Optimal Active Distribution Network Reconfiguration for Loss and Supply Cost Minimization Using Grey Wolf Algorithm

Document Type : Original Article


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

2 Department of Electrical Engineering, Gorgan Branch, Islamic Azad University, Gorgan, Iran.

3 Department of Electrical Engineering, Faculty of Engineering, Golestan University, Gorgan, Iran.


In this paper, a novel reconfiguration approach for distribution network incorporating distributed generation is introduced aiming to minimize power losses and energy supply costs. Given the temporally variable consumption of residential, industrial and commercial loads and the time-variant energy prices, an hourly reconfiguration scheme for an entire daily cycle is proposed. Also considering the privately-owned nature of distributed sources, the energy supply is carried out within a competitive market. The optimization is based on grey wolf algorithm (GWO), implemented in MATLAB software on an IEEE 33-bus test network. The simulation is done for four scenarios with respective objective functions for the evaluations of the results thereof. By comparing the obtained results it is concluded the configuration of network will be the unique for each of objective function. Finally, the effects of switching at different hours of day are compared in terms of loss minimization and supply costs against single daily switching scheme.


[1] Abdelaziz, A.Y., et al., Distribution system reconfiguration using a modified Tabu Search algorithm. Electric Power Systems Research, 2010. 80(8): p. 943-953.
[2] Khoa, T. and B. Phan. Ant colony search-based loss minimum for reconfiguration of distribution systems. in 2006 IEEE Power India Conference. 2006. IEEE, April 2006.
[3] Franco, J.F., et al., A mixed-integer LP model for the reconfiguration of radial electric distribution systems considering distributed generation. Electric Power Systems Research, 2013. 97: p. 51-60.
[4] Nguyen, T.T., A.V. Truong, and T.A. Phung, A novel method based on adaptive cuckoo search for optimal network reconfiguration and distributed generation allocation in distribution network. International Journal of Electrical Power & Energy Systems, 2016. 78: p. 801-815.
[5] Mendoza, J., et al., Microgenetic multi objective reconfiguration algorithm considering power losses and reliability indices for medium voltage distribution network. IET Generation, Transmission & Distribution, 2009. 3(9): p. 825-840.
[6] Gupta, N., A. Swarnkar, and K. Niazi, Distribution network reconfiguration for power quality and reliability improvement using Genetic Algorithms. International Journal of Electrical Power & Energy Systems, 2014. 54: p. 664-671.
[7] Tian, Z., et al., Mixed-integer second-order cone programing model for VAR optimisation and network reconfiguration in active distribution networks. IET Generation, Transmission & Distribution, 2016. 10(8): p. 1938-1946.
[8] Capitanescu, F., et al., Assessing the potential of network reconfiguration to improve distributed generation hosting capacity in active distribution systems. IEEE Transactions on Power Systems, 2014. 30(1): p. 346-356.
[9] Liang, W., et al. A novel reconfiguration strategy for active distribution network considering maximum power supply capability. in Applied Mechanics and Materials. 2014. Trans Tech Publ.448: p.2747-2752.
[10] Esmaeili, M., M. Sedighizadeh, and M. Esmaili, Multi-objective optimal reconfiguration and DG (Distributed Generation) power allocation in distribution networks using Big Bang-Big Crunch algorithm considering load uncertainty. Energy, 2016. 103: p. 86-99.
[11] Farahani, V., B. Vahidi, and H.A. Abyaneh, Reconfiguration and capacitor placement simultaneously for energy loss reduction based on an improved reconfiguration method. IEEE Transactions on power systems, 2011. 27(2): p. 587-595.
[12] Nikkhah, S. and A. Rabiee, Multi-objective stochastic model for joint optimal allocation of DG units and network reconfiguration from DG owner’s and DisCo’s perspectives. Renewable energy, 2019. 132: p. 471-485.
[13] Hamida, I.B., et al., Optimal network reconfiguration and renewable DG integration considering time sequence variation in load and DGs. Renewable energy, 2018. 121: p. 66-80.
[14] Mirjalili, S., S.M. Mirjalili, and A. Lewis, Grey wolf optimizer. Advances in engineering software, 2014. 69: p. 46-61.
[15] Sunil, D.V. and N. Yadaiah. A novel improved particle swarm optimization frame work for reconfiguration of radial distribution system. in 2017 IEEE International Conference on Environment and Electrical Engineering and 2017 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe). 2017. IEEE, june 2017.
[16] Ebrahimi, R., M. Ehsan, and H. Nouri, A profit-centric strategy for distributed generation planning considering time varying voltage dependent load demand. International Journal of Electrical Power & Energy Systems, 2013. 44(1): p. 168-178.
[17] Alemohammad, S.H., E. Mashhour, and M. Saniei, A market-based method for reconfiguration of distribution network. Electric Power Systems Research, 2015. 125: p. 15-22.
[18] Rao, R.S., S. Narasimham, and M. Ramalingaraju, Optimization of distribution network configuration for loss reduction using artificial bee colony algorithm. International Journal of Electrical Power and Energy Systems Engineering, 2008. 1(2): p. 116-122.