A SAIWD-Based Approach for Simultaneous Reconfiguration and Optimal Siting and Sizing of Wind Turbines and DVR units in Distribution Systems

Document Type : Research paper


1 Department of Electrical Engineering, Dezful Branch, Islamic Azad University, Dezful, Iran

2 Department of Electrical Engineering, Khorramabad Branch, Islamic Azad University, Khorramabad, Iran

3 Department of Computer Engineering, Shahr-e-Qods Branch, Islamic Azad University, Tehran, Iran


In this paper, a combination of simulated annealing (SA) and intelligent water drops (IWD) algorithm is used to solve the nonlinear/complex problem of simultaneous reconfiguration with optimal allocation (size and location) of wind turbine (WT) as a distributed generation (DG) and dynamic voltage restorer (DVR) as a distributed flexible AC transmission systems (DFACT) unit in a distribution system. The objectives of this research are to minimize active power loss, minimize operational cost, improve voltage stability, and increase the load balancing of the system. For evaluation purposes, the proposed algorithm is evaluated using the Tai-Power 11.4-kV real distribution network. The impacts of the optimal placement of the WT, DVR, and WT with DVR units are separately evaluated. The results are compared in terms of statistical indicators. By comparing all the testing scenarios, it is observed that the multi-objective optimization evolutionary algorithm is more beneficial than its single-objective optimization counterpart. Also, the obtained results show that the proposed SAIWD method outperforms the IWD method and other intelligent search algorithms such as genetic algorithm or particle swarm optimization.


Main Subjects

[1]        H. B. Tolabi, R. Hosseini, M. R. Shakarami “A robust hybrid fuzzy-simulated annealing-intelligent water drops approach for tuning a distribution static compe-nsator nonlinear controller in a distribution system,” Eng. Optim., vol. 48, no. 6, pp. 999-1018, 2016.
[2]        M. Sedighizadeh, M. Mahmoodi “Optimal reconfig-uration and capacitor allocation in radial distribution systems using the hybrid shuffled frog leaping algorithm in the fuzzy framework,” J. Oper. Autom. Power Eng.,vol. 3, no. 1, pp. 56-70, 2015.
[3]        A. Merlin, H. Back “Search for a minimal-loss operat-ing spanning tree configuration in an urban power distribution system,” in Proce. of the PSCC, Cambridge, 1975, pp.1-18.
[4]        K. Nara, A. Shiose, M. Kitagawa, T. Ishihara “Impleme-ntation of genetic algorithm for distribution system loss minimum reconfiguration,” IEEE Trans. Power Delivery, vol. 7, no.3, pp.1044-1051, 1992.
[5]        B. Venkatesh, R. Ranjan “Optimal radial distribution system reconfiguration using fuzzy adaptation of evolutionary programming,” Int. J. Electr. Power Energy Syst., vol. 25, no. 10, pp. 775-780, 2003.
[6]        T. Gözel T, M. Hakan Hocaoglu “An analytical method for the sizing and siting of distributed generators in radial systems,” Electr. Power Syst. Res., vol. 79, no. 6, pp. 912-918, 2009.
[7]        R. Kollu, S. R. Rayapudi, V. L. N. Sadhu “A novel method for optimal placement of distributed generation in distribution systems using HSDO,” Int. Trans. Electric. Energy Syst., vol. 24, pp. 547-561, 2014.
[8]        D. K. Tanti, M. K. Verma, B. Singh, O. N. Mehrotra, “An ANN based approach for optimal placement of DSTATCOM and DVR in power system for voltage sag mitigation under faults,” presented at the AIATA, IT-BHU Varanasi, 2011.
[9]        A. Jain, A. R. Gupta, A. Kumar “An efficient method for D-STATCOM placement in radial distribution system,” in Proce of the IICPE, pp. 1-6, 2014.
[10]     H. B. Tolabi, M. H. Ali, M. Rizwan “Novel hybrid fuzzy-intelligent water drops approach for optimal feeder multi objective reconfiguration by considering multiple-distributed generation,” J. Oper. Autom. Power Eng., vol. 2, no. 2, pp. 91-102, 2014.
[11]     A. Kavousi-Fard, T. Niknam “Multi-objective stochastic distribution feeder reconfiguration from the reliability point of view,” Energy, vol. 64, pp. 342-354, 2014.
[12]     H. B. Tolabi, M. H. Ali, S. B. M. Ayob, M. Rizwan “Novel hybrid fuzzy-Bees algorithm for optimal feeder multi-objective reconfiguration by considering multiple-distributed generation,” Energy, vol. 71, pp. 507-515, 2014.
[13]     R. Srinivasa Rao, K. Ravindra, K. Satish, S. V. L. Narasimham “Power loss minimization in distribution system using network reconfiguration in the presence of distributed generation,” IEEE Trans. Power Syst., vol. 28, no. 1, pp. 317-325, 2013.
[14]     K. Bhumkittipich, N. Mithulananthan “Performance enhancement of DVR for mitigating voltage sag/swell using vector control strategy,” Energy Procedia, vol. 9, pp. 366-379, 2011.
[15]     H. Chen, J. Chen, D. Shi, X. Duan “Power flow study and voltage stability analysis for distribution systems with distributed generation,” in Proc. of the IEEE PES General Meeting, pp. 1-8, 2006.
[16]     K.R. Devabalaji, K Ravi “Optimal size and siting of multiple DG and DSTATCOM in radial distribution system using bacterial foraging optimization algorithm,” Ain Shams Eng. J., vol. 7, no. 3, pp. 959-971, 2016.
[17]     S. Chandramohan, N. Atturulu, R.P. Kumudini Devi, B. Venkatesh “Operating cost minimization of a radial distribution system in a deregulated electricity market through reconfiguration using NSGA method,” Int. J. Electr. Power Energy Syst., vol. 32, no. 2, pp. 126-132, 2010.
[18]     Power System Analysis, 1rded., McGraw-Hill Co., New York, US, 1994.
[19]     N. Metropolis, A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller,  E. Teller “Equation of state calculations by fast computing machines,” J. Chem. Phys, vol. 21, no. 6, pp. 1087-1092, 1953.
[20]     S. Kirkpatrick, C. D. Gelatt, M. P. Vecchi “optimization by simulated annealing,” Science, vol. 220, no. 4598. pp. 671-680, 1983.
[21]     V. Cerny “A thermodynamical approach to the travelling salesman problem: an efficient simulation algorithm,” J. Optimiz. Theory App., vol. 45, pp. 41-51, 1985.
[22]     M. Gandomkar, H. B. Tolabi “Investigation of simulated annealing, ant-colony and genetic algorithms for distrib-ution network expansion planning with distributed generation,” in Proce of the WSEAS, pp. 48-52, 2010.
[23]     S. H. Hosseini “The intelligent water drops algorithm: a nature-inspired swarm-based optimization algorithm,” Int. J. Bio-Inspir Comput., vol. 1, no. 1/2, pp. 71-79, 2009.
[24]     L.W. Oliveira, S. Carneiro, E.J. Oliveira, J.L.R. Pereira, I.C. Silva, J.S. Costa, “Optimal reconfiguration and capacitor allocation in radial distribution systems for energy losses minimization,” Int. J. Electr. Power Energy Syst., vol. 32, pp. 840-848, 2010.
[25]      New Methods To Protect Wind Generators During Voltage Dips Developed, Basque Research, 2015.
[26]     J. Olamaei, T. Niknam, G. Gharehpetian, “Application of particle swarm optimization for distribution feeder reconfiguration considering distributed generators,” Appl. Math. Comput., vol. 201, no.1-2, pp. 575-586, 2008.
Volume 4, Issue 2
December 2016
Pages 93-103
  • Receive Date: 16 September 2015
  • Revise Date: 01 April 2016
  • Accept Date: 25 December 2016
  • First Publish Date: 25 December 2016