The Journal of Engineering Research (TJER), Vol. 15, No. 2 (2018) 102-113 DOI: 10.24200/tjer.vol15iss2p102-113 Technical Loss Reduction in Rural Areas - The Case of Saih Al Khairat M.H. Albadi*,a, A.H. Al Maghdria, A.S. Al Hinaia, E.F. El-Saadanyb, M.S. Awladthanic, S. Al Hinaia, A. Al Jabria, K. Al Azania, and A. Al Maqrashia a Department of Electrical and Computer Engineering, College of Engineering, Sultan Qaboos University, PO Box 33, Muscat-123, Sultanate of Oman. b Department of Electrical and Computer Engineering, University of Waterloo, 200 University Ave W, Waterloo, ON N2L3G1, Canada. c Real Areas Electricity Company, P.O. Box 1166, A’Seeb 133, Sultanate of Oman. Received 31 October 2016; Accepted 6 March 2017 Abstract: This article investigates the potential for reducing technical losses in the rural area network of Saih Al Khairat in Thumrait, Oman. Based on the available network data, a power flow model of the system under study is built and the system performance is studied. To reduce losses and improve the voltage profile, different candidate solutions are investigated in coordination with the distribution system operator. An economic evaluation of the different options is conducted. Keywords: Distribution systems; Rural areas; Loss minimization; Power flow; Reactive compensation. حقلة سدح اخلريات-التقندة يف املنقطق الريافدة افقيدداحلد من امل ،*م. ح. البقدي أ ، أ. ، أ اهلنقئي س.، جثقني أوالد ص.م.، بنيعدلسا ف..إ، أ يئهلنقا ع. س، أ ملغدري. ا حأ. أ قرشيملا .وأ أ زانيالع خ.، أ جلقبريا التقندة يف شبكة املنقطق الريافدة يف سدح اخلريات يف منطقة افقيددتبحث هذه املققلة يف إمكقندة احلد من امل :امللخص واستنقدا إىل بدقنقت الشبكة املتقحة، مت بنقء منوذج لتدفق الطقية للنظقم يدد البحث وتقددم أداء .مثريت بسلطنة عمقن اجلهد، يتم البحث يف احللول املقرتحة املختلافة بقلتنسدق مع مشغل شكلوحتسني افقيددأجل التقلدل من املومن .النظقم .كمق جيري تقددم ايتصقدي ملختلف اخلدقرات .نظقم التوزيع .التافقعلي التعويض الطقية، تدفق املافقيدد، تقلدل الريافدة، املنقطق ،عالتوزي أنظمة: املافتقحدة الكلمقت * Corresponding author’s email: mbadi@squ.edu.om mailto:mbadi@squ.edu.om M.H. Albadi, A.H. Al Maghdri, A.S. Al Hinai, E.F. El-Saadany, M.S. Awladthani, S. Al Hinai, A. Al Jabri, K. Al Azani, and A. Al Maqrashi 103 Nomenclature AER authority of electricity regulation AMI advanced metering infrastructure DVR dynamic voltage restorer DPS Dhofar power system 𝐼 current in Amperes MIS main interconnected system RAEC Rural Areas Electricity Company NPV net present value OMR Omani Riyals P real power in Watt PF power factor PP payback period in years Q reactive power in VAR S apparent power in VA UPFC unified power-flow controller 𝐸𝑠𝑜𝑢𝑟𝑐𝑒 measured supplied energy in kWh 𝐸𝑙𝑜𝑎𝑑 measured consumed energy in kWh |𝑉𝑖 | voltage magnitudes at bus 𝑖 in per unit 𝛿𝑖 voltage angle at bus 𝑖 in radian |𝑌𝑖𝑗 | magnitude of Y-bus element in per unit 𝜃𝑖𝑗 angle of Y-bus element in radian 𝑆𝐿 𝑖𝑗 losses in the branch i-j in per unit 𝐹𝐿𝑠 loss factor 𝐹𝐿𝐷 load factor 𝑃𝑉𝐴 present value of the recurring annuity A. 1. Introduction Losses in power systems are classified into two categories: technical losses and non-technical losses Al-Mahroqi, Metwally et al. (2012). Non- technical losses result from actions external to power systems, for instance, human manipulation or mistakes in meter reading. Electricity theft is one of the main causes of non- technical losses in some systems Antmann (2009). To reduce losses of this type, some utilities use automated meter reading and advanced metering infrastructure (AMI). In addition, it is possible to use data mining and intelligence-based techniques to detect and reduce non-technical losses Nagi, Mohammad et al. (2008). Even without AMI, non-technical losses can be estimated by comparing the measured energy consumption in a feeder with the energy that the utility bills plus technical losses Neto and Coelho (2013). In comparison, technical losses are related to the physical characteristics and functions of the electrical network that result in the dissipation of electrical energy as heat. These types of losses occur mainly in low-efficiency equipment and in transmission and distribution lines. Technical losses can be classified as generation losses, transmission losses, and distribution losses. The causes of the technical losses include a low power factor, long lines, unbalanced loading, and overloading. An assessment of technical losses can be made with engineering calculations based on the design of the system. The majority of avoidable technical losses occur where the current is high. Technical losses represent economic loss for utilities as generating more energy results in higher costs of generation. In addition, as losses result in the generation of more electrical energy to satisfy the generation-load balance requirement, high technical losses contribute to global warming. According to the Authority of Electricity Regulation (AER), the total losses (technical and non-technical) in Oman’s electricity sector were estimated to be 10.2% in 2015, a decrease from 11.6% in 2014. Moreover, losses in the Main Interconnected System (MIS) decreased from 11.6% in 2014 to 10% in 2015, whereas the Rural Areas Electricity Company’s (RAEC’s) losses increased from 9.2% in 2014 to 10.7% in 2015 (AER 2016). The main contributions of this article are listed below: 1. Modelling of a practical system in Oman during peak load condition. The system is known to have an under-voltage problem. 2. Simulating the system performance in terms of voltage profile and technical losses using ETAP® software package. 3. Simulating the system performance considering five solutions that aim for reducing system losses and improving voltage profile. These options were coordinated with the network operator. 4. Conducting a cost-benefit study of potential savings due to loss reduction for the considered options. These options involve adding reactive compensation elements at selected buses as well as network reconfiguration by adding a new 11 Technical Loss Reduction in Rural Areas - The Case of Saih Al Khairat kV feeder. More details of these options are presented in section 5. Following this introduction, the paper presents a survey of sources of and mitigation techniques for technical losses in distribution systems. Section 3 presents the data of the system under study. As for section 4, it presents and discusses the results of the simulation of the network performance with different options. Finally, section 5 presents a summary of the main conclusions. 2. Technical Loss Sources and Mitigation Techniques Technical losses in a power system result naturally from current flow through resistive materials as well as the nonlinear characteristics of some equipment in the grid. The most common example of technical loss is the power dissipated in transformers and transmission lines due to their internal resistance. For example, the losses in a transmission line can be calculated by determining the difference between the measured energy injected from the source into the transmission line (𝐸𝑠𝑜𝑢𝑟𝑐𝑒 ) and the measured energy consumed (𝐸𝑙𝑜𝑎𝑑 ) by loads located at the end of that transmission line. 𝐿𝑜𝑠𝑠𝑒𝑠 = 𝐸𝑠𝑜𝑢𝑟𝑐𝑒 − 𝐸𝑙𝑜𝑎𝑑 (1) 2.1 Poor Power Factor In general, losses that occur in conductive materials can be decreased by reducing the current or by reducing the resistance. However, reducing the current is more effective as the loss formula (I2R) shows. The magnitude of the current is a function of the apparent power (S), which, in turn, has two components: real power (P) and reactive power (Q). |𝐼| =|S|/√3|V| (2) 𝑆 = 𝑃 + 𝑗𝑄 (3) The power factor (PF) is related to the cosine of the angle between the voltage and the current or the ratio of the real power to the apparent power. 𝑃𝐹 = 𝑃 𝑆 (4) 𝑃𝐹 = cos (tan−1 𝑄 𝑃 ) (5) The PF decreases as the ratio of reactive power to the real power increases. It is possible to achieve an improvement in PF by using devices, such as capacitors (switched/fixed) that deliver reactive power. A case study involving the reduction of losses using power factor correction was presented in Phetlam- phanh, Premrudeepreechacharn et al. (2012). Figure 1 is a simple illustration of the reactive compensation concept. Distribution utilities require customers to maintain a good (high) power factor to reduce losses. For example, industrial customers in Oman are obliged to maintain a power factor of at least 0.9 (AER 2016). 2.2 Unbalanced Loads Distribution network losses can vary depending on the level of the balancing of the load. In an unbalanced operation mode, voltages and currents are not equally distributed between phases. Different factors can result in unbalanced operation modes. They include unequal phase loading and different line parameters in different phases. Unbalance commonly occurs in three-phase distribution systems. However, it can be harmful to the operation of power systems. On the generation side, current asymmetry negatively affects efficiency. Unbalance reduces transmission capacity and efficiency Albadi, Hinai et al. (2015). In addition, it reduces the capacity and efficiency of the transformers. Zero sequence current is converted into a circulating current in a delta/wye-grounded transformer, resulting in increased losses. There are several approaches to reduce the effects of unbalance. It is essential to adopt regulations and standards to ensure that all system components are designed and manufactured to be symmetrical. These components include generators, transformers, Sbefore (VA) Saft er (VA) Qaft er (VAR) Qbefore (VAR) P (W) Figure 1. Illustration of the reactive compen- sation concept. 104 M.H. Albadi, A.H. Al Maghdri, A.S. Al Hinai, E.F. El-Saadany, M.S. Awladthani, S. Al Hinai, A. Al Jabri, K. Al Azani, and A. Al Maqrashi 107 transmission lines, three-phase motors, and switching equipment. In addition, imposing standards related to voltage and current unbalance levels is essential. These unbalance levels should be defined for both utilities and customers Albadi, Hinai et al. (2015). Another approach involves revising the connection of single-phase loads on the utility and customer sides. In addition, unbalance can be reduced by using balancing equipment such as single-phase voltage regulators, a dynamic voltage restorer (DVR), surge-protection devices, unified power- flow controllers (UPFC), and energy storage devices Kazibwe, Ringlee et al. (1990), Jouanne and Banerjee (2001). 2.3 Transformers Losses In power distribution networks, the losses in transformers can reach 3% of the total electrical power generated (Ltd 2006). The transformer efficiency can be increased by reducing these losses. The losses in transformers can be classified in two different categories: the core loss or no-load loss category and the load or copper (winding) loss category Al-Badi, Elmoudi et al. (2011). Load losses are not highly sensitive to grid voltage changes, but they are highly sensitive to temperature variations. In the new distribution transformers, the secondary winding takes the form of a cylindrical sheet of aluminium. This is an important consideration in the adjustment of losses for temperature variation. 2.4 Network / Feeder Reconfiguration In some cases, distribution network restructuring to minimize losses is highly cost- efficient. This option is of interest to efficiency- conscious electric utilities. Distribution feeders can be reconfigured by opening and closing switches while maintaining all load requirements (Ramesh, Chowdhury et al. 2009). To reduce power losses and improve voltage profile, both optimal feeder reconfiguration and optimal capacitor placement has been studied extensively using different optimization techniques Chang (2008). For example, genetic algorithms are used in Swarnkar, Gupta et al. (2010), Farahani, Vahidi et al. (2012). To overcome genetic algorithms drawbacks encountered in radial feeders’ capacitor placement problem, authors in Fu-Yuan and Men-Shen (2005) used evolutionary programm- ing. In Farahani, Vahidi et al. (2012), simulated annealing is used for joint reconfiguration and capacitor placement optimization. An ant colony algorithm is used in Kasaei and Gandomkar (2010) to solve feeder reconfiguration and capacitor placement optimization problem. Authors in Khalil, Gorpinich et al. (2013), and Sedighizadeh, Dakhem et al. (2014) used a selective particle swarm optimization to solve the optimization problem. 3 System Data and Modelling The Rural Areas Electricity Company (RAEC) is an Omani closed joint stock company registered under the Commercial Companies Law of the Sultanate of Oman. The company commenced its operations on the 1st of May, 2005, following the implementation of a decision that the Ministry of National Economy issued pursuant to the Regulation and Privatization of the Electricity and Related Water Sector law, which was promulgated by Royal Decree 78/2004 (RAEC 2016) (RAEC 2016). RAEC serves customers who are not connected to the Main Interconnected System (MIS) and Dhofar Power System (DPS). Its license and business activities are associated with generation, transmission, and distribution (RAEC 2016). 3.3 System Data The system under study consists of 12 diesel generators (43 MW and 81 MW) and 103 transformers, as Table 1 indicates. The network has four feeders. One is a 33 kV feeder and three are 11kV feeders. The 33 kV feeder is operated and owned and operated by a large customer and was not modelled due to missing data. Three types of overhead transmission lines are used in the network. They are Panther, Wolf, and Dog ACSR conductors. Moreover, the underground cables in the 11kV feeders come in different sizes (50, 185, 240, and 500 mm2). In the 33 kV feeder, the underground cables have a size of 300 mm2. Table 1. Network data summary. Component Numbers Diesel Generators 12 Transformers 95 Busses 262 Overhead Lines (km) 175.750 Cables (km) 15.790 Measured peak kW 4 975 Measured peak kVAR 3,784 105 Technical Loss Reduction in Rural Areas - The Case of Saih Al Khairat There are six 11 kV/415 V transformers, as Table 2 indicates. Appendices A and B present the details of the loading on these transformers. The single-line diagram is presented in the Appendix C, as is the per unit line data. 3.4 Power Flow Model Power flow analysis is widely used in power system operation and planning. The power flow model of the system can be built using relevant network, load, and generation data. The outputs of the power flow model include voltages at different busses and line flows in the network. These outputs are obtained by solving power balance equations: 𝑃𝑖 = ∑ |𝑉𝑖 ||𝑉𝑗 ||𝑌𝑖𝑗 | cos(𝛿𝑖 − 𝛿𝑗 − 𝜃𝑖𝑗 ) 𝑛 𝑗=1 (6) 𝑄𝑖 = ∑ |𝑉𝑖 ||𝑉𝑗 ||𝑌𝑖𝑗 | sin(𝛿𝑖 − 𝛿𝑗 − 𝜃𝑖𝑗 ) 𝑛 𝑗=1 (7) where |𝑉𝑖 | and |𝑉𝑗 | are the magnitudes of the voltage at bus 𝑖 and 𝑗, respectively; 𝛿𝑖 and 𝛿𝑗 are the associated angles; |𝑌𝑖𝑗 | is the magnitude of the Y-bus element between the two busses; and 𝜃𝑖𝑗 is the corresponding angle. These power balance equations are nonlinear; therefore, iterative techniques such as the Newton-Raphson, the Gauss-Seidel, and the fast-decoupled methods are commonly used Saadat (1999). In this case, ETAP® software package was used to model and evaluate the case study. The system losses can be calculated once the power flow problem is iteratively solved. For example, the losses in the branch i-j are the algebraic sum of the power flows. 𝑆𝐿 𝑖𝑗 = 𝑆𝑖𝑗 + 𝑆𝑗𝑖 (8) where 𝑆𝑖𝑗 = 𝑉𝑖 𝐼𝑖𝑗 ∗ and 𝑆𝑗𝑖 = 𝑉𝑗 𝐼𝑗𝑖 ∗ 3.5 Load Data The load data of the power flow model were validated with the recorded data as shown in Table 2. Transformer data summary. kVA Numbers X/R % Z% 1,000 6 5.10 4.75 500 5 5.1 4.75 315 69 3.97 4.75 200 9 3.37 4.75 100 5 2.32 4.75 50 1 3.97 4.75 Fig. 2. The difference between the measured data and that obtained from the model was about 8.7%, 2.35%, and 0.2% for Feeders 1, 2, and 3, respectively. These differences were due to records missing for some of the transformers. In addition, the recorded peak loads did not occur at the same time. Individual service transformer (11 kV/415 V) load data were included in the model to obtain the feeder load data. 4 Results and Discussion 4.1 System Technical Losses Simulation results presented in Table 3 show that technical real power losses represented about 5.18% of the generated power, whereas the reactive power losses were 11.28% of the generated power. The distributions of these losses are given in Figs. 3 and 4. Figure 2. Recorded versus modelled data. Figure 3. Distribution of power losses in the system. Figure 4. Distribution of losses in the feeders. 0 0.5 1 1.5 2 Feeder 1 Feeder 2 Feeder 3 P o w e r (M W ) Actual 1% 76% 23% Cables Lines Transformer 16.43 % 75.24 % 5.48 % Feeder 1 Feeder 2 Feeder 3 106 M.H. Albadi, A.H. Al Maghdri, A.S. Al Hinai, E.F. El-Saadany, M.S. Awladthani, S. Al Hinai, A. Al Jabri, K. Al Azani, and A. Al Maqrashi 107 It was clear that most losses occurred in the overhead line. This result was attributable to the fact that overhead lines dominated the rural area system. Another observation was that three quarters of the losses occurred in Feeder 2. This result was attributed to the long distances connecting the scattered loads in this feeder. 4.2 Voltage Profile The Omani distribution code mandates that the voltage profile be within 6% of the nominal value in distribution networks (33 kV, 11 kV, and 415 V) MJEC, MZEC et al. (2005). The Omani grid code allows for variation from the nominal value of up to 10% in transmission networks (132 kV, 220 kV, and 400 kV) (OETC 2010). The voltage profile at all busses is given in Fig. 5. The nominal voltage in this system was 11 kV. The voltage drop varied from one bus to another, depending on the loading of each bus and the distance from the power house. In general, the voltage drop was mostly due to the long distances between the service transformers in the network. The 6% limit that the distribution code specified was violated at many busses, especially in Feeder 2. The main reasons of having this low voltage problem are the growing demand and extension of feeders to connect new customers in this small isolated system. These substantial changes in this area are due to transferring some agricultural activities from Albatinah Governorate to Saih Al Khairat in Al Najd area, where an underground water reservoir was discovered. This transfer is aiming to conserve underground water in Albatinah Governorate and reduce air pollution caused by some agricultural activities. Therefore, to connect new customers, feeders were extended. 4.3 Candidate Solutions Different options for reducing losses and improving the voltage profile were studied in coordination with RAEC. These options are listed below: Table 3. Total supply, demand, and losses. Supply Demand Losses P (MW) 4.975 4.553 0.422 Q (MVar) 3.7844 3.017 0.641 S (MVA) 6.092 5.325 0.7674 Option 1: Adding 500 kVAR to the existing 500 kVAR pole-mounted capacitor bank at Bus 66. Option 2: Installing a 1 MVAR pole-mounted capacitor bank in Feeder 2 at Bus 46 in addition to the existing 500 kVAR capacitor bank at Bus 66. Option 3: Installing two pole-mounted capa- citor banks (1 MVAR each), one in Feeder 2 at Bus 46 and the other in Feeder 1 at Bus 40, in addition to the existing 500 kVAR capacitor bank at Bus 66. Option 4: Installing 3 pole-mounted capacitor banks, two 1MVAR capacitor banks in Feeder 2 at Bus 46 and one 500 kVAR capacitor bank in Feeder 1 at Bus 40, in addition to the existing 500 kVAR capacitor bank. Option 5: Network reconfiguration by dis- connecting Feeder 2 at Bus 34 and connecting a new 11 kV Feeder (length: 30.97 km) from the power station to the disconnected point. The candidate buses for reactive compensation options are selected based on the network voltage profile and the network topology. The sizes of switched capacitors are based on standard sizes used by RAEC. The suggested new feeder connection point (bus 34) is based on distribution of loads and actual network topology. The voltage profiles of these options in the peak loading condition are shown in Fig. 6. 4.4 System Performance with Different Options Figure 6 presents the improvements in voltage at different locations of the network using the different proposed options. It is clear that Option 2, Option 3, and Option 4 led to significant improvements in voltage. Hence, they will result in better compliance with ±6% voltage limits, which, in turn, will result in increases in equipment lifetime and customer satisfaction. 4.5 Economic Evaluation of Different Options Tables 4 and 5 show the losses and associated energy costs for the aforementioned options. The energy calculation can be performed by determining the loss factor (𝐹𝐿𝑠) using the following equation (Gonen 2008). 𝐹𝐿𝑆 = 0.84 𝐹𝐿𝐷 2 + 0.16 𝐹𝐿𝐷 (9) where 𝐹𝐿𝐷 is the Load Factor. Technical Loss Reduction in Rural Areas - The Case of Saih Al Khairat 108 Figure 5. Voltage profile at all busses. Figure 6. Voltage profile for different scenarios. Once 𝐹𝐿𝑠 is found, the average power loss can be calculated using the peak losses obtained from the power flow simulation. The load factor used in this study is 0.82. Based on this value, the loss factor is 0.7. Average Loss = FLS ∗ Peak loss (10) The annual energy loss can be calculated using the following formula: Annual Energy Loss = Average Loss * 8760 (11) RAEC estimated the capital cost of the different options. It was estimated that the costs of the switched capacitor banks and the 11 kV feeder were 14,000 OMR/MVAR and 13,000 OMR/km, respectively. Using these values and the results from the previous table, the annual savings, payback period, and net present value were calculated based on the 10.4% discount rate and 25-year lifetime that RAEC employed. The net present value (NPV) was calculated for each option. The NPV of a project is the difference between revenues and costs in the current monetary value. In any comparison of investment options, the project with the maximum NPV is the winning one. For a recurring constant annual income, the present value can be found using the following formula: Table 4. Reduction in losses. Scenario Loss (MW) Loss Reduction (%) Existing Network 0.400 ------- Option 1 0.377 5.75 Option 2 0.351 12.25 Option 3 0.336 16.0 Option 4 0.337 15.75 Option 5 0.233 41.75 60 70 80 90 100 110 B u s 1 B u s 6 B u s 1 1 B u s 1 6 B u s 2 1 B u s 2 6 B u s 3 1 B u s 3 6 B u s 4 1 B u s 4 6 B u s 5 1 B u s 5 7 B u s 6 3 B u s 6 9 B u s 7 4 B u s 8 1 B u s 8 6 B u s 9 6 B u s 1 0 1 B u s 1 0 6 B u s 1 1 1 B u s 1 1 6 B u s 1 2 1 B u s 1 6 3 B u s2 1 5 B u s2 2 0 B u s2 2 5 B u s2 3 3 B u s2 3 9 V o lt a g e ( % ) Existing Lower Limt (%V) Upper Limt (%V) 60 70 80 90 100 110 B u s 1 B u s 6 B u s 1 1 B u s 1 6 B u s 2 1 B u s 2 6 B u s 3 1 B u s 3 6 B u s 4 1 B u s 4 6 B u s 5 1 B u s 5 7 B u s 6 3 B u s 6 9 B u s 7 4 B u s 8 1 B u s 8 6 B u s 9 6 B u s 1 0 1 B u s 1 0 6 B u s 1 1 1 B u s 1 1 6 B u s 1 2 1 B u s 1 6 3 B u s2 1 5 B u s2 2 0 B u s2 2 5 B u s2 3 3 B u s2 3 9 V o lt a g e ( % ) Existing OPTION 1 OPTION 2 OPTION 3 OPTION 4 OPTION 5 M.H. Albadi, A.H. Al Maghdri, A.S. Al Hinai, E.F. El-Saadany, M.S. Awladthani, S. Al Hinai, A. Al Jabri, K. Al Azani, and A. Al Maqrashi 109 𝑃𝑉𝐴 = 𝐴 (1+𝑑𝑟)𝑁−1 𝑑𝑟(1+𝑑𝑟)𝑁 (12) where 𝑃𝑉𝐴 is the present value of the recurring annuity, A. In this context, the annuity refers to the annual savings with the implementation of different options. The payback period (PP) is defined as the length of time required to recover the initial investment in a project. The shorter the length, the more economically attractive to investors the project is. Another benefit of reducing losses is that it brings down fuel costs and subsidies. Additionally, the reduction of losses results in improved conditions for the immediate environment due to the reduction in power generation and CO2 emission. The results presented in Table 6 show that any of the first four options would recover the costs within 2.5 years. The best option, which had the highest NPV, was Option 4. Although Option 5 had the largest annual savings, it was not economically attractive. This was because it was associated with a high capital cost. 5. Conclusion The objective of the article was to investigate different candidate solutions for reducing technical losses and improving the voltage profile of a rural area distribution system. A model of the Saih Al Khairat network, which the Rural Areas Electricity Company owns, was developed, and the load flow solution was obtained using ETAP® software package. The network data was collected from the Rural Areas Electricity Company and the equipment manufacturers. To improve the voltage profile and reduce losses, five options were studied and ranked according to their economic feasibility. The best option among them was the installation of three pole-mounted capacitor banks: two in Feeder 2 and one in Feeder 1. Conflict of Interest The authors declare no conflicts of interest. Funding No funding was received for this project. Acknowledgment The authors would like to thank the Rural Area Electricity Company for providing the system data and for their support in achieving this work. References AER (2016), Authority of Electricity Regulation Annual Report 2015. Table 5. Cost of losses for different scenarios. Scenario Average Loss (MW) Energy Loss (kWh/year) Cost (OMR/year) Existing Network 0.28 2452800 110,376 Option 1 0.2639 2311764 104,029 Option 2 0.2457 2152332 96,855 Option 3 0.2352 2060352 92,716 Option 4 0.2359 2066484 92,992 Option 5 0.1631 1428756 64,294 Table 6. Economic evaluation for different scenarios. 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Al Maqrashi 109 Appendix A: Branch data Table A1: The per unit system branch data From To R (pu) X (pu) B (pu) M1 457 0.326492562 0.521906224 1.36722E-05 457 213 0.056652893 0.091773945 5.74309E-07 457 33 0.673791736 1.091498115 6.83045E-06 33 215 0.064961983 0.105234123 6.58541E-07 33 35 0.574082645 0.929975972 5.81966E-06 35 217 0.389771901 0.631404739 3.95124E-06 35 38 0.143520661 0.232493993 1.45492E-06 38 39 0.034324793 0.057795373 5.76642E-06 39 40 0.270347107 0.568832638 3.79526E-06 40 44 0.123909091 0.260714959 1.73949E-06 44 45 0.054208264 0.061759603 3.4956E-07 45 47 0.255230579 0.290784798 1.64585E-06 47 49 0.347836364 0.396290786 2.24301E-06 44 50 0.074536364 0.084919454 4.80645E-07 50 233 0.422372727 0.48121024 2.72366E-06 44 51 0.101380165 0.213312239 1.42322E-06 51 52 0.006758678 0.014220816 9.48815E-08 52 54 0.074049587 0.064793388 3.90785E-05 54 236 0.016198347 0.018016529 1.23554E-05 54 235 0.026727273 0.029727273 2.03863E-05 52 55 0.234094215 0.21715301 0.000103544 55 56 0.195702479 0.171239669 0.000103279 56 59 0.032793388 0.028694215 1.73062E-05 55 57 0.221350413 0.252185046 1.42737E-06 57 239 0.170082645 0.102396694 4.97665E-05 57 58 0.584997521 0.66648905 3.77234E-06 58 242 0.149072727 0.169838908 9.6129E-07 40 41 0.328826446 0.365735535 0.000250814 41 42 0.151272727 0.132363636 7.98318E-05 42 221 0.128 0.112 6.75499E-05 42 224 0.058181818 0.050909091 3.07045E-05 M1 71 0.498016529 0.799352537 1.60206E-05 71 72 0.883785124 1.431673536 8.95922E-06 72 73 0.146542149 0.237388603 1.48555E-06 72 74 0.166181818 0.269203571 1.68464E-06 71 70 0.072515702 0.117470649 7.35115E-07 70 62 0.045322314 0.073419156 4.59447E-07 62 63 0.15107438 0.244730519 1.53149E-06 63 64 0.083090909 0.134601785 8.4232E-07 64 67 0.203950413 0.330386201 2.06751E-06 63 61 0.083090909 0.134601785 8.4232E-07 61 58 0.092155372 0.149285617 9.34209E-07 58 59 0.048343802 0.078313766 4.90077E-07 58 57 0.099709091 0.161522143 1.01078E-06 77 78 0.355024793 0.57511672 3.599E-06 57 51 0.696452893 1.128207693 7.06017E-06 51 52 0.101219835 0.163969448 1.0261E-06 51 46 1.661818182 2.692035709 1.68464E-05 46 47 0.400347108 0.648535875 4.05845E-06 47 48 0.203950413 0.330386201 2.06751E-06 48 50 0.15107438 0.244730519 1.53149E-06 48 49 0.007553719 0.012236526 7.65745E-08 46 45 0.377685951 0.611826298 3.82873E-06 45 44 0.190353719 0.374437694 2.34318E-06 44 44G 0.028704132 0.046498799 2.90983E-07 44G 44GH 0.012247934 0.003421488 1.01281E-06 44G 43 0.090644628 0.146838311 9.18894E-07 43 34 0.490991736 0.795374187 4.97734E-06 34 35 0.045322314 0.073419156 4.59447E-07 35 36 0.21452562 0.347517337 2.17472E-06 36 37 0.021150413 0.034262273 2.14409E-07 37 38L 0.045322314 0.073419156 4.59447E-07 38L 39 0.039279339 0.063629935 3.98187E-07 38L 40 0.024171901 0.039156883 2.45038E-07 40 42 0.045322314 0.073419156 4.59447E-07 40 41 0.199418182 0.323044285 2.02157E-06 34 32 0.019639669 0.031814967 1.99094E-07 32 40 0.249272727 0.403805356 2.52696E-06 32 31 0.241719008 0.39156883 2.45038E-06 31 30 0.143520661 0.232493993 1.45492E-06 30 29 0.203950413 0.330386201 2.06751E-06 29 26 0.211504132 0.342622727 2.14409E-06 26 27 0.188842975 0.305913149 0 27 28 0.001510744 0.002447305 1.53149E-08 26 25 0.037768595 0.06118263 3.82873E-07 25 17 0.007553719 0.012236526 7.65745E-08 17 18 0.042300826 0.068524545 4.28817E-07 18 19 0.009064463 0.014683831 9.18894E-08 19 20 0.051365289 0.083208376 5.20707E-07 20 21 0.13898843 0.225152077 1.40897E-06 20 22 0.099709091 0.161522143 1.01078E-06 22 23 0.086112397 0.139496396 8.72949E-07 23 24 0.143520661 0.232493993 1.45492E-06 17 16 0.045322314 0.073419156 4.59447E-07 16 15 0.06345124 0.102786818 6.43226E-07 15 14 0.045322314 0.073419156 4.59447E-07 14 14H 0.001510744 0.002447305 1.53149E-08 14 12 0.061940496 0.100339513 6.27911E-07 12 13 0.339917355 0.550643668 3.44585E-06 12 9 0.078558678 0.12725987 7.96375E-07 9 10 0.046833058 0.075866461 4.74762E-07 10 11 0.256826446 0.416041882 2.60353E-06 9 7 0.030214876 0.048946104 3.06298E-07 7 8 0.069494215 0.112576039 7.04485E-07 7 6 0.052876033 0.085655682 5.36022E-07 6 5 1.6210281 2.625958469 1.64329E-05 5 4L 1.577216529 2.554986618 1.59888E-05 4L 3 0.175246281 0.283887402 1.77653E-06 3 2 0.080824793 0.130930828 8.19347E-07 2 1 0.114723141 0.18891979 3.0825E-06 M1 80 0.33938843 0.542385492 1.44125E-05 80 81 0.148052893 0.239835909 1.50086E-06 81 82 0.128413223 0.208020941 1.30177E-06 81 83 0.389771901 0.631404739 3.95124E-06 83 163 0.166181818 0.269203571 1.68464E-06 84 85 0.206971901 0.335280811 2.09814E-06 85 87 0.067983471 0.110128734 6.89171E-07 85 86 0.086112397 0.139496396 8.72949E-07 84 88 0.317256198 0.51393409 3.21613E-06 88 95 0.261358678 0.423383798 2.64948E-06 88 84 0.093666116 0.151732922 9.49524E-07 80 96 0.61185124 0.991158602 6.20253E-06 96 97 0.250783471 0.406252662 2.54227E-06 96 98 0.077047934 0.124812565 7.8106E-07 98 162 0.054386777 0.088102987 5.51336E-07 98 99 0.211504132 0.342622727 2.14409E-06 99 100 0.018884298 0.030591315 1.91436E-07 99 101 0.098198347 0.159074837 9.95469E-07 101 102 0.024171901 0.039156883 2.45038E-07 101 103 0.419986777 0.680350843 4.25754E-06 103 104 0.21452562 0.347517337 2.17472E-06 104 105 0.392793389 0.636299349 3.98187E-06 104 106 0.096687603 0.156627532 9.80154E-07 106 107 0.022661157 0.036709578 2.29724E-07 106 108 0.543867769 0.881029868 5.51336E-06 108 109 0.312723967 0.506592174 0 108 110 0.045322314 0.073419156 4.59447E-07 110 111 0.249272727 0.403805356 2.52696E-06 110 110H 0.03172562 0.051393409 3.21613E-07 103 112 0.403368595 0.653430486 4.08908E-06 112 113 0.160138843 0.25941435 1.62338E-06 113 114 0.007553719 0.012236526 7.65745E-08 114 115 0.126902479 0.205573636 1.28645E-06 112 116 0.090644628 0.146838311 9.18894E-07 116 117 0.225100826 0.364648473 2.28192E-06 117 118 0.01208595 0.019578442 1.22519E-07 118 119 0.013596694 0.022025747 1.37834E-07 118 120 0.191864463 0.310807759 1.94499E-06 116 121 0.143520661 0.232493993 1.45492E-06 121 122 0.280998347 0.455198765 2.84857E-06 121 125 0.746307438 1.208968764 7.56556E-06 111 Technical Loss Reduction in Rural Areas - The Case of Saih Al Khairat 108 Appendix B: Transformer Loading Data Table B1: Feeder 1 data SL. NO Tx. No. Tx. kVA Load (kW) Load (kVAR) 1 58 315 38 29 2 44 315 36 29 3 34 315 34 26 4 21 200 34 25 5 7 315 34 25 6 92 315 67 51 7 93 315 67 50 8 98 315 8 6 9 64 315 67 50 10 66 315 67 50 11 97 315 67 50 12 3G 1000 159 121 13 1G 1000 159 119 14 1G 1000 159 119 15 88G 500 40 30 16 72 315 3 2 17 5G 1000 158 121 18 65 315 35 26 19 67 315 102 77 20 1G 1000 81 61 21 4G 500 81 60 22 3G 1000 33 25 23 2G 500 81 60 Table B2: Feeder 3 data SL. NO Tx. No. Tx. kVA Load (kW) Load (kVAR) 1 83 315 4 3 2 82 315 3 2 3 28 315 4 3 4 18 315 54 40 5 29 315 14 11 6 71 315 2 1 7 59 315 15 11 8 35 68 40 30 9 17 315 40 30 10 16 315 71 54 11 27 315 27 20 12 79 315 33 25 13 81 315 1 1 14 9 315 38 28 15 80 315 16 12 16 104 315 68 51 17 23 315 44 33 18 69 315 127 95 19 26 315 30 22 20 60 315 145 109 21 61 315 88 66 22 53 315 53 40 23 10 315 67 51 Table B3: Feeder 2 data SL. NO Tx. No. Tx. kVA Load (kW) Load (kVAR) 1 14 500 183 137 2 13 100 14 11 3 12 100 0 0 4 50 315 12 9 5 38 200 19 15 6 45 200 40 30 7 2 200 30 22 8 24 200 75 56 9 3 50 7 5 10 19 315 0 0 11 54 315 76 8 12 95 100 0 0 13 39 200 11 34 14 55 315 22 20 15 4A 315 45 16 16 4 315 27 20 17 78 315 21 16 18 1 315 42 31 19 101 200 7 5 20 48 315 55 42 21 11 315 27 20 22 25 315 21 16 23 6 315 99 74 24 49 315 23 17 25 70 315 0 26 22 200 91 68 27 37 315 36 27 28 5 500 94 71 29 5A 200 89 67 30 107 315 0 31 32 315 70 53 32 36 315 11 8 33 57 315 0 0 34 33 315 64 48 35 73 100 39 30 36 41 315 41 31 37 40 315 67 50 38 46 315 51 38 39 15 315 43 32 40 42 315 41 31 41 68 315 18 13 42 62 315 31 24 43 51 315 10 7 44 56 315 0 0 45 43 315 8 6 46 63 315 36 27 47 20 315 113 85 48 30 315 67 50 49 31 315 24 18 112 M.H. Albadi, A.H. Al Maghdri, A.S. Al Hinai, E.F. El-Saadany, M.S. Awladthani, S. Al Hinai, A. Al Jabri, K. Al Azani, and A. Al Maqrashi 109 Appendix C: Single-Line Diagram Figure C1: Feeder 1 SLD. Figure C2: Feeder 3 SLD. Figure C3: Feeder 2 SLD. 113