion

This work has illustrated serious reliability problem of Hawassa 33kV feeder line 10 and recommended the solutions. This is accomplished through detailed analysis and calculation. Therefore, this work represents a practical problem of Hawassa city distribution network, Ethiopia. Different cases studies are performed under various solution techniques. In base case environment, the reliability of feeder line 10 is very poor, as assessed by the different reliability indices. The calculated value of different reliability indices such as SAIDI, SAIFI, CAIDI, ASAI and EENS are 85.4837 hr/customer, 47.5205 interruptions/customer, 1.799 hr/customer, 0.9902 and 93.706 MWhr/yr, respectively, for base case Hawassa substation 33kV feeder line 10. Higher values of these indices are indicated that the reliability of feeder line 10 is poor. Due to poor reliability the outage cost associated with customers are also very high. The outage cost of energy is (0.6 ∗ 1000 ∗ 93.706) 56223 birr or 2530.062$. Therefore, to improve the reliability of selected feeder 10, three remedies are proposed by the authors. Further, reliability analysis is performed, which showed that reliability is considerably improved by using propped solutions. The proposed solutions are design, security strategy and adequacy solutions. ### 6.1 Design Practice In this method of reliability enhancement, the existing 50mm2 conductors are replaced by upgraded 95mm2 conductors. Further, the overloaded distribution transformers are replaced by enhanced capacity distribution transformers. The analysis is performed on ETAP software and results are discussed below. #### 6.1.1 Reliability analysis with design practice A study has been conducted on the performance and design practice of the Hawassa feeder 10 distribution systems to identify causes for power interruptions. The work has been conducted with field observation and data collection. The collected data includes peak load, type of faults, frequency and duration of interruption of medium voltage (33kV) outgoing feeder of the distribution system. It is found that over-loading, earth fault and short circuits are the major causes of interruptions in the distribution systems. It is also found that some transformers are over loaded and are not protected from the faults. Most of the interruptions are because of the transformer and related accessories like HRC fuses, oil and drop out fuse. Therefore, it is recommended to upgrade overloaded transformers and to install di-connectors at branching point and drop out fuse near to the distribution transformers. The other most frequent cause of outage is earth fault due to clearance problem. It is identified that Hawassa feeder 10 distribution line is constructed with 50mm2 AAC conductor, which has unacceptable voltage drop of 9.99%. This is corrected by re-conductoring the line with new 95mm2 AAC conductor, which improves the voltage drop to 4.99%. Further, the calculated short circuit capacity of 50mm2 conductor is 7.42kA, which is less than the rated short circuit fault current capacity i.e. 14.009kA. Therefore, for reliability enhancement, replacement of the line with 95mm2 conductor with 14.106kA short circuit capacity is performed. A upgraded capacity redesigned system is developed in ETAP software as discussed in the previous section, which shows that voltage drops are within permissible range. Further, by re-conductoring with 95mm2 conductor, the rated short circuit fault current capacity i.e. 14.106 kA is also increased and now equal to system’s rated short circuit fault current capacity 14.009 kA. Therefore, the reliability is greatly improved by redesigning the system. It is observed that out of 100% interruptions, majority (80.3%) of the interruptions are due to short circuit, earth fault and over load, while 15.52% is due to load shading and the remaining 4.18% is due to line and distribution transformer overload. It is also observed that active and reactive power losses occurred (copper loss of the feeder at full load) in the feeder line 10 with 50mm2 AAC conductors are 60.04kW and 11.39kVAr, respectively. Therefore, by re-conductoring with 95mm2 AAC conductors, these losses are reduced to 30.2kW and 10kVAr. #### 6.1.2 Cost benefit analysis with design practice With higher degree of reliability, investment cost also higher. On the other side, the customer costs associated with customer outages reduces as the reliability increases. The cost incurred due to the replacement of existing 50mm2 size conductor by 95 mm2 and upgrading the transformer capacity is discussed in this section. For 50mm2 conductor, total cost incurred is 65386.09$ as total length required is 91.7 km and cost per meter is 0.713$for 50 mm2 conductor. On the other hand, for 95mm2 conductor size, total cost incurred is 109641.31$ as total length required is 91.7 km and cost per meter is 1.196$. The installation costs of 50kVA, 100kVA and 200kVA transformers are 7951.56$, 9948.10$and 13281.61$, respectively. Further, the extra costs incurred for capacity up gradation from 50kVA transformer to 100kVA and 100kVA to 200kVA are 1996.53$and 3333.51$, respectively. Therefore, the total cost required for replacing the 50mm2 size conductor with 95mm2 and capacity enhancement of three transformers (2 transformers from 50kVA to 100kVA and 1 transformer form 100kVA to 200kVA) are 116967.88$. The revenue saved by minimizing the interruptions is 1886.43$ (=2444.48-558.05). Finally, the total revenue required for replacement of conductors and enhancement of transformer capacities is 49695.37$(=116967.88-65386.09-1886.43). ### 6.2 Security Strategies Under this solution technique, number of faults and fault duration is reduced by using various techniques such as preventive maintenance, monitoring, system automation, system reconfiguration etc. The analysis is performed on ETAP software and results are discussed below. #### 6.2.1 Reliability analysis with security strategies The frequency and duration of interruptions for Hawassa feeder 10 distribution networks for 12 months are analyzed. Distribution reliability indices (SAIFI, SAIDI, CAIDI, ASAI and EENS) are calculated and analyzed. Further, calculated distribution reliability indices are compared with benchmark values. From the analysis, an average value of SAIFI, SAIDI, CAIDI, ASAI and EENS are 22.1833 f/customer*year, 46.046 hr/customer*year, 2.076 hr/customer Interruptions, 0.9947 and 51.212 MWh/yr, respectively. By incorporating security strategies in the system, there is an improvement in the reliability of selected feeder by the percentage of 46.13%, 53.32% and 45.35% for SAIDI, SAIFI and EENS, respectively from the base case. #### 6.2.2 Cost benefit analysis with security strategies The total outage cost due to interruptions after the incorporation of security strategies in the system is reduced to 1382.724$, as compared to 2530.062$in the base case condition. Therefore, the total reduction in the outage cost after incorporation of security strategies is 1147.338$.

This solution technique incorporated distributed renewable energy generation sources at distribution level for reliability enhancement. In this work modular distributed generation (DG) sources such as photovoltaic (PV), wind turbine generator (WTG) and battery energy storage (BES) are incorporated at Hawassa distribution feeder 10 for enhancing reliability. The analysis is performed on ETAP software and results are discussed below.

#### 6.3.1 Reliability analysis with adequacy solutions

Distribution reliability indices (SAIFI, SAIDI, CAIDI, ASAI and EENS) are calculated and analyzed with distributed generation sources. From the analysis, an average value of SAIFI, SAIDI, CAIDI, ASAI and EENS are 9.0924 f/customer*year, 19.444 hr/customer*year, 2.139 hr/customer Interruptions, 0.9978 and 21.392 MWh/yr, respectively. By incorporating distributed generation sources in the system, there is great improvement in the reliability of selected feeder by 77.25%, 80.87% and 77.17% for SAIDI, SAIFI and EENS, respectively from the base case.

#### 6.3.2 Cost benefit analysis with adequacy solutions (distributed generations)

In this section cost benefit analysis is performed with distributed generation sources.

##### 6.3.2.1 pV generation

Table 21 provides the component price of 400kW PV power generation system.

Table 21 Component price of 400kW PV power generation

 No. Description Quantity Unit Price [Birr] Total Price [Birr] 1. Module (250 Wp) 1600 22/Wp 8,800,000 2. Cabling, Switch, Holder, Plug, Divider and PV panel support structure cost - - 10,000 3. 400kW inverter 1 600,000 600,000 4. Direct cost of equipment - - 9,410,000 5. Installation cost (7% of direct cost of the equipment) 658,700 Total system cost 19478700

##### 6.3.2.3 Battery energy storage system

Table 23 provides the component price of battery energy storage system.

Table 23 Component price of battery system

 No. Component Description Unit Price (Birr) Total Price (Birr) 1 Lead Acid Deep Cycle Battery 4.762Ah of 300 pcs. total 1428.6Ah 974.95 292485

The total cost of all the components of battery energy storage system is 292485 birr or 10635.82$. Therefore, the total cost associated with DGs is 734785.9$ (=708316.43+10635.82+11502.28). The total outage cost due to interruptions after the incorporation of DGs in the system is reduced to 560.05$, which is less than the previous cases i.e. base case (2530.062$) and security strategies solution (1382.72$). Therefore, the total reduction in the outage cost after incorporation of DGs with respect to base case is 1970.012$ per 1000 hours. Thus, the total saving in one year is around 17257.31\$.

From the above analysis, it is clear that by incorporating DGs in the system, the reliability of the Hawassa distribution system feeder 10 is greatly improved. Therefore, DG installation is the best method for improving the reliability of practical distribution system.

## 7 Conclusion

The main aim of this work is to identify causes of interruptions and suggest possible solutions to enhance the reliability of Hawassa distribution feeder 10. Furthermore in this work, reliability analysis is performed with proposed solutions for Hawassa distribution systems feeder 10. The collected data for selected distribution network is analyzed in detail to evaluate the reliability of the system. From the substations fault record, it is concluded that mostly the failures in the distribution system are happened due to short circuits, earth faults and over loads (i.e. when generated power is below the total demand, other than blackout). The Hawassa distribution feeder 10 reliability indices (SAIDI, SAIFI, CAIDI and ASAI) for eleven months (January 2015 to November 2015) have been calculated, analyzed and results are stated in this work. The calculated results for SAIDI, SAIFI, CAIDI and ASAI are compared with benchmarks, which verified Hawassa distribution feeder 10 is noncompliance with standards. Furthermore, three potential solutions for the improvement of the reliability of the distribution system are considered. The proposed solutions are design, security (operation) and adequacy for reliability improvement and they are discussed extensively. The result shows the improvement in the reliability of feeder 10. Largest improvement (around 80%) is obtained in the feeder 10 reliability with the incorporation of renewable distributed generations (DGs). Since the location and the size of the renewable DG’s greatly affects the reliability of the power systems, finding the optimal size and location of the DG’s can be investigated in the future work. Optimization techniques that consider constraints such as the range and location of DG in the distribution network can be also be used for this purpose in the futuristic enhancement of the current study.

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## Biographies

Yishak Kifle completed his Bachelor of Science degree in Electrical Engineering. He completed his Master of Science degree in Power System and Energy Engineering from the School of Electrical and Computer Engineering, Hawassa University Institute of Technology, Hawassa University, Hawassa, Ethiopia. Currently, he is working as an Assistant Manager in the Ethiopian Electric Power Corporation, Ethiopia. His area of interest are power system, renewable energy integration, micro grid, reliability analysis of power system, solar generation, wind generation, battery storage system, adequacy analysis of power system, power quality, optimal operation of renewable energy generation sources, stability analysis and probabilistic load flow analysis of power system.

Baseem Khan received his Bachelor of Engineering degree in Electrical Engineering from the Rajiv Gandhi Technological University, Bhopal, India in 2008. He received his Master of Technology and Doctor of Philosophy degrees in Electrical Engineering from the Maulana Azad National Institute of Technology, Bhopal, India, in 2010 and 2014, respectively. Currently, he is working as an Assistant Professor in the School of Electrical and Computer Engineering, Hawassa University Institute of Technology, Hawassa University, Hawassa, Ethiopia. His research interest includes power system restructuring, power system planning, smart grid technologies, meta-heuristic optimisation techniques, reliability analysis of renewable energy system, power quality analysis and renewable energy integration.

pawan Singh received the B.E. (Computer Science and Engineering) from CCS University, Meerut, India, M.Tech. (Information Technology) from GGSIPU, New Delhi, India and Ph.D. (Computer Science) from Magadh University, Bodh Gaya, India in 2013. Currently, he is serving in School of Informatics, Institute of Technology, Hawassa University, Hawassa, Ethiopia. His research interests include software metrics, software testing, software cost estimation, web structure mining, energy aware scheduling, nature inspired meta-heuristic optimization techniques and its applications. He has published number of research papers in the journals of international reputation.