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  1. 1. Zuhusna Adilla Binti Ibrahim B011110121 Supervisor : Encik Mohamad Fani bin Sulaima Distribution Network Reconfiguration (DNR) Using Improved Artificial Bee Colony (IABC) For Energy Saving 1
  2. 2. Motivation In Malaysia, the growing industrialization and increasing standard of living has considerably increased the usage of energy. The increasing demand of the electrical energy is quietly related to the power demand. In order to cope the demand of the electricity, the distribution system has become more complex and causing power loss always occurred while distributing the electric. To reduce the power loss, the network distribution system needs to be reconfigured. 2
  3. 3. • The demand for the electricity is rising due to the increasing population group. • The distribution system has become more complex. • The current drawn increasing during the distribution of electricity which lead to the instability. • As the system unstable, the power losses will occur. Problem Statements 3
  4. 4. Research Background Power System Generation Transmission Distribution LoopMeshRadial DNR Act of opening and closing switches Easy to analyze and isolate fault 4
  5. 5. Research Background Optimization Technique Heuristic Artificial Intelligence ABCGAANN Works by mimicking bee behavior of finding food source Optimal Flow Pattern (OFP) Branch Exchange Method (BEM) 5
  6. 6. 6
  7. 7. Scope 7
  8. 8. Previous Work Author Project Title Method Used Description Comment R.J Safri, M.M.A Salama, A.Y Chikhani Distribution System Reconfiguration for Loss Reduction : A New Algorithm based on a set of Quantified Heuristic Rules Quantified Heuristic Rules  Aim to reduce power losses  The method serves as pre- processor by removing the undesirable switching  Does not perform the complex analysis load flow.  This proposed method does not perform the load flow analysis  A new artificial intelligence technique is proposed 8
  9. 9. Author Project Title Method Used Description Comment S. Ganesh Network Reconfiguration of Distribution System Using Artificial Bee Colony Algorithm ABC algorithm technique  Aim to minimize power losses  The ABC is tested on the 33-bus system  Compared with Refined Generic Algorithm (RGA) and Tabu Search Algorithm (TSA)  ABC has the best performance in minimizing power losses.  Does not apply the improved ABC algorithm  Does not improve the voltage profile 9
  10. 10. Author Project Title Method Used Description Comment M. Assadian, M.M Farsangi, Hossein GCPSO in cooperation with graph theory to distribution network reconfiguration for energy saving Guaranteed Convergence Particle Swarm Optimization (GCPSO) and Particle Swarm Optimization (PSO)  Objectives are to reduce power loss and enhancement of voltage profile  Compared with applied GA + GCPSO  Results show that the GA and GCPSO are better than conventional PSO in term of energy saving.  The paper does not show the cost saving  The proposed method does not show the value of energy saved. 10
  11. 11. METHODOLOGY 11
  12. 12. Methodology Start Initialization Phase Employed Bee Phase Onlooker Bee Phase Scout Bee Phase Memorize the best solution Exceed maximum cycle? Stop No Yes Flowchart of ABC 12
  13. 13. Improved Artificial Bee Colony (IABC) Technique • Inspired by the improved strategies of Particle Swarm Optimization (PSO) • An inertial weight w inspired by PSO evolution equation and its improving strategies are added. • The benefits of using this technique are:  Maximize the exploitation capacity  Balanced the exploitation and exploration phase 13
  14. 14. Start Initialization Phase Employed Bee Phase (Weight is added here) Onlooker Bee Phase Scout Bee Phase Memorize the best solution Exceed maximum cycle? Stop No Yes Flowchart of IABC 14
  15. 15. Energy Saving Formulation 15
  17. 17. Test System Analysis 17
  18. 18. • In this system, the 33-bus initial configuration are consists of: • 1 feeder, 32 normally closed tie line and 5 normally open tie lines. • The normally open tie lines are represented by 33, 34, 35, 36 and 37 branches. Sectionalizing Switch Tie Switch Figure 1: IEEE 33-bus radial original network configuration Test System Analysis 18
  19. 19. • The IABC algorithm is tested on 33-bus network system for 30 times. • From the 30 run times, only 12 of them are radial. • The best combination of switches that has been chosen is at 20 because value of power loss at this 20th running times is the lowest which is 107.1 kW and has the fastest computational time (1222.6623s). • The best combination switches are opened at S31, S6, S21, S13 and, S37 Test System Analysis 19
  20. 20. Figure 4.2: The Power Loss after IABC Network Reconfiguration 20
  21. 21. Power Losses 21
  22. 22. Test System Analysis Figure 5.1: Power Loss (kW) Comparison between the Network Reconfiguration 22
  23. 23. Figure 5.2: Loss Reduction Comparison between the Network Reconfiguration 23
  24. 24. Voltage Profile 24
  25. 25. Figure 4.5: Voltage Profile of the Three Network Reconfiguration System 25
  26. 26. Energy Saving & Cost Saving 26
  27. 27. Company SAIDI (Minute) 2008 2009 2010 2011 2012 2013 TNB 68.31 56.72 88.1 63.25 49.30 56.20 Data from SAIDI (TNB) Table 4.3: The Average SAIDI data in Peninsular Malaysia [22] Region Electricity Average Selling Price (sen/kWh) Peninsular Malaysia 33.88 Table 4.4: The Electricity Average Selling Price (sen/kWh) [22] 27
  28. 28. Energy Saving Network Reconfiguration Initial Network ABC IABC Total Power Loss (kW) 202.71 134.26 107.10 Energy (kWh) 4 833.82 3201.56 2553.90 Total loss Cost for one day (RM) 1 637.70 1084.69 865.26 Table 5.2: The total energy and total cost loss in one day 28
  29. 29. Total Cost Loss Figure 5.3: The Monthly Cost Loss of the Network Reconfiguration 29
  30. 30. Figure 5.4: Total Cost Loss for a Year Total Cost Loss 30
  31. 31. Conclusion • IABC algorithm technique has shown a good performance in minimizing the power loss when it is compared to the ABC and other optimization method • Succeeded in reducing the energy losses in the distribution network system • The objectives of this study have been achieved successfully 31
  32. 32. Recommendation • Tested on 14-kV and 69-kV IEEE test bus system in order to get better outcomes and analysis. • To consider the Distribution Generators (DGs) in the future. • To consider the power quality. 32
  33. 33. References [1] R.J Safri, M.M.A Salama and A.Y Chickani, “Distribution system reconfiguration for loss reduction: a new algorithm based on a set of quantified heuristic rules”, Proceedings of Electrical and Computer Engineering, Vol. 1, Canada , pp. 125-130,1994. [2] S. Ganesh, “Network Reconfiguration of Distribution System Using Artificial Bee Colony Algorithm”, International Journal of Electrical, Robotics, Electronics and Communication Engineering, Vol.8, No. 2, pp. 403-409, 2014. [3] M. Assadian, M. M. Farsangi, Hossein Nezamabadi, “GCPSO in cooperation with graph theory to distribution network reconfiguration for energy saving”, Energy Conversion and Management vol. 51,pp. 418-417, 2010. [22] Suruhanjaya Tenaga, Performance and Statistical Information on Electricity Supply Industry in Malaysia, pp. 22-24, 2013. [14] M. Rohani, H. Tabatabaee & A. Rohani, “Reconfiguration Optimization for Loss reduction in Distribution Networks using Hybrid PSO Algorithm and Fuzzy Logic”, MAGNT Research Report, Vol. 2(5), pp. 903-911, 2011 33
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