Optimal network reconfiguration and renewable DG integration considering time sequence variation in load and DGs

Abstract Several studies of distribution network enhancement focused only on the optimization of either the integration of distributed generations (DG) or network reconfiguration. However, very few researches have been done for distribution network reconfiguration simultaneously with the DG location and sizing. This paper presents a multi-objective management operations based on network reconfiguration in parallel with renewable DGs allocation and sizing for minimizing active power loss, annual operation costs (installation, maintenance, and active power loss costs) and pollutant gas emissions. The time sequence variation in wind speed, solar irradiation and load are taken into consideration. An efficient evolutionary technique based on the Pareto optimality is adopted to solve the problem. A fuzzy set theory is used to select the best compromise solution among obtained Pareto set. The obtained results prove the efficiency and the accuracy of the suggested method for the network manager to find the optimal network configuration simultaneously with DG location and sizing considering multiple criteria.

[1]  H. Mori,et al.  An Efficient Multi-objective Meta-heuristic Method for Distribution Network Expansion Planning , 2007, 2007 IEEE Lausanne Power Tech.

[2]  Yunhua Li,et al.  Optimal Placement and Sizing of Distributed Generation via an Improved Nondominated Sorting Genetic Algorithm II , 2015, IEEE Transactions on Power Delivery.

[3]  Florin Bogdan Enacheanu Outils d'aide à la conduite pour les opérateurs des réseaux de distribution , 2007 .

[4]  K. Zare,et al.  Application of binary group search optimization to distribution network reconfiguration , 2014 .

[5]  Leonardo W. de Oliveira,et al.  Optimal allocation of distributed generation with reconfiguration in electric distribution systems , 2013 .

[6]  Mohd Hasan Ali,et al.  Simultaneous Reconfiguration, Optimal Placement of DSTATCOM, and Photovoltaic Array in a Distribution System Based on Fuzzy-ACO Approach , 2015, IEEE Transactions on Sustainable Energy.

[7]  Marco Laumanns,et al.  SPEA2: Improving the strength pareto evolutionary algorithm , 2001 .

[8]  Reza Noroozian,et al.  Optimal siting and sizing of distributed generation accompanied by reconfiguration of distribution networks for maximum loss reduction by using a new UVDA-based heuristic method , 2016 .

[9]  K. C. Divya,et al.  Models for wind turbine generating systems and their application in load flow studies , 2006 .

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

[11]  Antonio José Gil Mena,et al.  An efficient approach for the siting and sizing problem of distributed generation , 2015 .

[12]  Hossein Nezamabadi-pour,et al.  An Improved Multi-Objective Harmony Search for Optimal Placement of DGs in Distribution Systems , 2013, IEEE Transactions on Smart Grid.

[13]  M. M. Aman,et al.  A new approach for optimum simultaneous multi-DG distributed generation Units placement and sizing based on maximization of system loadability using HPSO (hybrid particle swarm optimization) algorithm , 2014 .

[14]  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.

[15]  M. P. Selvan,et al.  Fuzzy Embedded Genetic Algorithm Method for Distributed Generation Planning , 2011 .

[16]  P. G. Student,et al.  Backward / Forward Sweep Load Flow Algorithm for Radial Distribution System , 2014 .

[17]  Arash Asrari,et al.  Pareto Dominance-Based Multiobjective Optimization Method for Distribution Network Reconfiguration , 2016, IEEE Transactions on Smart Grid.

[18]  Mohammad Yusri Hassan,et al.  Optimal distributed renewable generation planning: A review of different approaches , 2013 .

[19]  Olga Roudenko Application des algorithmes évolutionnaires aux problèmes d'optimisation multi-objectif avec contraintes. , 2004 .

[20]  Kwang Y. Lee,et al.  Determining PV Penetration for Distribution Systems With Time-Varying Load Models , 2014, IEEE Transactions on Power Systems.

[21]  Luis Neves,et al.  Multi-objective optimization using NSGA-II for power distribution system reconfiguration , 2015 .

[22]  Ramesh C. Bansal,et al.  Simultaneous allocation of distributed energy resource using improved particle swarm optimization , 2017 .

[23]  Kalyanmoy Deb,et al.  Evolutionary multiobjective optimization , 2007, GECCO '07.

[24]  M. Kowsalya,et al.  A novel integration technique for optimal network reconfiguration and distributed generation placement in power distribution networks , 2014 .

[25]  Dheeraj K. Khatod,et al.  Evolutionary programming based optimal placement of renewable distributed generators , 2013, IEEE Transactions on Power Systems.

[26]  R. Siezen,et al.  others , 1999, Microbial Biotechnology.

[27]  Gevork B. Gharehpetian,et al.  Optimal allocation and sizing of DG units considering voltage stability, losses and load variations , 2016 .

[28]  Attia A. El-Fergany,et al.  Optimal allocation of multi-type distributed generators using backtracking search optimization algorithm , 2015 .

[29]  Nadarajah Mithulananthan,et al.  AN ANALYTICAL APPROACH FOR DG ALLOCATION IN PRIMARY DISTRIBUTION NETWORK , 2006 .

[30]  Yuan Liu,et al.  Optimal sitting and sizing of DGs in distribution system considering time sequence characteristics of loads and DGs , 2015 .

[31]  Lothar Thiele,et al.  Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach , 1999, IEEE Trans. Evol. Comput..

[32]  Srinivasa Rao Gampa,et al.  Optimum placement and sizing of DGs considering average hourly variations of load , 2015 .

[33]  Chandrasekhar Yammani,et al.  A Multi-objective Shuffled Bat algorithm for optimal placement and sizing of multi distributed generations with different load models , 2016 .

[34]  Magdy M. A. Salama,et al.  Distributed generation technologies, definitions and benefits , 2004 .

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

[36]  Faouzi Msahli,et al.  Strength pareto evolutionary algorithm 2 for Environmental/Economic Power Dispatch , 2015, 2015 7th International Conference on Modelling, Identification and Control (ICMIC).

[37]  Mostafa Sedighizadeh,et al.  Multi-objective optimal reconfiguration and DG (Distributed Generation) power allocation in distribution networks using Big Bang-Big Crunch algorithm considering load uncertainty , 2016 .

[38]  Yuji Ohya,et al.  A Shrouded Wind Turbine Generating High Output Power with Wind-lens Technology , 2010 .

[39]  A. C. Zambroni de Souza,et al.  Artificial Immune Systems Optimization Approach for Multiobjective Distribution System Reconfiguration , 2015, IEEE Transactions on Power Systems.

[40]  Daniel Kunkle,et al.  A Summary and Comparison of MOEA Algorithms , 2005 .

[41]  Almoataz Y. Abdelaziz,et al.  Ant Lion Optimization Algorithm for optimal location and sizing of renewable distributed generations , 2017 .

[42]  J. Kruskal On the shortest spanning subtree of a graph and the traveling salesman problem , 1956 .

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

[44]  R. K. Ursem Multi-objective Optimization using Evolutionary Algorithms , 2009 .

[45]  E.F. El-Saadany,et al.  Optimal Renewable Resources Mix for Distribution System Energy Loss Minimization , 2010, IEEE Transactions on Power Systems.

[46]  Morad Abdelaziz,et al.  Distribution network reconfiguration using a genetic algorithm with varying population size , 2017 .

[47]  Suneet Singh,et al.  Optimal Sizing of Distributed Generation Placed on Radial Distribution Systems , 2010 .

[48]  N. Mithulananthan,et al.  Loss reduction and loadability enhancement with DG: A dual-index analytical approach , 2014 .