Multi-objective Grasshopper Optimization Algorithm based Reconfiguration of Distribution Networks

Document Type: Research paper

Authors

1 Department of Electrical Engineering, Zanjan Branch, Islamic Azad University, Zanjan, Iran.

2 Department of Electrical Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran

3 Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran.

Abstract

Network reconfiguration is a nonlinear optimization procedure which calculates a radial structure to optimize the power losses and improve the network reliability index while meeting practical constraints. In this paper, a multi-objective framework is proposed for optimal network reconfiguration with the objective functions of minimization of power losses and improvement of reliability index. The optimization problem is solved by multi-objective grasshopper optimization algorithm (MOGOA) which is one of the most modern heuristic optimization tools. To solve an optimization problem, the suggested algorithm mathematically mimics and formulates the behavior of grasshopper swarms. The modifying comfort zone coefficient needs grasshoppers to balance exploration and exploitation, which helps the MOGOA to find an exact approximation of global optimization and not trapped in local optima. The efficiency of the suggested technique is approved regarding the 33-bus and 69-bus test systems. Optimization results expressed that the suggested technique not only presents the intensified exploration ability but also has a better solution compared with previous algorithms.

Keywords

Main Subjects


[1]    I. Ziari,  G. Ledwich,  A. Ghosh and G. Platt,  “Integrated  distribution  systems  planning  to  improve  reliability  under  load  growth”, IEEE Trans. Power Delivery, vol. 27, no. 2, pp. 757-765, 2012.

[2]    A. Abdelaziz, F. Mohamed, S. Mekhamer and M. Badr, “Distribution system reconfiguration using a modified tabu search algorithm”, Electr. Power Syst. Res., vol. 80, no. 8, pp. 943-953, 2010.

[3]    N. Gupta, A. Swarnkar, K. Niazi and RC. Bansal, “Multi-objective reconfiguration of distribution systems using adaptive genetic algorithm in fuzzy framework”, IET Gener. Transm. Distrib., vol. 4 no. 12, pp. 1288-1298, 2010.

[4]    R. R. Srinivasa, S. L. Narasimham, R. M. Ramalinga and R. A. Srinivasa, “Optimal network reconfiguration of large-scale distribution system using harmony search algorithm”, IEEE Trans. Power Syst., vol. 26, no. 3, pp. 1080-1088, 2011.

[5]    T. Niknam, A. Kavousi Fard and A. Baziar, “Multi-objective stochastic distribution feeder reconfiguration problem considering hydrogen and thermal energy production by fuel cell power plants”, Energy, vol. 42, no. 1, pp. 563-573, 2012.

[6]    Y. M. Shuaib, M. S. Kalavathi and C. C. Asir Rajan, “Optimal reconfiguration in radial distribution system using gravitational search algorithm”, Elec. Power Compon. Sys., vol. 42, no. 7, pp. 703-715, 2014.

[7]    D. L. Duan, X. D. Ling, X. Y. Wu, and B. Zhong, “Reconfiguration of distribution network for loss reduction and reliability improvement based on an enhanced genetic algorithm”, Int. J. Electr. Power Energy Syst., vol. 64, pp. 88-95, 2015.

[8]    H. Shareef, A. A. Ibrahim, N. Salman, A. Mohamed and W. Ling Ai, “Power quality and reliability enhancement in distribution systems via optimum network reconfiguration by using quantum firefly algorithm”, Int J. Electr. Power Energy Syst., vol. 58, pp. 160-169, 2014.

[9]    A. Kavousi-Fard and T. Niknam “Multi-objective Stochastic distribution feeder reconfiguration from the reliability point of view”, Energy, vol. 64, pp. 342-354, 2014.

[10]  Sh. Saremi, S.A. Mirjalili and A. Lewis, “Grasshopper optimisation algorithm: theory and application”, Advances in Eng. Software, vol.105, pp. 30-45, 2017.

[11]  J. Wu, W. Honglun, N. Li and P. Yao, “Distributed trajectory optimization for multiple solar-powered UAVs target tracking in urban environment by Adaptive Grasshopper Optimisation Algorithm”, Aerosp. Sci. Technol, vol. 70, 2017.

[12]  S. Mirjalili, S.A. Mirjalili, Sh. Saremi, H. Faris and I. Aljarah, “Grasshopper optimization algorithm for multi-objective optimization problems”, Appl Intell, vol. 48, no. 4, pp. 805-820, 2018.

[13]  K. Deb, N. Padhye, “Enhancing performance of particle swarm optimization through an algorithmic link with genetic algorithms”, Comput Optim Appl, vol. 57, pp. 761-794, 2014.

[14]  J. Endrenyi, J. Reliability and J. Wiley, “Modeling in electric power systems”, New York, NY, 1978.

[15]   S. Yin, C. Lu, “Distribution feeder scheduling considering variable load profile and outage costs”, IEEE Trans., Power Syst., vol. 24, pp. 652-60, 2009.

[16]  SA. Yin, CN. Lu, “Distribution feeder scheduling considering variable load profile and outage costs”, IEEE Trans., Power Syst., vol. 24, pp. 652-60, 2009.

[17]  I. Waseem, “Impacts of distributed generation on the residential distribution network operation”, in Electrical Engineering, Virginia Polytechnic Institute and State University, 2008.

[18]  D. Q. Hung and N. Mithulananthan, “Loss reduction and load ability enhancement with DG: a dual-index analytical approach”, Appl Energy, vol. 115, pp. 233-241, 2014.

[19]  M. Sedighizadeh, M. Esmaili and M. M. Mahmoodi, “Reconfiguration of distribution systems to improve reliability and reduce power losses using imperialist competitive algorithm”, Iranian J. Electr. & Electron. Eng., vol. 13, No. 3, pp. 287-302, 2017.

[20]  N. M. G. Kumar, “Reliability improvement of radial distribution system with incorporating protective devices-case study”, Int.  J.  Eng. Sci. Emerg Technol, vol. 4, no.2, pp. 60-74, 2013.

[21]  P. Carvalho, P. F. Correia and L. Ferreira, “Mitigation of interruption reimbursements by periodic network reconfiguration: risk-based versus expected-value optimization”, IEEE Trans. Power Syst., vol. 22, no. 2, pp. 845-50, 2007.

[22]   S. Chen, W.  Hu and Z. Chen, “Comprehensive cost minimization in distribution networks using segmented-time feeder reconfiguration and reactive power control of distributed generators”, IEEE Trans. Power Syst., vol. 31, no. 2, pp. 983-993, 2016.

[23]  J. Tian, C. Su, and Z. Chen, “Reactive power capability of the wind turbine with doubly fed induction generator”, in Proc. 39th IEEE IECON,  pp. 5310-5315, 2013.

[24]  K. Deb, N. Padhye, “Enhancing performance of particle swarm optimization through an algorithmic link with genetic algorithms”, Comput Optim Appl, vol. 57, pp. 761-794, 2014.

[25] N. Padhye, P. Bhardawaj and K. Deb, “Improving differential evolution through a unified approach”, J Glob Optim, vol. 55, pp. 771, 2013.