Automatic generation control of multi source power generation under deregulated environment

Abstract This paper presents a novel scheme for automatic generation control (AGC) of interconnected two area multi source power generation under deregulated environment. The conventional two-area AGC system is modified to take into account the effect of bilateral contracts on the dynamics. In the considered two area power system, each area contains two GENCOs; first GENCO is a reheat steam turbine with appropriate generation rate constraint nonlinearity and second GENCO is a gas turbine generation. The performances of integral (I), proportional-integral (PI), integral-derivative (ID) and proportional-integral-derivative (PID) are evaluated for the proposed AGC system in the deregulated environment. The gain of the controllers and speed regulation parameters are optimised using differential evolution (DE) algorithm. Differential evolution algorithm is used because of its convergence superiority and easy to implement. The performance of DE algorithm applied to the proposed problem is compared with that of Genetic Algorithm (GA) to establish its optimisation superiority of the former.

[1]  H. Bevrani.  Robust load frequency controller in a deregulated environment: a /spl mu/-synthesis approach , 1999, Proceedings of the 1999 IEEE International Conference on Control Applications (Cat. No.99CH36328).

[2]  Hassan Bevrani.  Reduced µ-based load frequency controller in a deregulated power system environment , 1999 .

[3]  M. A. Pai,et al.  Simulation and Optimization in an AGC System after Deregulation , 2001, IEEE Power Engineering Review.

[4]  Sidhartha Panda,et al.  Differential evolution algorithm for SSSC-based damping controller design considering time delay , 2011, J. Frankl. Inst..

[5]  Sidhartha Panda,et al.  Robust coordinated design of multiple and multi-type damping controller using differential evolution algorithm , 2011 .

[6]  S.C. Srivastava,et al.  A decentralized automatic generation control scheme for competitive electricity markets , 2006, IEEE Transactions on Power Systems.

[7]  A. P. Sakis Meliopoulos,et al.  Load-frequency control service in a deregulated environment , 1999, Decis. Support Syst..

[8]  Sakti Prasad Ghoshal,et al.  Application of GA based optimal integral gains in fuzzy based active power-frequency control of non-reheat and reheat thermal generating systems , 2003 .

[9]  Mohammad Teshnehlab,et al.  Load frequency controller design in a deregulated environment using flexible neural networks , 2000 .

[10]  Hassan Bevrani,et al.  Robust Power System Frequency Control , 2009 .

[11]  Aysen Demiroren,et al.  GA application to optimization of AGC in three-area power system after deregulation , 2007 .

[12]  S. P. Ghoshal,et al.  Application of GA/GA-SA based fuzzy automatic generation control of a multi-area thermal generating system , 2004 .

[13]  Lalit Chandra Saikia,et al.  AGC of a multi-area thermal system under deregulated environment using a non-integer controller , 2013 .

[14]  G. Sheblé,et al.  AGC simulator for price-based operation. II. Case study results , 1997 .

[15]  Somanath Majhi,et al.  Load frequency control of a realistic power system with multi-source power generation , 2012 .

[16]  R. D. Christie,et al.  Load frequency control issues in power system operations after deregulation , 1995 .

[17]  Gerald B. Sheblé,et al.  AGC simulator for price-based operation. I. A model , 1997 .

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

[19]  B. H. Bakken,et al.  Automatic generation control in a deregulated power system , 1998 .

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