A modified approach to solution of Unit Commitment problem using Mendel's GA method

Phenomenal increase in load and cost of electricity has raised many challenges ranging from security of the system to the economics of generation. For economic operation of power system, the solution to Unit Commitment problem is necessary. Unit Commitment aims to schedule the power generation to meet the load demands at the most economical rate for the next few hours. It decides that which unit should be operated in that particular period of study and which should not. On time horizon basis, it can be varied from few hours to one week. In this paper unit commitment problem is solved using modified Mendel's GA approach for standard 4 unit system scheduled for 8 hours with 1-hour time interval. The results obtained show that the proposed technique is able to achieve the solution of unit commitment problem in lesser number of trials and in minimum cost as compared to the conventional approach.

[1]  Dilip Datta,et al.  A binary-real-coded differential evolution for unit commitment problem , 2012 .

[2]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[3]  Min-Jea Tahk,et al.  Modified Mendel operation for multimodal function optimization , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[4]  C. Fonseca,et al.  GENETIC ALGORITHMS FOR MULTI-OBJECTIVE OPTIMIZATION: FORMULATION, DISCUSSION, AND GENERALIZATION , 1993 .

[5]  Peter J. Fleming,et al.  Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization , 1993, ICGA.

[6]  Yanbin Yuan,et al.  An improved binary particle swarm optimization for unit commitment problem , 2009, Expert Syst. Appl..

[7]  A. H. Mantawy,et al.  A new genetic-based tabu search algorithm for unit commitment problem , 1999 .

[8]  Chern-Lin Chen,et al.  Branch-and-bound scheduling for thermal generating units , 1993 .

[9]  B. A. Kadrovach,et al.  Use of mendelian pressure in a multi-objective genetic algorithm , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[10]  B. Vahidi,et al.  Bacterial Foraging-Based Solution to the Unit-Commitment Problem , 2009, IEEE Transactions on Power Systems.

[11]  P. K. Singhal.  Generation scheduling methodology for thermal units using lagrangian Relaxation , 2011, 2011 Nirma University International Conference on Engineering.

[12]  Prateek Kumar Singhal,et al.  Dynamic programming approach for solving power generating unit commitment problem , 2011, 2011 2nd International Conference on Computer and Communication Technology (ICCCT-2011).

[13]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[14]  Eiichi Tanaka,et al.  An Evolutionary Programming Solution to the Unit Commitment Problem , 1997 .

[15]  Kalyanmoy Deb,et al.  Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms , 1994, Evolutionary Computation.

[16]  Abbas Khosravi,et al.  A computational framework for uncertainty integration in stochastic unit commitment with intermittent renewable energy sources , 2015 .

[17]  K. Uezato,et al.  A fast technique for unit commitment problem by extended priority list , 2003, 2003 IEEE Power Engineering Society General Meeting (IEEE Cat. No.03CH37491).

[18]  Guangfeng Zhang,et al.  Exchange rates determination based on genetic algorithms using Mendel's principles: Investigation and estimation under uncertainty , 2013, Inf. Fusion.

[19]  David E. Goldberg,et al.  A niched Pareto genetic algorithm for multiobjective optimization , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[20]  Chuan-Ping Cheng,et al.  Unit commitment by Lagrangian relaxation and genetic algorithms , 2000 .

[21]  A genetic algorithm with a Mendel operator for global minimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[22]  Philip G. Hill,et al.  Power generation , 1927, Journal of the A.I.E.E..