Planning of multi-type FACTS devices in restructured power systems with wind generation

Abstract Many electrical power systems are changing from a vertically integrated entity to a deregulated, open-market environment. This paper proposes an approach to optimally allocate multi-type flexible AC transmission system (FACTS) devices in restructured power systems with wind generation. The objective of the approach is to maximize the present value of long-term profit. Many factors like load variation, wind generation variation, generator capacity limit, line flow limit, voltage regulation, dispatchable load limits, generation rescheduling cost, load shedding cost, and multilateral power contracts are considered in problem formulation. The proposed method accurately evaluates the annual costs and benefits obtainable by FACTS devices in formulating the large-scale optimization problem under both normal condition and possible contingencies. The overall problem is solved using both Particle Swarm Optimization (PSO) for attaining optimal FACTS devices allocation as main problem and optimal power flow as sub optimization problem. The efficacy of the proposed approach is demonstrated for modified IEEE 14-bus test system and IEEE 118-bus test system.

[1]  Naoto Yorino,et al.  FACTS Allocation Based on Expected Security Cost by Means of Hybrid PSO , 2010, 2010 Asia-Pacific Power and Energy Engineering Conference.

[2]  A.J. Conejo,et al.  Optimal Network Placement of SVC Devices , 2007, IEEE Transactions on Power Systems.

[3]  F. Milano,et al.  An open source power system analysis toolbox , 2005, 2006 IEEE Power Engineering Society General Meeting.

[4]  Dhaneshwari Sahu,et al.  Efficient Voltage Regulation in Three PhaseA.C. Transmission Lines Using Static VARCompensator , 2013 .

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

[6]  Laszlo Gyugyi,et al.  Understanding FACTS: Concepts and Technology of Flexible AC Transmission Systems , 1999 .

[7]  Héctor Pulgar-Painemal,et al.  Emerging issues due to the integration of wind power in competitive electricity markets , 2010, 2010 Power and Energy Conference At Illinois (PECI).

[8]  Naresh Acharya,et al.  Influence of TCSC on congestion and spot price in electricity market with bilateral contract , 2007 .

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

[10]  M. A. Abido,et al.  Optimal location and setting of SVC and TCSC devices using non-dominated sorting particle swarm optimization , 2009 .

[11]  M. Saravanan,et al.  Application of particle swarm optimization technique for optimal location of FACTS devices considering cost of installation and system loadability , 2007 .

[12]  Naoto Yorino,et al.  Multi-load level reactive power planning considering slow and fast VAR devices by means of particle swarm optimisation , 2008 .

[13]  Amir H. Mohammadi,et al.  A Novel Algorithm for Optimal Location of FACTS Devices in Power System Planning , 2008 .

[14]  H. Sasaki,et al.  A New Formulation for FACTS Allocation for Security Enhancement against Voltage Collapse , 2002, IEEE Power Engineering Review.

[15]  Muhammad Murtadha Othman,et al.  Transmission Loss Minimization Using Evolutionary Programming Considering UPFC Installation Cost , 2010 .

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

[17]  Seema Singh,et al.  An Approach for Optimal Placement of Static VAr Compensators Based on Reactive Power Spot Price , 2007 .

[18]  N. Yorino,et al.  FACTS Devices Allocation With Control Coordination Considering Congestion Relief and Voltage Stability , 2011, IEEE Transactions on Power Systems.

[19]  Ali Abur,et al.  Static security enhancement via optimal utilization of thyristor-controlled series capacitors , 2002 .

[20]  James Kennedy,et al.  Particle swarm optimization , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[21]  S. Surender Reddy,et al.  Congestion management in deregulated power system by optimal choice and allocation of FACTS controllers using multi-objective genetic algorithm , 2010, T&D 2010.

[22]  Mohammad Shahidehpour,et al.  Market operations in electric power systems , 2002 .

[23]  Antonio J. Conejo,et al.  Electric Energy Systems : Analysis and Operation , 2008 .

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

[25]  Georgios C. Stamtsis,et al.  Optimal choice and allocation of FACTS devices in deregulated electricity market using genetic algorithms , 2004, IEEE PES Power Systems Conference and Exposition, 2004..

[26]  Norman Mariun,et al.  On the Application of heuristic Method and Saddle Node Bifurcation for Optimal Placement of FACTS Devices in Power System , 2011 .