Minimum switching losses for solving distribution NR problem with distributed generation

Power losses in a distribution system are commonly minimised via optimal network reconfiguration (NR). Previously, research on NR was focused on planning, where the final configuration reporting the lowest power losses being the main goal. However, power losses during switching operations from the original state to the optimal state of configuration were not considered. This study discusses the optimal switching path for minimising power losses when reconfiguring a network. The simultaneous optimal NR and distributed generation (DG) output was also proposed. The proposed methodology involves: (i) optimal NR and DG output simultaneously and (ii) optimal switching path to convert the network from the initial configuration to the final configuration obtained from (i). The selected optimisation technique in this study is the firefly algorithm. The proposed method was tested using IEEE 33-bus, 69-bus, and 118-bus radial distribution networks, while also accounting for static and dynamic loads. The results confirmed the effectiveness of the proposed method in determining the optimal path of switching operations, as well as the optimal network configuration and optimal output of DG units.

[1]  A. Gandomi,et al.  Mixed variable structural optimization using Firefly Algorithm , 2011 .

[2]  M.A.L. Badr,et al.  Distribution system reconfiguration using a modified Tabu Search algorithm , 2010 .

[3]  D. Das,et al.  Impact of Network Reconfiguration on Loss Allocation of Radial Distribution Systems , 2007, IEEE Transactions on Power Delivery.

[4]  K. R. Niazi,et al.  Multi-objective reconfiguration of distribution systems using adaptive genetic algorithm in fuzzy framework , 2010 .

[5]  J. Z. Zhu Optimal reconfiguration of electrical distribution network using the refined genetic algorithm , 2002 .

[6]  M. Kitagawa,et al.  Implementation of genetic algorithm for distribution systems loss minimum re-configuration , 1992 .

[7]  Zhengcai Fu,et al.  An improved TS algorithm for loss-minimum reconfiguration in large-scale distribution systems , 2007 .

[8]  Hazlee Azil Illias,et al.  Gravitational Search Algorithm and Selection Approach for Optimal Distribution Network Configuration Based on Daily Photovoltaic and Loading Variation , 2015, J. Appl. Math..

[9]  Luciane Neves Canha,et al.  Real‐Time Reconfiguration of Distribution Network with Distributed Generation , 2014 .

[10]  Davood Azizian,et al.  A multi-objective optimal sizing and siting of distributed generation using ant lion optimization technique , 2017, Ain Shams Engineering Journal.

[11]  Thang Trung Nguyen,et al.  Multi-objective electric distribution network reconfiguration solution using runner-root algorithm , 2017, Appl. Soft Comput..

[12]  Wanxing Sheng,et al.  A Network Reconfiguration Method Considering Data Uncertainties in Smart Distribution Networks , 2017 .

[13]  K. Ravindra,et al.  Power Loss Minimization in Distribution System Using Network Reconfiguration in the Presence of Distributed Generation , 2013, IEEE Transactions on Power Systems.

[14]  M. Haghifam,et al.  Adaptive multi-objective distribution network reconfiguration using multi-objective discrete particles swarm optimisation algorithm and graph theory , 2013 .

[15]  Anh Viet Truong,et al.  Distribution network reconfiguration for power loss minimization and voltage profile improvement using cuckoo search algorithm , 2015 .

[16]  Felix F. Wu,et al.  Network Reconfiguration in Distribution Systems for Loss Reduction and Load Balancing , 1989, IEEE Power Engineering Review.

[17]  Provas Kumar Roy,et al.  Multi-objective quasi-oppositional teaching learning based optimization for optimal location of distributed generator in radial distribution systems , 2014 .