Hide
Show all
Email this article Optimisasi Multi-objektif pada Rekonfigurasi Jaringan Distribusi Tenaga Listrik dengan Integrasi Pembangkit Terdistribusi Menggunakan Metode Sistem Kekebalan Buatan
(1) Department of Electrical Engineering, Faculty of Engineering, Universitas Muhammadiyah Yogyakarta Jl. Brawijaya, Tamantirto, Kasihan, Daerah Istimewa Yogyakarta, 55183 Indonesia
(2) Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada Jl. Grafika 2, Kampus UGM, Yogyakarta, 55281 Indonesia
Abstract
This study proposes a multi-objective optimization for power distribution network reconfiguration by integrating distributed generators using an artificial immune system (AIS) method. The most effective and inexpensive technique in reducing power losses in distribution networks is optimizing the network reconfiguration. On the other hand, small to medium scale renewable energy power plant applications are growing rapidly. These power plants are operated on-grid to a distribution network, known as distributed generation (DG). The presence of DG in this distribution network poses new challenges in distribution network operations. In this study, the distribution network optimization was carried out using the AIS method. In optimization, the goal to be achieved is not only one objective but should be multiple objectives. Multi-objective optimization aims to reduce power losses, improve the voltage profile, and maintain a maintained network load balance. The AIS method has the advantage of fast convergence and avoids local minima. To test the superiority of the AIS method, the distribution network optimization with and without DG integration was carried out for the 33-bus and 71-bus models of the IEEE standard distribution networks. The results show that the AIS method can produce better system operating conditions than before the optimization. The parameters for the success of the optimization are minimal active power losses, suitable voltage profiles, and maintained load balance. This optimization has successfully increased the efficiency of the distribution network by an average of 0.61%.
Keywords
Full Text:
PDFReferences
R. Syahputra, O. I. Pambudi, F. Mujaahid, and I. Soesanti, “A Study of Sugarcane Waste for Biomass Energy in the Supply of Electrical Energyâ€, Journal of Electrical Technology UMY, vol. 4, no. 1, pp. 28-38, 2020.
N.C. Hien, N. Mithulananthan, and R. C. Bansal, “Location and Sizing of Distributed Generation Units for Loadabilty Enhancement in Primary Feederâ€, IEEE System journal, vol. 7, no. 4, pp. 797-806, 2013.
Y. Chai, L. Guo, C. Wang, Z. Zhao, X. Du, and J. Pan, “Network Partition and Voltage Coordination Control for Distribution Networks With High Penetration of Distributed PV Unitsâ€, IEEE Transactions on Power Systems, vol. 33, no. 3, pp. 3396–3407, 2018.
R. Syahputra, “Distribution Network Optimization Based on Genetic Algorithmâ€, Journal of Electrical Technology UMY, vol. 1, no. 1, pp. 1-9, 2017.
F.C.R. Coelho, W. Peres, I.C.S. Júnior, and B.H. Dias, “Empirical continuous metaheuristic for multiple distributed generation scheduling considering energy loss minimisation, voltage and unbalance regulatory limitsâ€, IET Generation, Transmission & Distribution, vol. 14, no.16, pp. 3301–3309, 2020.
V. Calderaro, A. Piccolo, and P. Siano, “Maximizing DG penetration in distribution networks by means of GA based reconfigurationâ€, International Conference on Future Power Systems, Amsterdam, 2005.
V. Farahani, B. Vahidi, and H.A. Abyaneh, “Reconfiguration and Capacitor Placement Simultaneously for Energy Loss Reduction Based on an Improved Reconfiguration Methodâ€, IEEE Trans. on Power Systems, vol. 27, no. 22, pp. 587-595, 2012.
Merlin and H. Back, “Search for a minimal-loss operating spanning tree configuration in an urban power distribution systemâ€, Proc. 5th PSCC Conference, Cambridge, U.K, pp. 1–18, 1975.
R.S. Rao, S.V.L. Narasimham, M.R. Raju, and A.S. Rao, “Optimal Network Reconfiguration of Large-Scale Distribution System Using Harmony Search Algorithmâ€, IEEE Transaction on Power System, vol. 26, no. 3, pp. 1080–1088, 2011.
Augugliaro, L. Dusonchet, M. Ippolito, and E. R. Sanseverino, “Minimum Losses Reconfiguration of MV Distribution Networks Through Local Control of Tie-Switchesâ€, IEEE Transactions on Power Delivery, vol. 18, no. 3, pp. 762–771, 2003.
B. Enacheanu, B. Raison, R. Caire, O. Devaux, W. Bienia, and N. Hadjsaid, “Radial Network Reconfiguration Using Genetic Algorithm Based on the Matroid Theoryâ€, IEEE Transactions on Power Systems, vol. 23, no. 1, pp. 186-195, 2008.
J. Bao, X. Liu, Z. Xiang, and G. Wei, “Multi-Objective Optimization Algorithm and Preference Multi-Objective Decision-Making Based on Artificial Intelligence Biological Immune Systemâ€, IEEE Access, vol. 8, pp. 160221–160230, 2020.
T. Niknam, H.Z. Meymand, and H.D. Mojarrad, “A practical multi-objective PSO algorithm for optimal operation management of distribution network with regard to fuel cell power plantsâ€, Renewable Energy, vol. 36, pp. 1529-1544, 2011.
N.H. Ahmad, T.K.A. Rahman, and N. Aminuddin, “Multi-objective quantum-inspired Artificial Immune System approach for optimal network reconfiguration in distribution systemâ€, Proceedings of IEEE Conf. on Power Engineering and Optimization Conference (PEOCO), Melaka, Malaysia, 2012.
E.G. Carrano, F.G. Guimaraes, R.H.C. Takahashi, O.M. Neto, and F. Campelo, “Electric Distribution Network Expansion Under Load-Evaluation Uncertainty Using an Immune System Inspired Algorithmâ€, IEEE Transactions on Power Systems, vol. 22, no. 2, pp. 851-861, 2007.
J. Mendoza, R. Lopez, D. Morales, E. Lopez, P. Dessante, and R. Moraga, “Minimal loss reconfiguration using genetic algorithms with restricted population and addressed operatorsâ€, IEEE Trans. on Power Systems, vol. 21, no. 2, pp. 948–954, 2006.
N. Rugthaicharoencheep and S. Sirisumrannukul, “Optimal feeder reconfiguration with distributed generators in distribution system by fuzzy multiobjective and Tabu searchâ€, International Conference on Sustainable Power Generation and Supply, Nanjing, China, 2009.
Y. J. Jeon, J. C. Kim, J. O. Kim, J. R. Shin, and K. Y. Lee, “An Efficient Simulated Annealing Algorithm for Network Reconfiguration in Large-Scale Distribution Systemsâ€, IEEE Trans. on Power Delivery, vol. 17, no. 4, pp. 1070–1078, 2002.
H. Falaghi, M.R. Haghifam, and C. Singh, “Ant Colony Optimization-Based Method for Placement of Sectionalizing Switches in Distribution Networks Using a Fuzzy Multiobjective Approachâ€, IEEE Trans on Power Delivery, vol. 24, no. 1, pp. 268-276, 2009.
R. Syahputra, I. Robandi, and M. Ashari, “Optimization of Distribution Network Configuration with Integration of Distributed Energy Resources Using Extended Fuzzy Multi-objective Methodâ€, International Review of Electrical Engineering (IREE), vol. 9, no.3, pp. 629-639, 2014.
J. Li, Z.M. Liu, C. Li, and Z. Zheng. “Improved artificial immune system algorithm for Type-2 fuzzy flexible job shop scheduling problemâ€, IEEE Transactions on Fuzzy Systems, vol. 2020.
A. Abid, M.T. Khan, and M.S. Khan. “Multidomain Features-Based GA Optimized Artificial Immune System for Bearing Fault Detectionâ€, IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 50, no. 1, pp. 348–359, 2020.
S.S.D. Xu, H.C. Huang, Y.C. Kung, and Y.Y. Chu. “A Networked Multirobot CPS with Artificial Immune Fuzzy Optimization for Distributed Formation Control of Embedded Mobile Robotsâ€, IEEE Transactions on Industrial Informatics, vol. 16, no. 1, pp. 414–422, 2020.
Å. Åšladewski, and K. Åšwirski. “Optimization of a Coal Fired Boiler Using Artificial Immune Systemâ€, IEEE 6th International Conference on Energy Smart Systems (ESS), Kyiv, Ukraine, 17-19 April, 2019.
D. Chen and F. Zhang. “5G Message Service System Based on Artificial Immune Dynamic Adaptive Mechanismâ€, IEEE Access, vol. 7, pp. 91146–91159, 2020.
A. Azizivahed, A. Arefi, S. Ghavidel, M. Shafie-khah, L. Li, J. Zhang, and J.P.S. Catalão. “Energy Management Strategy in Dynamic Distribution Network Reconfiguration Considering Renewable Energy Resources and Storageâ€, IEEE Transactions on Sustainable Energy, vol. 11, no. 2, pp. 662–673, 2020.
B. Singha and B.J. Gyanish. “Impact assessment of DG in distribution systems from minimization of total real power loss viewpoint by using optimal power flow algorithmsâ€, Energy Reports, vol. 4, pp. 407-417, 2018.
I. Dutt, S. Borah, and I.K. Maitra. “Immune System Based Intrusion Detection System (IS-IDS): A Proposed Modelâ€, IEEE Access, vol. 8, pp. 34929–34941, 2020.
C.C.B. Fioravanti, T.M. Centeno, and M.R. Delgado. “A Deep Artificial Immune System to Detect Weld Defects in DWDI Radiographic Images of Petroleum Pipesâ€, IEEE Access, vol. 7, pp. 180947-180964, 2020.
F.T.S. Silva, L.R. Araujo, and D.R.R. Penido, “Optimal Substation Placement in Distribution Systems using Artificial Immune Systemsâ€, IEEE Latin America Transactions, vol. 16, no. 2, pp. 505–513, 2018.
Y. Wang, Y. Xu, J. Li, J. He, and X. Wang. “On the Radiality Constraints for Distribution System Restoration and Reconfiguration Problemsâ€, IEEE Transactions on Power Systems, vol. 35, no. 4, pp. 3294–3296, 2020.
Z. Yin, X. Ji, Y. Zhang, Q. Liu, and X. Bai. “Data-driven approach for real-time distribution network reconfigurationâ€, IET Generation, Transmission & Distribution, vol. 14, no. 13, pp. 2450–2463, 2020.
M.A. Muhammad, H. Mokhlis, K. Naidu, A. Amin, J.F. Franco, and M. Othman. “Distribution Network Planning Enhancement via Network Reconfiguration and DG Integration Using Dataset Approach and Water Cycle Algorithmâ€, Journal of Modern Power Systems and Clean Energy, vol. 8, no. 1, pp. 86–93, 2020.
Refbacks
- There are currently no refbacks.
