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Distribution system is a critical link between the electric power distributor and the consumers. Most of the distribution networks commonly used by the electric utility is the radial distribution network. However in this type of network, it has technical issues such as enormous power losses which affect the quality of the supply. Nowadays, the introduction of Distributed Generation (DG) units in the system help improve and support the voltage profile of the network as well as the performance of the system components through power loss mitigation. In this study network reconfiguration was done using two meta-heuristic algorithms Particle Swarm Optimization and Gravitational Search Algorithm (PSO-GSA) to enhance power quality and voltage profile in the system when simultaneously applied with the DG units. Backward/Forward Sweep Method was used in the load flow analysis and simulated using the MATLAB program. Five cases were considered in the Reconfiguration based on the contribution of DG units. The proposed method was tested using IEEE 33 bus system. Based on the results, there was a voltage profile improvement in the system from 0.9038 p.u. to 0.9594 p.u.. The integration of DG in the network also reduced power losses from 210.98 kW to 69.3963 kW. Simulated results are drawn to show the performance of each case.

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- 1. https://iaeme.com/Home/journal/IJEET 1 editor@iaeme.com International Journal of Electrical Engineering and Technology (IJEET) Volume 13, Issue 7, July 2022, pp. 1-13, Article ID: IJEET_13_07_001 Available online at https://iaeme.com/Home/issue/IJEET?Volume=13&Issue=7 ISSN Print: 0976-6545 and ISSN Online: 0976-6553 © IAEME Publication Scopus Indexed OPTIMAL RECONFIGURATION OF POWER DISTRIBUTION RADIAL NETWORK USING HYBRID META-HEURISTIC ALGORITHMS Jamali A. Nagamora1*, Reuel C. Pallugna2, Noel R. Estoperez3 1,2 College of Engineering and Architecture, University of Science and Technology of Southern Philippines, Cagayan de Oro City, Philippines 3 College of Engineering and Technology, Mindanao State University-Iligan Institute of Technology, Iligan City, Philippines *Corresponding Author ABSTRACT Distribution system is a critical link between the electric power distributor and the consumers. Most of the distribution networks commonly used by the electric utility is the radial distribution network. However in this type of network, it has technical issues such as enormous power losses which affect the quality of the supply. Nowadays, the introduction of Distributed Generation (DG) units in the system help improve and support the voltage profile of the network as well as the performance of the system components through power loss mitigation. In this study network reconfiguration was done using two meta-heuristic algorithms Particle Swarm Optimization and Gravitational Search Algorithm (PSO-GSA) to enhance power quality and voltage profile in the system when simultaneously applied with the DG units. Backward/Forward Sweep Method was used in the load flow analysis and simulated using the MATLAB program. Five cases were considered in the Reconfiguration based on the contribution of DG units. The proposed method was tested using IEEE 33 bus system. Based on the results, there was a voltage profile improvement in the system from 0.9038 p.u. to 0.9594 p.u.. The integration of DG in the network also reduced power losses from 210.98 kW to 69.3963 kW. Simulated results are drawn to show the performance of each case. Key words: Particle Swarm Optimization, Gravitational Search Algorithm, Metaheuristic, Radial Distribution, Optimization, Load Flow Analysis, Reconfiguration, Distributed Generation, Voltage Profile. Cite this Article: Jamali A. Nagamora, Reuel C. Pallugna and Noel R. Estoperez, Optimal Reconfiguration of Power Distribution Radial Network using Hybrid Meta- heuristic Algorithms, International Journal of Electrical Engineering and Technology (IJEET). 13(7), 2022, pp. 1-10. https://iaeme.com/Home/issue/IJEET?Volume=13&Issue=7
- 2. Optimal Reconfiguration of Power Distribution Radial Network using Hybrid Meta-heuristic Algorithms https://iaeme.com/Home/journal/IJEET 2 editor@iaeme.com 1. INTRODUCTION Electrical distribution system plays a vital part in power system. It has to be designed efficiently to fulfill economic, reliability and even environmental objectives. While there were a bunch of studies devoted to generation stations and transmission system, the distribution level seems to have less attention. For the past years, most innovations were focused to the devices or equipment because the electrical distribution networks do not require special research and development. With the rapid construction of new infrastructures, the investment and update for distribution network is relatively backward. In effect the quality of electric supply to the user is being compromised. Loss of supply (outage) can be considered as cost factor for operation, especially in commercial or industrial entity. The distribution network must be designed efficiently to keep the voltage value within a given range for the correct operation of appliances and minimize the voltage drop on the network. Nowadays, our electrical networks are undergoing tremendous changes. The introduction of Renewable Energy Sources (RES), Plug-in Hybrid Electric Vehicle (PHEV) as well as the smart end-users systems makes our distribution system very dynamic, changing the new paradigm of energy. Some of the countries like France, 95% of these sources are located in distribution level while the concept for smart grid were applied to their system (Hung et al,. (2013). Thereupon, classical distribution network should also evolve, and accurate Reconfiguration must be done to optimize capital investment programs, efficient operations, reduce outages times and maintenance of equipment. These needs to be considered during the designing and planning of distribution network. The integration also of Distribution Generators (DG) units introduces uncertainties and dynamic operation in the system. To this aim, some optimization algorithms are utilized to find the optimal placement and size of distribution generators (DG) as well as the opening and closing of tie switches on buses. According to (Shahnia, Arefi, & Ledwich, 2018), the commonly used for solving mixed-integer nonlinear programming problem, includes mathematical and heuristic-based algorithms. Determining the optimal Distribution Network Reconfiguration (DNR) account for different constraint such as voltage limits, maximum permissible line current carrying capacity and network structure of the system. 2. METHODOLOGY In this study the standard IEEE 33 bus system in figure 1 will be used as the test network for validation of reconfiguration method and results. The test network has 32 lines, 33 buses and 5 tie switches (Baran & Wu, 1989). Distribution networks are usually built as interconnected networks, while in operation, they are arranged into a radial tree-like structure. This means that distribution systems are divided into subsystems of radial feeders, which contain a number of normally closed switches and normally open switches. Initially, the open line switches before Reconfiguration are 33, 34, 35, 36 and 37. The test network has 3,715 kW and 2300 kVAR of load with a base voltage of 12.66 kV. In the Reconfiguration of the system, the proposed hybrid algorithms were used to determine the optimum switching sequence and size of DG units in the network. Sensitivity factor and index method (Belwin & Suresh, 2018) was applied to determine the optimal locations of DG units in the network. The number of DG units integrated in the radial system was assumed to have unity power factor. Voltage constraints were within ±5% of the nominal value. DG capacities were limited from 20% to 85% of the total power load demands of the radial distribution networks to avoid the reverse power flow in the network. The following cases were considered in this study:
- 3. Jamali A. Nagamora, Reuel C. Pallugna and Noel R. Estoperez https://iaeme.com/Home/journal/IJEET 3 editor@iaeme.com Case 1: The base network before Reconfiguration Case 2: After Reconfiguration of tie switches by PSO without DG’s. Case 3: After Reconfiguration by of tie switches by PSO with DG’s. Case 4: After Reconfiguration of tie switches by PSOGSA without DG’s. Case 5: After Reconfiguration of tie switches by PSOGSA with DG’s Figure 1 IEEE 33 Bus Test Network 2.1. Network Reconfiguration The general reconfiguration flowchart is shown in Figure 2 where the sequence is presented. The first step is to read the IEEE 33 bus system data in the MATLAB program such as the line impedance, loads and voltage. Second is to locate the fault (opened switch) in the system and initial configuration of tie and sectionalizing switches. Third, the backward and forward load flow analysis (Rupa & Ganesh, 2014) will be done on the system to compute for current flow, the voltage profile and power system loss. Fourth, the PSO-GS algorithm (Mirjalili & Mohd Hashim, 2010) program will be run to determine the optimal closing/opening of tie switches as well as the size and placement of distributed generator (DG) in the network.
- 4. Optimal Reconfiguration of Power Distribution Radial Network using Hybrid Meta-heuristic Algorithms https://iaeme.com/Home/journal/IJEET 4 editor@iaeme.com (1) Figure 2 General Reconfiguration Flowchart 2.3. Formulation of Objective Functions The objectives for reconfiguring the radial network are to minimize power loss and improve voltage profile. In network reconfiguration, some constraints have to be identified in order to maintain the topology of the network. For 33-bus system, it should always have 5 tie switches and 32 sectionalizing switch. Some of the functions were utilized. Minimize the total power losses ( f ), P(Tloss) Determine the feeder’s power limits for each nth branch (Atteya, Ashour, Fahmi, & Strickland, 2016); Read data of distribution system (bus, loads, branch data) Identify fault and initial configuration of the systems (tie/ sectionalizing Run Load Flow Program for the system Compute current flow, the voltage profile, power system loss. Print output result Change of tie/sectionalizing switches and DG in the distribution system using the proposed algorithms Objectives & constraints attained? Yes No
- 5. Jamali A. Nagamora, Reuel C. Pallugna and Noel R. Estoperez https://iaeme.com/Home/journal/IJEET 5 editor@iaeme.com (2) (3) (4) (5) (6) The radial network topology; where; Rn : line resistance. Qn : Reactive power value. Pn : Real power value. Vm : Node voltage value. kn : Status for each branches (if kn = 1 means closed and if kn=0 means open). NL: Number of branches Nbus: Number of Buses 3. RESULTS AND DISCUSSION 3.1. Reconfigurations without DG Units The network reconfigurations in the absence of DG units are represented by case 2 and case 4. Table 1 shows the summary of load flow results for Case 1, 2 and 4. The initial value of Case 1 was used as the reference for the comparison of two algorithms. At 100% loading the initial total power loss of the system is found to be 210.9876 kW. After the Reconfiguration by Case 2 (PSO), the optimal switch sequence for new topology were 28, 32, 14, 9 and 7. The power loss had been reduced to 139.9814, amounting 33.65% of reduction from the initial network. For Case 4 (PSOGSA), the switch sequence were the bus 37, 32, 14, 9 and 7. The power loss from the initial network had been reduced further to 139.5301 kW amounting 33.87%. Table 1 Network reconfiguration without DG units Parameters Case 1 (Initial) Case 2 (PSO) Case 4 (PSOGSA) Opened Switches 33, 34, 35, 36, 37 28, 32, 14, 9, 7 37, 32, 14, 9, 7 Real power Loss (kW) 210.9876 139.9814 139.5301 Loss reduction (%) ---- 33.65 33.87 Time (s) --- 18.32 6.73 Lowest Voltage (p.u) 0.9044 (node : 20) 0.9385 (node : 32) 0.9419 (node : 32) Figure 3 shows the summary of power losses for each bus in the network at different case scenarios. The simulation was performed at 100% loading without DG units. Notice in the figure that the majority of buses of Case 1 have significant power loss. But when case 2 and case 4 were applied, almost 85% of buses had been reduced of power loss except for Bus 3, 5, 22, 23, 25, 30, 31, and 32 wherein there were a slight increase in power loss due to transfer of load to other feeders.
- 6. Optimal Reconfiguration of Power Distribution Radial Network using Hybrid Meta-heuristic Algorithms https://iaeme.com/Home/journal/IJEET 6 editor@iaeme.com Figure 3 Comparison of power losses in each bus for different Cases Figure 4 shows the summary of the voltage profile of the network before and after reconfigurations without DG units. There is a significant improvement of voltages from bus 1 to bus 19 when Case 2 and Case 4 were applied to the network. But due to the changes made in the network topology, certain buses like bus 20, 21, and 22 had a slight voltage drop from these two cases. However, in bus 23 to 33, the two algorithms were able to recover and improve the voltages between these buses. Figure 4 Summary Voltage profile of the network for different Cases 0.0 10.0 20.0 30.0 40.0 50.0 60.0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223242526272829303132 Power Loss (kW) Bus Number Power Loss of each bus CASE 1 CASE 2 CASE 4 0.8400 0.8600 0.8800 0.9000 0.9200 0.9400 0.9600 0.9800 1.0000 1.0200 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 Voltage (p.u.) Bus Number Voltage profile of network after reconfiguration CASE 1 CASE 2 CASE 4
- 7. Jamali A. Nagamora, Reuel C. Pallugna and Noel R. Estoperez https://iaeme.com/Home/journal/IJEET 7 editor@iaeme.com 3.2. Results for Optimal Placement of DG Units The optimal positions of Distributed Generator was determined using sensitivity index method. Table 2 shows the results of optimal placement and size of DG unit without the Reconfiguration of the network. Using the PSO algorithm, the optimal placements for DG units were in bus 8, 13, and 32 having a capacities of 981.6 kW, 982.7 kW and 1176.8 kW respectively. In PSOGSA algorithm the optimal placements for DG units were in bus 8, 13 and 31 with a capacities of 500 kW, 600 kW and 810 kW respectively. Figure 4.7 shows the performance summary of two algorithms when DG units were installed in the system. PSOGSA algorithm had the total power loss reduction of 83.8401 kW amounting 60.26% of initial power loss in the network. While the PSO algorithm had the total power loss in the system of 105.35 kW amounting 50.06% of reduction of loss. Table 2 Summary of Optimal size and placement of DG units Parameters Before Optimization After Optimization Initial PSO PSOGSA Total Power Loss (kW) 210.9876 105.3524 83.8401 Loss Reduction % --- 50.0612 60.2616 Worst bus voltage 0.9038 (node : 20) 0.9806 (30) 0.9661 (18) Optimal location (bus#) and size of DGs (kW) --- (13) 981.6 (32) 982.7 (8) 1176.8 (8) 500 (13) 600 (31) 810 Total DG (kW) --- 2988.1 1910 DG power factor --- unity unity Figure 5 also shows the comparison of two algorithms when DG units only are considered in the network. The introduction of DG units contribute significantly in the reduction of power loss in each branch. Almost every branch in the network had been reduced except for bus 10, 15, 30, 31, wherein the PSO algorithm did not perform well from these buses. Figure 5 Power Loss of each bus by optimal placement and sizing of DG units The optimal placement and sizing also of DG units had improved the voltage profile of the network as depicted in figure 6. The PSO algorithm did a satisfying improvement in voltage profile of network which had recorded a lowest bus voltage of 0.9806 p.u at bus 30. While the
- 8. Optimal Reconfiguration of Power Distribution Radial Network using Hybrid Meta-heuristic Algorithms https://iaeme.com/Home/journal/IJEET 8 editor@iaeme.com PSOGSA algorithm, had recorded the lowest voltage of 0.9661 p.u at bus 18. Though big difference of voltage profile can be observed in the figure, the PSOGSA utilized only 1,910 kW from DG units while the PSO had utilized 2988.1 kW of DG units. Figure 6 Comparison of Voltage Profile of Network considering DG units. 3.3. Results for Reconfigurations with DG units In this section multi-objective optimization was performed to determine the optimal switching and allocation of DG units for loss minimization in the network. As shown in Table 3, the results for the proposed algorithm and PSO algorithm had reduced the initial power loss of network by 67.11% and 66.03% respectively. Both had the same placements for DG units (6, 12, 16, and 31) and opened switches (9, 14, 28, 32, and 33). Table 3 Comparison of two algorithms in network reconfiguration considering DG units Parameters Before Optimization Reconfiguration + DG Initial (Case 1) PSO (Case 3) PSOGSA (Case 5) Switch 33, 34, 35, 36, 37 9, 14, 28, 32, 33 9, 14, 28, 32, 33 Total Loss (kW) 210.9876 71.6689 69.3963 Loss Reduction % --- 66.03 67.11 Worst bus voltage 0.9038 (node : 20) 0.9590 (33) 0.9594 (33) Optimal location (bus#) and size of DGs (kW) --- (6) 499.5 (12) 499.5 (16) 400.5 (31) 499.5 (6) 499.5 (12) 499.5 (16) 499.5 (31) 499.5 Total DG (kW) --- 1899 1998 DG power factor --- unity unity In figure 7, all branches except for branch 22, 25, 26 and 30 had reduced the initial power loss in the system when Reconfiguration with DG units were applied. The total power loss in the system for Case 3 and Case 5 are 71.6689 and 69.3963kW respectively.
- 9. Jamali A. Nagamora, Reuel C. Pallugna and Noel R. Estoperez https://iaeme.com/Home/journal/IJEET 9 editor@iaeme.com Figure 7 Comparison of power losses in each branch of two cases The improvement of voltage profile from the initial network topology is shown in figure 8. It can be observed that two algorithms had almost the same profile having the bus 33 as the worst voltage dropped. Figure 8 Comparison of Case 3 and Case 5 voltage profile 3.4. Results for Convergence Rate The comparisons of convergence rate for PSO and a hybrid PSOGSA is shown in figure 9. Notice that proposed algorithm (PSOGSA) converge much faster than the widely known PSO algorithm. Simulation iterations were set to 50 to allow the algorithms to find the better solution and the optimum fitness. Based on the figure, the PSOGSA algorithm converged at 8 iteration only while PSO settled at 32 iterations.
- 10. Optimal Reconfiguration of Power Distribution Radial Network using Hybrid Meta-heuristic Algorithms https://iaeme.com/Home/journal/IJEET 10 editor@iaeme.com Figure 9 Convergence rate of two Algorithms 4. CONCLUSION AND RECOMMENDATIONS 4.1. General Conclusion Load flow analysis using Backward-Forward Sweep method was successfully implemented and tested in different cases of network. Based on the results, the proposed hybrid algorithm has been proven to be effective in determining the optimal Reconfiguration of power distribution network. The combination of both meta-heuristic algorithms can be more powerful compared to using a single algorithm only. The speed of convergence rate must be considered during the process, as these could be the criteria in making modern switching devices. The advantage of PSOGSA from other algorithm is that as the particles increases in size, it becomes more aggressive in finding optimum values. The optimal placement and sizing of DG units, helped also in the improvement of voltage profile and power quality of the network. However, optimal position of DG units should be considered in this study to obtain an efficient operation of the system. As shown in results Case 5 had 50.26% more power loss reduction from Case 4, while Case 3 had only 48.80% power loss reduction from Case 2. From graphical presentations of results, we can deduce that the proposed algorithm outperformed in voltage profile and power quality enhancement of test network. 4.2. Recommendations In this study, it is recommended to consider the optimal placement for capacitor bank, dynamic loads, DG operation modes, penetration level of DG and economic analysis of the system. It is recommended also to perform reliability study for this reconfigured network and test the algorithm into existing utility distribution network. To evaluate also the actual performance of the algorithm, the succeeding researcher shall create miniature smart distribution network which will mimic the modern utility distribution network. The proposed algorithm can be developed into firmware which will be embedded into the controller unit of the system. In addition, there are still more metaheuristic algorithms published which can be further explore, study its characteristics and determine the possibility of combining it into other algorithm to produce more efficient and powerful algorithm.
- 11. Jamali A. Nagamora, Reuel C. Pallugna and Noel R. Estoperez https://iaeme.com/Home/journal/IJEET 11 editor@iaeme.com ACKNOWLEDGMENT The researchers would like to their gratitude to the University of Science and Technology of Southern Philippines, Cagayan de Oro City, Philippines. REFERENCES [1] Hung, D. Q., & Mithulananthan, N. (2013). Multiple Distributed Generator Placement in Primary Distribution Networks for Loss Reduction. Ieee Transactions On Industrial Electronics, 60(4), 1700. [2] Sharma, S., Bhattacharjee, S., & Bhattacharya, A. (214). Optimal Location and Sizing of DG to Minimize Loss of Distribution System Using SIMBO-Q Method. 2014 International Conference on Control, Instrumentation, Energy & Communication, 340. [3] Abedinia, O., Amjady, N., & Ghasemi, A. (2014). A New Metaheuristic Algorithm Based on Shark Smell Optimization. Wiley Online Libr., 21(5), 97-116. [4] Atteya, I. I., Ashour, H. A., Fahmi, N., & Strickland, D. (2016). Distribution network reconfiguration in smart grid system using modified particle swarm optimization. IEEE Int. Conf. Renew. Energy Res. Appl. ICRERA 2016, 5, 305-313. [5] Azizivahed, A., Narimani, H., Naderi, E., Fathi, M., & Narimani, M. R. (2017). A hybrid evolutionary algorithm for secure multi-objective distribution feeder reconfiguration. Energy, 138, 355-373. [6] Badran, O., Mekhilef, S., & Dahalan, W. (2017). Optimal switching sequence path for distribution network reconfiguration considering different types of distributed generation. IEEJ Trans. Electr. Electron. Eng, 12(6), 874-882. [7] Balamurugan, K., & Srinivasan, D. (2011). Review of the power flow studies on distribution network with distributed generation." Power Electronics and Drive Systems (PEDS). 2011 IEEE Ninth International Conference. IEEE, 2011. [8] Baldwin, J. L. (2011). Reduce Losses in the Transmission and Distribution System. Retrieved Jan 15, 2018, from http://www.raponline.org/ document/download/id/4537: http://www.raponline.org/ document/download/id/4537 [9] Baran, M., & Wu, F. (1989). Network Reconfiguration in Distribution Systems For Loss Reduction and Load Balancing. Berkely California: IEEE transaction on Power Delivery. [10] Belwin, E. J., & Suresh, M. V. (2018). Optimal DG placement for benefit maximization in distribution networks by using DragonFly Algorithm. Renewables:wind, water, solar, 5(4), 8. [11] Boillot, M. (2014). Advanced Smartgrids for Distribution System Operators. USA: John Wiley and Son. [12] Charles, D., & Ravichandran, S. (2005). Reduction Using Ant Colony System Algorithm. IEEE Indicon 2005 Conf, 1-4. [13] Dahalan, W., & Mokhlis, H. (2012). Network reconfiguration for loss reduction with distributed generations using PSO. IEEE Int. Conf. Power Energy (PECon), 823-828. [14] Feng, N., & Jianming, Y. (2009). Line losses calculation in distribution network based on RBF neural network optimized by Hierarchical GA. 1st Int. Conf. Sustain. Power Gener. Supply, SUPERGEN ’09. [15] Glover, J. D., Sarma, M., & Overbye, J. T. (2008). Power Systems Analysis and Design_ Glover.pdf. Toronto: Chris Carson. [16] Gnanasekaran, N., Chandramohan, S., Kumar, P., & Imran, A. M. (2016). Optimal placement of capacitors in radial distribution system using shark smell optimization algorithm. Ain Shams Eng. J., 7(2), 907-916.
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