Market-based Method for Reconfiguration of Distribution Networks Using Mine Blast Algorithm (MBA)
الموضوعات : Majlesi Journal of Telecommunication DevicesSajjad Niroomand 1 , Alireza Bakhshinejad 2 , Mehdi Tabasi 3
1 - Department of electrical engineering, Sowmesara branch, Islamic Azad University, Sowmesara
2 - Somesara Branch, Islamic Azad University
3 - Department of electrical engineering, Sowmesara branch, Islamic Azad University, Sowmesara
الکلمات المفتاحية: reconfiguration of distribution networks, Demand Response, mine blast optimization algorithm,
ملخص المقالة :
Today, reduction of losses and operational costs is considered an important issue in power systems. Demand response program causes diminished consumption during peak hours and thus increased reliability and reduced costs. Reconfiguration of distribution networks are among the practical methods in reducing losses and costs as well as improving the voltage profile. In this paper, the reconfiguration of distribution networks is performed considering demand response potential and in the presence of distribution generated (DG) sources using a new optimization algorithm called mine blast optimization algorithm (MBA). For this purpose, reducing losses, improving voltage profile, and lowering operational costs of the network are also taken into account as objective function. The proposed method is applied on 33-bus radial network. Simulation has been performed using MATLAB software.
[1] C. Su and C. Lee, “Feeder reconfiguration and capacitor setting for loss reduction of distribution systems,” Electric Power Systems Research, vol. 58, pp. 97–102, 2001.
[2] S. Kalambe and G. Agnihotri, “Loss minimization techniques used in distribution network: bibliographical survey,” Renewable and Sustainable Energy Reviews, vol. 29, pp. 184-200, 2014.
[3] U.S. Department of Energy, "Energy Policy Act of 2005", secton 1252, February 2006.
[4] Y. Ma, F. Liu, X. Zhou and Z. Gao, “Overview on Algorithms of Distribution Network Reconfiguration,” Proceedings of the 36th Chinese Control Conference, China, 2017.
[5] D. Jakus, R. Čađenović, M. Bogdanović, P. Sarajčev and J. Vasilj, “Distribution network reconfiguration using hybrid heuristic — Genetic algorithm,” 2nd International Multidisciplinary Conference on Computer and Energy Science (SpliTech), Croatia, 2017.
[6] R. Čađenović, D. Jakus, P. Sarajčev and J. Vasilj, “Optimal reconfiguration of distribution network using cycle-break/genetic algorithm,” IEEE Manchester PowerTech, UK, 2017.
[7] M. R. M. Cruz, S. F. Santos, D. Z. Fitiwi and J. P. S. Catalão, “Coordinated distribution network reconfiguration and distributed generation allocation via genetic algorithm,” IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe) Environment and Electrical Engineering, Italy, 2017.
[8] D. Shu, Z. Huang, J. Li and X. Zou, “Application of Multi-Agent Particle Swarm Algorithm in Distribution Network Reconfiguration,” Chinese Journal of Electronics, vol. 25, pp. 1179 - 1185, 2016.
[9] I. I. Atteya, H. A. Ashour, N. Fahmi and D. Strickland, “Distribution network reconfiguration in smart grid system using modified particle swarm optimization,” IEEE International Conference on Renewable Energy Research and Applications (ICRERA), UK, 2016.
[10] M. Andervazh, J. Olamaei and M. Haghifam, “Adaptive multi-objective distribution network reconfiguration using multi-objective discrete particles swarm optimisation algorithm and graph theory,” IET Gener. Transm. Distrib., vol. 7, pp. 1367–1382, 2013.
[11] R. Cheng, L. Xu, Y. Liu and J. Gao, “Distribution Network Reconfiguration Based on Adaptive Bi-Group Particle Swarm Algorithm,” 8th International Symposium on Computational Intelligence and Design (ISCID), China, 2015.
[12] J. Zhu, Z. Wu, P. Jiang, S. Song, J. Ren, W. Sheng, K. Liu and W. Gu, “An improved PSO algorithm based on statistics for distribution network reconfiguration to increase the penetration of distributed generations,” IET International Conference on Resilience of Transmission and Distribution Networks (RTDN), UK, 2015.
[13] S. R. Tuladha, J. G. Singh and W. Ongsakul, “Multi-objective approach for distribution network reconfiguration with optimal DG power factor using NSPSO,” IET Gener. Transm. Distrib., vol. 10, pp. 2842–2851, 2016.
[14] T. H. Chang, T. E. Lee and C. H. Lin, “Distribution network reconfiguration for load balancing with a colored Petri net algorithm,” International Conference on Applied System Innovation (ICASI), Japan, 2017.
[15] M. Mosbah1, S. Arif1, R. D. Mohammedi and A. Hellal, “Optimum Dynamic Distribution Network Reconfiguration using Minimum Spanning Tree Algorithm,” The 5th International Conference on Electrical Engineering – Boumerdes (ICEE-2017), Algeria, 2017.
[16] L. S. M. Guedes, A. C. Lisboa, D. A. G. Vieira, and R. R. Saldanha, “A Multiobjective Heuristic for Reconfiguration of the Electrical Radial Network,” IEEE Trans. on Power Delivery, vol. 28, 2013.
[17] A. Tiguercha, A. A. Ladjici and M. Boudour, “Optimal radial distribution network reconfiguration based on multi objective differential evolution algorithm,” IEEE Manchester PowerTech, UK, 2017.
[18] A. Mendes, N. Boland, P. Guiney, and C. Riveros, “Switch and Tap-Changer Reconfiguration of Distribution Networks Using Evolutionary Algorithms,” IEEE Trans. on Power System, vol. 28, 2013.
[19] F. Scenna, D. Anaut, L. Passoni, and G. Meschino, “Reconfiguration of Electrical Networks by an Ant Colony Optimization Algorithm,” IEEE Latin America Trans., vol. 11, 2013.
[20] S. Jazebi, M. Moghimi Hadji, and R. A. Naghizadeh, “Distribution Network Reconfiguration in the Presence of Harmonic Loads: Optimization Techniques and Analysis,” IEEE Trans. on Smart Grid, vol. 5, 2014.
[21] J.E. Mendoza, M.E. Lo´pez, C.A. Coello Coello and E.A. Lo´pez, “Microgenetic multiobjective reconfiguration algorithm considering power losses and reliability indices for medium voltage distribution network,” IET Generation, Transmission & Distribution, vol. 3, pp. 825–840, 2009.
[22] M. Rostami, A. Kavousi-Fard, and T. Niknam, “Expected Cost Minimization of Smart Grids with Plug-In Hybrid Electric Vehicles Using Optimal Distribution Feeder Reconfiguration,” IEEE Trans. on Industrial Informatics, vol. 11, pp. 388-397, 2015.
[23] A. Zakariazadeh, S. Jadid and P. Siano, “Smart microgrid energy and reserve scheduling with demand response using stochastic optimization,” Electric Power Systems Research, vol. 63, pp. 523–533, 2014.
[24] R. T. Ganesh Vulasala and S. Sirigiri, “Feeder Reconfiguration for Loss Reduction in Unbalanced Distribution System Using Genetic Algorithm,” Int. J. Electr. Electron. Eng., vol. 3, pp. 754–762, 2009.
[25] F. V. Gomes, S. Carneiro, J. L. R. Pereira, M. P. Vinagre, P. A. N. Garcia, and L. R. Araujo, “A New Heuristic Reconfiguration Algorithm for Large Distribution Systems,” IEEE Trans. Power Syst., vol. 20, pp. 1373–1378, 2005.
