Solution of unit commitment problem using quasi-oppositional teaching learning based algorithm

Abstract In this article, quasi-oppositional teaching learning based algorithm (QOTLBO) is proposed to solve thermal unit commitment (UC) problem. Teaching learning based algorithm (TLBO) is a recently developed meta-heuristic algorithm based on the effect of the influence of a teacher on the output of learners in a class. The objective of UC is to economically schedule generating units over a short-term planning horizon subjugating to the forecasted demand and other system operating constraints in order to meet the load demand and spinning reserve for each interval. The proposed method is implemented and tested using MATLAB programming. The tests are carried out using 10-unit system during a scheduling period of 24 h for four different cases. Additionally, the QOTLBO algorithm is also carried out for large scale power systems viz. 20, 60, 80 and 100 units to prove the scalability of the algorithm. The results confirm the potential and effectiveness of the proposed algorithm after comparison with various methods such as, simulated annealing (SA), genetic algorithm (GA), evolutionary programming (EP), differential evolution (DE), particle swarm optimization (PSO), improved PSO (IPSO), hybrid PSO (HPSO), binary coded PSO (BCPSO), quantum-inspired evolutionary algorithm (QEA), improved quantum-inspired evolutionary algorithm (IQEA), Muller method, quadratic model (QM), iterative linear algorithm (ILA), binary real coded firefly algorithm (BRCFF) and basic TLBO.

[1]  K. M. Dale,et al.  A Study of the Economic Shutdown of Generating Units in Daily Dispatch , 1959, Transactions of the American Institute of Electrical Engineers. Part III: Power Apparatus and Systems.

[2]  K. Manivannan,et al.  Improved genetic algorithm solution to unit commitment problem , 2002, IEEE/PES Transmission and Distribution Conference and Exhibition.

[3]  R. Venkata Rao,et al.  Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems , 2011, Comput. Aided Des..

[4]  Whei-Min Lin,et al.  An efficient hybrid Taguchi-immune algorithm for the unit commitment problem , 2011, Expert Syst. Appl..

[5]  A. J. Svoboda,et al.  Short-term resource scheduling with ramp constraints [power generation scheduling] , 1997 .

[6]  Shahryar Rahnamayan,et al.  Quasi-oppositional Differential Evolution , 2007, 2007 IEEE Congress on Evolutionary Computation.

[7]  Qiaozhu Zhai,et al.  Subhourly unit commitment with feasible energy delivery constraints , 2012 .

[8]  K.-i. Tokoro,et al.  Soving unit commitment problem by combining of continuous relaxation method and genetic algorithm , 2008, 2008 SICE Annual Conference.

[9]  S. K. Goswami,et al.  Fuzzy and simulated annealing based dynamic programming for the unit commitment problem , 2009, Expert Syst. Appl..

[10]  Provas Kumar Roy,et al.  Optimal reactive power dispatch using quasi-oppositional teaching learning based optimization , 2013 .

[11]  Ganesh Kumar Venayagamoorthy,et al.  Unit commitment with vehicle-to-Grid using particle swarm optimization , 2009, 2009 IEEE Bucharest PowerTech.

[12]  D. P. Kothari,et al.  Unit commitment problem solution using invasive weed optimization algorithm , 2014 .

[13]  Fernando Magnago,et al.  Symmetry issues in mixed integer programming based Unit Commitment , 2014 .

[14]  S. Virmani,et al.  Implementation of a Lagrangian Relaxation Based Unit Commitment Problem , 1989, IEEE Power Engineering Review.

[15]  Danny H. K. Tsang,et al.  Unit commitment problem: A new formulation and solution method , 2014 .

[16]  Provas Kumar Roy,et al.  Solution of unit commitment problem using gravitational search algorithm , 2013 .

[17]  Provas Kumar Roy,et al.  Teaching learning based optimization for short-term hydrothermal scheduling problem considering valve point effect and prohibited discharge constraint , 2013 .

[18]  Nima Amjady,et al.  Security constrained unit commitment of power systems by a new combinatorial solution strategy composed of enhanced harmony search algorithm and numerical optimization , 2013 .

[19]  Narayana Prasad Padhy,et al.  Thermal unit commitment using binary/real coded artificial bee colony algorithm , 2012 .

[20]  Sishaj P. Simon,et al.  Nodal ant colony optimization for solving profit based unit commitment problem for GENCOs , 2012, Appl. Soft Comput..

[21]  T.O. Ting,et al.  A novel approach for unit commitment problem via an effective hybrid particle swarm optimization , 2006, IEEE Transactions on Power Systems.

[22]  Anastasios G. Bakirtzis,et al.  A genetic algorithm solution to the unit commitment problem , 1996 .

[23]  Shahryar Rahnamayan,et al.  Opposition versus randomness in soft computing techniques , 2008, Appl. Soft Comput..

[24]  Hong-Tzer Yang,et al.  A new thermal unit commitment approach using constraint logic programming , 1997 .

[25]  Kit Po Wong,et al.  An Advanced Quantum-Inspired Evolutionary Algorithm for Unit Commitment , 2011, IEEE Transactions on Power Systems.

[26]  Chuangxin Guo,et al.  An improved particle swarm optimization algorithm for unit commitment , 2006 .

[27]  S. Jalilzadeh,et al.  An Improved Genetic Algorithm for unit commitment problem with lowest cost , 2009, 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems.

[28]  K. S. Swarp,et al.  Unit Connuitment Solution Methodology Using Genetic Algorithm , 2002, IEEE Power Engineering Review.

[29]  T. Funabashi,et al.  Unit Commitment Computation - A Novel Fuzzy Adaptive Particle Swarm Optimization Approach , 2006, 2006 IEEE PES Power Systems Conference and Exposition.

[30]  Sakti Prasad Ghoshal,et al.  A novel opposition-based gravitational search algorithm for combined economic and emission dispatch problems of power systems , 2012 .

[31]  I. Erlich,et al.  A new approach for solving the unit commitment problem by adaptive particle swarm optimization , 2008, 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century.

[32]  Tomonobu Senjyu,et al.  Fast technique for unit commitment by genetic algorithm based on unit clustering , 2005 .

[33]  F. N. Lee.  A fuel-constrained unit commitment method , 1989 .

[34]  Hamid R. Tizhoosh,et al.  Opposition-Based Learning: A New Scheme for Machine Intelligence , 2005, International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06).

[35]  M. A. Abido,et al.  A simulated annealing algorithm for fuzzy unit commitment problem , 1999, 1999 IEEE Transmission and Distribution Conference (Cat. No. 99CH36333).

[36]  Sakti Prasad Ghoshal,et al.  Solution of combined economic and emission dispatch problems of power systems by an opposition-based harmony search algorithm , 2012 .

[37]  K. Chandram,et al.  Unit Commitment by improved pre-prepared power demand table and Muller method , 2011 .

[38]  Provas Kumar Roy,et al.  Quasi-oppositional Biogeography-based Optimization for Multi-objective Optimal Power Flow , 2011 .

[39]  Weerakorn Ongsakul,et al.  Ramp rate constrained unit commitment by improved priority list and augmented Lagrange Hopfield network , 2008 .

[40]  J. Jian,et al.  Tighter relaxation method for unit commitment based on second-order cone programming and valid inequalities , 2014 .

[41]  C. Christober Asir Rajan,et al.  Genetic algorithm based simulated annealing method for solving unit commitment problem in utility system , 2007, T&D 2010.

[42]  Y. Pourjamal,et al.  HSA based solution to the UC problem , 2013 .

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

[44]  T. Funabashi,et al.  Thermal unit commitment strategy with solar and wind energy systems using genetic algorithm operated particle swarm optimization , 2008, 2008 IEEE 2nd International Power and Energy Conference.

[45]  D. Bertsekas,et al.  Solution of Large-Scale Optimal Unit Commitment Problems , 1982, IEEE Transactions on Power Apparatus and Systems.

[46]  H. H. Happ,et al.  Large Scale Hydro-Thermal Unit Commitment-Method and Results , 1971 .

[47]  Tomonobu Senjyu,et al.  Unit commitment computation by fuzzy adaptive particle swarm optimisation , 2007 .

[48]  Costas Vournas,et al.  Unit commitment by an enhanced simulated annealing algorithm , 2006 .

[49]  A. Bakirtzis,et al.  A solution to the unit-commitment problem using integer-coded genetic algorithm , 2004, IEEE Transactions on Power Systems.

[50]  K. Chandrasekaran,et al.  Network and reliability constrained unit commitment problem using binary real coded firefly algorithm , 2012 .

[51]  R. Venkata Rao,et al.  Teaching-Learning-Based Optimization: An optimization method for continuous non-linear large scale problems , 2012, Inf. Sci..

[52]  Boris Pavez-Lazo,et al.  A deterministic annular crossover genetic algorithm optimisation for the unit commitment problem , 2011, Expert Syst. Appl..

[53]  Arthur I. Cohen,et al.  A Branch-and-Bound Algorithm for Unit Commitment , 1983, IEEE Transactions on Power Apparatus and Systems.

[54]  T. Senjyu,et al.  Unit commitment strategy of thermal generators by using advanced fuzzy controlled binary particle swarm optimization algorithm , 2012 .

[55]  Risto Lahdelma,et al.  A variant of the dynamic programming algorithm for unit commitment of combined heat and power systems , 2008, Eur. J. Oper. Res..

[56]  Whei-Min Lin,et al.  Hybrid Taguchi-Immune Algorithm for the thermal unit commitment , 2011 .

[57]  Provas Kumar Roy,et al.  Oppositional biogeography-based optimisation for optimal power flow , 2014 .

[58]  Erlon Cristian Finardi,et al.  Hydro unit commitment and loading problem for day-ahead operation planning problem , 2013 .

[59]  Sleiman Mhanna,et al.  Application of semidefinite programming relaxation and selective pruning to the unit commitment problem , 2012 .

[60]  Sohrab Khanmohammadi,et al.  A primary unit commitment approach with a modification process , 2013, Appl. Soft Comput..

[61]  Haiping Ma,et al.  Oppositional ant colony optimization algorithm and its application to fault monitoring , 2010, Proceedings of the 29th Chinese Control Conference.

[62]  W. Ongsakul,et al.  Ant colony search algorithm for unit commitment , 2003, IEEE International Conference on Industrial Technology, 2003.

[63]  Takeshi Nagata,et al.  A Solution for Unit Commitment Using Lagrangian Relaxation Combined with Evolutionary Programming , 1996 .

[64]  Ana Viana,et al.  A new MILP based approach for unit commitment in power production planning , 2013 .

[65]  S. M. Shahidehpour,et al.  An intelligent dynamic programming for unit commitment application , 1991 .

[66]  W. L. Peterson,et al.  A capacity based Lagrangian relaxation unit commitment with ramp rate constraints , 1995 .

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