A new improved hybrid algorithm for congestion management in a deregulated electricity industry using chaos enhanced differential evolution

Congestion management (CM) is one of the severe problems in deregulated or liberalized electricity industry. In a liberalized electricity market, transmission lines are normally operated near its rated capacity and sometimes, this may cause congestion in the lines. This is because every market participants have the intention to maximize their profits. Failure of managing congestion properly may ultimately lead to the system collapse. Several methods including both classical and modern heuristic techniques have been used to address the problems of congestion management with different level of success. A novel hybrid algorithm based on differential evolution and chaos theory is presented in the present paper to alleviate congestion in power systems by rescheduling of participating generators. The algorithm tested on a standard system to demonstrate its capability in alleviating congestion of power systems. A comparative result is also presented in the present work. It is observed that the present technique is capable in improving the quality of solutions.

[1]  Xu Wang,et al.  A Congestion Management Approach Using Probabilistic Power Flow Considering Direct Electricity Purchase , 2015 .

[2]  Nima Amjady,et al.  Congestion management enhancing transient stability of power systems , 2010 .

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

[4]  S. Oren,et al.  Flow-Based Transmission Rights and Congestion Management , 2000 .

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

[6]  D. Thukaram,et al.  Congestion management in open access based on relative electrical distances using voltage stability criteria , 2007 .

[7]  V. Mukherjee,et al.  A novel symbiotic organisms search algorithm for congestion management in deregulated environment , 2017, J. Exp. Theor. Artif. Intell..

[8]  Anwar Shahzad Siddiqui,et al.  Congestion management in deregulated power system using FACTS device , 2017, Int. J. Syst. Assur. Eng. Manag..

[9]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[10]  K. P. Vijayakumar.  Congestion management in deregulated power systems , 2013 .

[11]  Yi Ding,et al.  Congestion Management Strategies of Real-Time Market , 2014 .

[12]  O. Alsac,et al.  Optimal Load Flow with Steady-State Security , 1974 .

[13]  R. Storn,et al.  Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces , 2004 .

[14]  Di Xiao,et al.  A novel Hash algorithm construction based on chaotic neural network , 2011, Neural Computing and Applications.

[15]  Anwar Shahzad Siddiqui,et al.  An efficient particle swarm optimizer for congestion management in deregulated electricity market , 2015 .

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

[17]  A. K. David,et al.  An integrated congestion management strategy for real-time system operation , 1999 .