Reconfiguration of distribution systems to improve reliability and reduce power losses using Imperialist Competitive Algorithm

Distribution systems can be operated in multiple configurations since they arepossible combinations of radial and loop feeders. Each configuration leads to its own power losses and reliability level of supplying electric energy to customers. In order to obtain the optimal configuration of power networks, their reconfiguration is formulated as a complex optimization problem with different objective functions and network operating constraints. In this paper, a multi-objective framework is proposed for optimal network reconfiguration with objective functions of minimization of power losses, System Average Interruption Frequency Index(SAIFI), System Average Interruption Duration Index (SAIDI), Average Energy Not Supplied (AENS), and Average Service Unavailability Index (ASUI). The optimization problem is solved by the Imperialist Competitive Algorithm (ICA) as one of the most modern heuristic tools. Since objective functions have different scales, a fuzzy membership is utilized here to transform objective functions into a same scale and then to determine the satisfaction level of the afforded solution using the fuzzy fitness. The efficiency of the proposed method is confirmed by testing it on 32-bus and 69-bus distribution test systems. Simulation results demonstrate that the proposed method not only presents intensified exploration ability but also has a better converge rate compared with previous methods.

[1]  Mostafa Sedighizadeh,et al.  An Efficient Hybrid Big Bang–Big Crunch Algorithm for Multi-objective Reconfiguration of Balanced and Unbalanced Distribution Systems in Fuzzy Framework , 2013 .

[2]  Abdul Rauf Bhatti,et al.  Review on reliability improvement and power loss reduction in distribution system via network reconfiguration , 2016 .

[3]  Engin Yesil,et al.  Interval type-2 fuzzy PID load frequency controller using Big Bang-Big Crunch optimization , 2014, Appl. Soft Comput..

[4]  Caro Lucas,et al.  Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition , 2007, 2007 IEEE Congress on Evolutionary Computation.

[5]  Ehab S. Ali,et al.  Speed control of induction motor supplied by wind turbine via Imperialist Competitive Algorithm , 2015 .

[6]  Abdollah Kavousi-Fard,et al.  Reliability enhancement using optimal distribution feeder reconfiguration , 2013, Neurocomputing.

[7]  D. Shirmohammadi,et al.  Reconfiguration of electric distribution networks for resistive line losses reduction , 1989 .

[8]  Fan Zhang,et al.  Improved Genetic Algorithm with Infeasible Solution Disposing of Distribution Network Reconfiguration , 2009, 2009 WRI Global Congress on Intelligent Systems.

[9]  Taher Niknam,et al.  Multi-objective stochastic distribution feeder reconfiguration problem considering hydrogen and thermal energy production by fuel cell power plants , 2012 .

[10]  Amin Khodabakhshian,et al.  A new fuzzy optimal reconfiguration of distribution systems for loss reduction and load balancing using ant colony search-based algorithm , 2011, Appl. Soft Comput..

[11]  Nikolaos G. Paterakis,et al.  Multi-objective reconfiguration of radial distribution systems using reliability indices , 2016, 2016 IEEE/PES Transmission and Distribution Conference and Exposition (T&D).

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

[13]  Taher Niknam,et al.  Multi-objective stochastic Distribution Feeder Reconfiguration from the reliability point of view , 2014 .

[14]  Taher Niknam,et al.  Optimal Distribution Feeder Reconfiguration for Reliability Improvement Considering Uncertainty , 2014, IEEE Transactions on Power Delivery.

[15]  S. Carneiro,et al.  A new heuristic reconfiguration algorithm for large distribution systems , 2005, 2006 IEEE Power Engineering Society General Meeting.

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

[17]  M. Raju,et al.  Optimal Network Reconfiguration of Large-Scale Distribution System Using Harmony Search Algorithm , 2011, IEEE Transactions on Power Systems.

[18]  Ali Deihimi,et al.  Optimized echo state networks using a big bang–big crunch algorithm for distance protection of series-compensated transmission lines , 2014 .

[19]  Xingying Chen,et al.  A hybrid particle swarm optimization approach for distribution network reconfiguration problem , 2008, 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century.

[20]  Taher Niknam,et al.  An efficient hybrid evolutionary algorithm based on PSO and HBMO algorithms for multi-objective Distribution Feeder Reconfiguration , 2009 .

[21]  Babita Majhi,et al.  Multiobjective optimization based adaptive models with fuzzy decision making for stock market forecasting , 2015, Neurocomputing.

[22]  Mehdi Vakilian,et al.  A dynamic method for feeder reconfiguration and capacitor switching in smart distribution systems , 2017 .

[23]  Azah Mohamed,et al.  Power quality and reliability enhancement in distribution systems via optimum network reconfiguration by using quantum firefly algorithm , 2014 .

[24]  Nikhil Gupta,et al.  Distribution network reconfiguration for power quality and reliability improvement using Genetic Algorithms , 2014 .

[25]  Eskandar Gholipour,et al.  Decreasing activity cost of a distribution system company by reconfiguration and power generation control of DGs based on shuffled frog leaping algorithm , 2014 .

[26]  Taher Niknam,et al.  Application of honey-bee mating optimization on state estimation of a power distribution system including distributed generators , 2008 .

[27]  Khaleequr Rehman Niazi,et al.  Adapted ant colony optimization for efficient reconfiguration of balanced and unbalanced distribution systems for loss minimization , 2011, Swarm Evol. Comput..

[28]  G. Ledwich,et al.  Integrated Distribution Systems Planning to Improve Reliability Under Load Growth , 2012, IEEE Transactions on Power Delivery.

[29]  M. Surya Kalavathi,et al.  Optimal Reconfiguration in Radial Distribution System Using Gravitational Search Algorithm , 2014 .

[30]  Saad Mekhilef,et al.  Optimal reconfiguration of distribution system connected with distributed generations: A review of different methodologies , 2017 .

[31]  Chen Xue.  RECONFIGURATION OF DISTRIBUTION NETWORKS BASED ON FUZZY GENETIC ALGORITHMS , 2000 .

[32]  Taher Niknam,et al.  A NEW HYBRID ALGORITHM FOR MULTI-OBJECTIVE DISTRIBUTION FEEDER RECONFIGURATION , 2009, Cybern. Syst..

[33]  Taher Niknam,et al.  Stochastic framework for reliability enhancement using optimal feeder reconfiguration , 2014 .

[34]  Shouxiang Wang,et al.  Reliability-oriented distribution network reconfiguration considering uncertainties of data by interval analysis , 2012 .

[35]  Xiao-Yue Wu,et al.  Reconfiguration of distribution network for loss reduction and reliability improvement based on an enhanced genetic algorithm , 2015 .

[36]  Taher Niknam,et al.  An efficient hybrid evolutionary algorithm based on PSO and ACO for distribution feeder reconfiguration , 2009 .

[37]  Taher Niknam,et al.  Distribution feeder reconfiguration considering fuel cell/wind/photovoltaic power plants , 2012 .

[38]  Caro Lucas,et al.  Imperialist competitive algorithm for minimum bit error rate beamforming , 2009, Int. J. Bio Inspired Comput..