Optimum allocation of FACTS devices under load uncertainty based on penalty functions with genetic algorithm

This paper presents genetic algorithm optimization method with a suitable objective function to determine optimum location and rated values of FACTS devices by taking into account changes in the power system load over time. In this study, annual daily load profile is considered as a whole instead of an instant load profile while looking for optimum size and location of FACTS devices. For this reason, to simplify the optimization procedure, a graph-based panelized objective function is developed, which can be used in a mixed integer search heuristic optimization technique. This paper focuses on the evaluation of the simultaneous use of thyristor controlled series capacitor and static VAR compensator. The proposed method allows including, in a simple way, the long term load profile in the planning stage to improve the power system performance using FACTS devices. After the optimization process, the performance of the proposed method has been tested on the IEEE-30 bus system with several annual test load profiles. The planning horizon is included in the optimization framework and the impact of planning horizon result is presented to compare with that of single load profile. The optimization strategy is shown to lead a significant reduction in the voltage and line violations under the long term test load profiles.

[1]  Salah Kamel,et al.  Fast decoupled load flow analysis with SSSC power injection model , 2014 .

[2]  Charles Audet,et al.  Nonsmooth optimization through Mesh Adaptive Direct Search and Variable Neighborhood Search , 2006, J. Glob. Optim..

[3]  Salah Kamel Non-member and,et al.  Fast decoupled load flow analysis with SSSC power injection model , 2014 .

[4]  Kr Padiyar,et al.  Facts Controllers in Power Transmission and Distribution , 2009 .

[5]  Y. Besanger,et al.  A multi-objective genetic algorithm approach to optimal allocation of multi-type FACTS devices for power systems security , 2006, 2006 IEEE Power Engineering Society General Meeting.

[6]  Kusum Deep,et al.  A real coded genetic algorithm for solving integer and mixed integer optimization problems , 2009, Appl. Math. Comput..

[7]  Sébastien Le Digabel,et al.  Algorithm xxx : NOMAD : Nonlinear Optimization with the MADS algorithm , 2010 .

[8]  S. Muralidharan,et al.  Application of Genetic Algorithm to power system voltage stability enhancement using facts devices , 2011, 2011 INTERNATIONAL CONFERENCE ON RECENT ADVANCEMENTS IN ELECTRICAL, ELECTRONICS AND CONTROL ENGINEERING.

[9]  Edoardo Amaldi,et al.  PGS-COM: A hybrid method for constrained non-smooth black-box optimization problems: Brief review, novel algorithm and comparative evaluation , 2014, Comput. Chem. Eng..

[10]  Nishi Sharma,et al.  A Comprehensive Survey of Optimal Placement and Coordinated Control Techniques of FACTS Controllers in Multi-Machine Power System Environments , 2010 .

[11]  M. Sydulu,et al.  Comparison of Genetic Algorithms and Particle Swarm Optimization for Optimal Power Flow Including FACTS devices , 2007, 2007 IEEE Lausanne Power Tech.

[12]  Frank Schettler,et al.  Power system problems solved by FACTS devices , 2009, 2009 IEEE/PES Power Systems Conference and Exposition.

[13]  K. Deb An Efficient Constraint Handling Method for Genetic Algorithms , 2000 .

[14]  Mitsuo Gen,et al.  Network Models and Optimization: Multiobjective Genetic Algorithm Approach , 2008 .

[15]  Singiresu S. Rao Engineering Optimization : Theory and Practice , 2010 .

[16]  T. W. Cease,et al.  Development of a /spl plusmn/100 MVAr static condenser for voltage control of transmission systems , 1995 .

[17]  K. Steemers,et al.  A method of formulating energy load profile for domestic buildings in the UK , 2005 .

[18]  Taher Niknam,et al.  A new multi objective optimization approach based on TLBO for location of automatic voltage regulators in distribution systems , 2012, Eng. Appl. Artif. Intell..

[19]  Yog Raj Sood,et al.  Optimal location of FACTS devices in power system using Genetic Algorithm , 2009, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC).

[20]  K. Chandrasekar,et al.  Performance comparison of DE, PSO and GA approaches in Transmission Power Loss minimization using FACTS Devices , 2011 .

[21]  Saeed Teimourzadeh,et al.  Application of HGSO to security based optimal placement and parameter setting of UPFC , 2014 .

[22]  Rustem Popa,et al.  Genetic Algorithms in Applications , 2012 .

[23]  Almoataz Y. Abdelaziz,et al.  Optimal Location of Thyristor-controlled Series Compensators in Power Systems for Increasing Loadability by Genetic Algorithm , 2011 .

[24]  Hortensia Amaris,et al.  Coordinated reactive power management in power networks with wind turbines and FACTS devices , 2011 .

[25]  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.

[26]  M. Basu,et al.  Multi-objective optimal power flow with FACTS devices , 2011 .

[27]  D. Devaraj,et al.  Genetic Algorithm approach for Optimal Power Flow with FACTS devices , 2008, 2008 4th International IEEE Conference Intelligent Systems.

[28]  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.

[29]  Nand Kishor,et al.  Robust H-infinity load frequency control in hybrid distributed generation system , 2013 .

[30]  Ehab F. El-Saadany,et al.  Distribution system reconfiguration for energy loss reduction considering the variability of load and local renewable generation , 2013 .

[31]  B. Vahidi,et al.  Optimal multi-type FACTS allocation using genetic algorithm to improve power system security , 2008, 2008 12th International Middle-East Power System Conference.

[32]  Vahid Vahidinasab,et al.  A modified harmony search method for environmental/economic load dispatch of real-world power systems , 2014 .

[33]  J.J. Paserba How FACTS controllers benefit AC transmission systems , 2004, IEEE Power Engineering Society General Meeting, 2004..

[34]  S. P. Singh,et al.  Voltage stability evaluation of power system with FACTS devices using fuzzy neural network , 2007, Eng. Appl. Artif. Intell..

[35]  Christine A. Shoemaker,et al.  SO-MI: A surrogate model algorithm for computationally expensive nonlinear mixed-integer black-box global optimization problems , 2013, Comput. Oper. Res..

[36]  Francisco Jurado,et al.  Particle swarm optimization for biomass-fuelled systems with technical constraints , 2008, Eng. Appl. Artif. Intell..

[37]  P. Venkatesh,et al.  Application of PSO technique for optimal location of FACTS devices considering system loadability and cost of installation , 2005, 2005 International Power Engineering Conference.

[38]  G. Venayagamoorthy,et al.  Comparison of Enhanced-PSO and Classical Optimization Methods: A Case Study for STATCOM Placement , 2009, International Conference on Intelligent System Applications to Power Systems.

[39]  K Sundareswaran,et al.  Optimal placement of Static VAr Compensators (SVC's) using Particle Swarm Optimization , 2010, 2010 International Conference on Power, Control and Embedded Systems.

[40]  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..

[41]  Abdelazeem A. Abdelsalam,et al.  Microgrid energy management in grid-connected and islanding modes based on SVC , 2014 .

[42]  Mohsen Gitizadeh,et al.  Allocation of multi-type FACTS devices using multi-objective genetic algorithm approach for power system reinforcement , 2010 .

[43]  Peerapol Jirapong,et al.  Optimal Placement of Multi-Type FACTS Devices for Total Transfer Capability Enhancement Using Hybrid Evolutionary Algorithm , 2007 .