Comprehensive Cost Minimization in Distribution Networks Using Segmented-Time Feeder Reconfiguration and Reactive Power Control of Distributed Generators

In this paper, an efficient methodology is proposed to deal with segmented-time reconfiguration problem of distribution networks coupled with segmented-time reactive power control of distributed generators. The target is to find the optimal dispatching schedule of all controllable switches and distributed generators' reactive powers in order to minimize comprehensive cost. Corresponding constraints, including voltage profile, maximum allowable daily switching operation numbers (MADSON), reactive power limits, and so on, are considered. The strategy of grouping branches is used to simplify the formulated mathematical problem and a hybrid particle swarm optimization (HPSO) method is presented to search the optimal solution. Fuzzy adaptive inference is integrated into basic HPSO method in order to avoid being trapped in local optima. The proposed fuzzy adaptive inference-based hybrid PSO algorithm (FAHPSO) is implemented in VC++ 6.0 program language. A modified version of the typical 70-node distribution network and several real distribution networks are used to test the performance of the proposed method. Numerical results show that the proposed methodology is an efficient method for comprehensive cost minimization in distribution systems with distributed generators.

[1]  C. Su,et al.  Variable scaling hybrid differential evolution for solving network reconfiguration of distribution systems , 2005 .

[2]  Helon D. M. Braz,et al.  Distribution Network Reconfiguration Using Genetic Algorithms With Sequential Encoding: Subtractive and Additive Approaches , 2011, IEEE Transactions on Power Systems.

[3]  J. R. Shin,et al.  An efficient simulated annealing algorithm for network reconfiguration in large-scale distribution systems , 2002 .

[4]  Guo Zhizhong,et al.  Optimal Time-varying Reactive Power and Voltage Control in Distribution Systems , 2005 .

[5]  S.N. Singh,et al.  Fuzzy Adaptive Particle Swarm Optimization for Bidding Strategy in Uniform Price Spot Market , 2007, IEEE Transactions on Power Systems.

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

[7]  Liu Qun-ying.  Distribution network reconfiguration based on incorporation data platform and improved PSO , 2012 .

[8]  Naoki Hayashi,et al.  Long-term distribution network expansion planning by network reconfiguration and generation of construction plans , 2003 .

[9]  D. Das.  A fuzzy multiobjective approach for network reconfiguration of distribution systems , 2006, IEEE Transactions on Power Delivery.

[10]  H. M. Khodr,et al.  Integral methodology for distribution systems reconfiguration based on optimal power flow using Benders decomposition technique , 2009 .

[11]  Luigi Fortuna,et al.  Chaotic sequences to improve the performance of evolutionary algorithms , 2003, IEEE Trans. Evol. Comput..

[12]  Pedro Faria,et al.  Modified Particle Swarm Optimization Applied to Integrated Demand Response and DG Resources Scheduling , 2013, IEEE Transactions on Smart Grid.

[13]  R. Romero,et al.  An Efficient Codification to Solve Distribution Network Reconfiguration for Loss Reduction Problem , 2008, IEEE Transactions on Power Systems.

[14]  Amany El-Zonkoly,et al.  Optimal placement of multi-distributed generation units including different load models using particle swarm optimisation , 2011 .

[15]  Zhe Chen,et al.  Fuzzy adaptive particle swarm optimisation for power loss minimisation in distribution systems using optimal load response , 2014 .

[16]  R. Jabr,et al.  Minimum Loss Network Reconfiguration Using Mixed-Integer Convex Programming , 2012, IEEE Transactions on Power Systems.

[17]  A. Ahuja,et al.  An AIS-ACO Hybrid Approach for Multi-Objective Distribution System Reconfiguration , 2007, IEEE Transactions on Power Systems.

[18]  Yuan-Kang Wu,et al.  Study of Reconfiguration for the Distribution System With Distributed Generators , 2010, IEEE Transactions on Power Delivery.

[19]  C. Su,et al.  Network Reconfiguration of Distribution Systems Using Improved Mixed-Integer Hybrid Differential Evolution , 2002, IEEE Power Engineering Review.

[20]  Mahmud Fotuhi-Firuzabad,et al.  Distribution system reliability enhancement using optimal capacitor placement , 2008 .

[21]  Men-Shen Tsai,et al.  Application of Enhanced Integer Coded Particle Swarm Optimization for Distribution System Feeder Reconfiguration , 2011, IEEE Transactions on Power Systems.

[22]  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.

[23]  Zhao Bo,et al.  Hybrid Particle Swarm Optimization for Distribution Network Reconfiguration , 2008 .

[24]  Russell C. Eberhart,et al.  Parameter Selection in Particle Swarm Optimization , 1998, Evolutionary Programming.

[25]  C. Riveros,et al.  Switch and Tap-Changer Reconfiguration of Distribution Networks Using Evolutionary Algorithms , 2013, IEEE Transactions on Power Systems.

[26]  Newton Bretas,et al.  Node-depth encoding and multiobjective evolutionary algorithm applied to large-scale distribution system reconfiguration , 2011, 2011 IEEE Power and Energy Society General Meeting.

[27]  N. Hadjsaid,et al.  Radial network reconfiguration using genetic algorithm based on the matroid theory , 2008, 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century.

[28]  Z. Chen,et al.  Reactive power capability of the wind turbine with Doubly Fed Induction Generator , 2013, IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society.

[29]  J B A London,et al.  Node-Depth Encoding and Multiobjective Evolutionary Algorithm Applied to Large-Scale Distribution System Reconfiguration , 2010, IEEE Transactions on Power Systems.