Coordinated aggregated-based particle swarm optimisation algorithm for congestion management in restructured power market by placement and sizing of unified power flow controller

This study presents a particle swarm optimisation (PSO)-based algorithm to perform congestion management by proper placement and sizing of one unified power flow controller (UPFC) device in a market-based power systems. The algorithm uses quadratic smooth curves for generators' costs. A typical load duration curve (LDC) is used to improve the accuracy of the model by incorporating the impacts of load variation on the optimisation problem. The proposed approach makes use of the PSO algorithm to allocate the near-optimal GenCos as well as the optimal location and size of UPFC whereas the Newton'Raphson solution minimises the mismatch of the power flow equations. Simulation results (without/with the line flow constraints, before and after the compensation) are used to analyse the impact of UPFC on the congestion levels of the reliability test system (RTS) 24-bus test system. Simulation results by the proposed PSO algorithm are also compared with solutions obtained by the conventional sequential quadratic programming (SQP) approach.

[1]  G. B. Shrestha,et al.  Effects of series compensation on spot price power markets , 2005 .

[2]  Sung-Kwan Joo,et al.  Social Welfare Maximization in Transmission Enhancement Considering Network Congestion , 2008, IEEE Transactions on Power Systems.

[3]  Seema Singh,et al.  Optimal location of FACTS devices for congestion management , 2001 .

[4]  Ali Deihimi,et al.  A Fuzzy Multi-Objective Multi-Case Genetic-Based Optimization for Allocation of FACTS Devices to Improve System Static Security, Power Loss and Transmission Line Voltage Profiles , 2010 .

[5]  H. Gupta,et al.  Impact on real and reactive power pricing in open power market using unified power flow controller , 2006, IEEE Transactions on Power Systems.

[6]  Joong-Rin Shin,et al.  A particle swarm optimization for economic dispatch with nonsmooth cost functions , 2005, IEEE Transactions on Power Systems.

[7]  Zuwei Yu.  Mixed Integer Social Welfare Maximization (MI-SWM) and implications in optimal electricity pricing , 1999 .

[8]  Akhtar Kalam,et al.  Optimal allocation of multi-type FACTS devices using bees algorithm for ATC enhancement in deregulated power system , 2010 .

[9]  Sergio Bruno,et al.  Unified power flow controllers for security-constrained transmission management , 2004 .

[10]  A. David,et al.  Optimal dispatch under transmission contracts , 1999 .

[11]  A. Kazemi,et al.  Congestion management in nodal pricing with genetic algorithm , 2006, 2006 International Conference on Power Electronic, Drives and Energy Systems.

[12]  J. Hazra,et al.  Congestion management using multiobjective particle swarm optimization , 2007, 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century.

[13]  Kanwardeep Singh,et al.  Social Welfare Maximization Considering Reactive Power and Congestion Management in the Deregulated Environment , 2009 .

[14]  S. Gerbex,et al.  Optimal Location of Multi-Type FACTS Devices in a Power System by Means of Genetic Algorithms , 2001, IEEE Power Engineering Review.

[15]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[16]  M. Fotuhi-Firuzabad,et al.  Application of Static Synchronous Series Compensator to Damp Sub-Synchronous Resonance , 2006, 2006 International Conference on Power Electronic, Drives and Energy Systems.

[17]  F. Galiana,et al.  Demand-side reserve offers in joint energy/reserve electricity markets , 2003 .

[18]  J. Arroyo,et al.  Energy and reserve pricing in security and network-constrained electricity markets , 2005, IEEE Transactions on Power Systems.

[19]  Naresh Acharya,et al.  Locating series FACTS devices for congestion management in deregulated electricity markets , 2007 .

[20]  A. Vojdani,et al.  Transmission dispatch and congestion management in the emerging energy market structures , 1998 .

[21]  Mohammad Shahidehpour,et al.  The IEEE Reliability Test System-1996. A report prepared by the Reliability Test System Task Force of the Application of Probability Methods Subcommittee , 1999 .

[22]  T. Overbye,et al.  An Individual Welfare Maximation Algorithm for Electricity Markets , 2002, IEEE Power Engineering Review.

[23]  Canbolat Uçak,et al.  Sensitivity analysis based on transmission line susceptances for congestion management , 2008 .

[24]  S. Dutta,et al.  Optimal Rescheduling of Generators for Congestion Management Based on Particle Swarm Optimization , 2008, IEEE Transactions on Power Systems.

[25]  J.G. Vlachogiannis,et al.  A Comparative Study on Particle Swarm Optimization for Optimal Steady-State Performance of Power Systems , 2006, IEEE Transactions on Power Systems.

[26]  Hari Om Gupta,et al.  Location of unified power flow controller for congestion management , 2001 .

[27]  Nadarajah Mithulananthan,et al.  A proposal for investment recovery of FACTS devices in deregulated electricity markets , 2007 .