An optimum forceful generation scheduling and unit commitment of thermal power system using sine cosine algorithm

Conventional thermal power system-based units and its participation schedule known as unit commitment problem (UCP) is a significant and stimulating undertaking of allocating generated power among the dedicated units subject to numerous restrictions above a scheduled time prospect to obtain the slightest generation cost. This problem becomes further more complex by increasing the size of the power system. Since unit commitment problem is link optimization problem as it has both binary and continuous variable that is why it is most challenging problem to solve. In this paper, a recently invented optimizer sine–cosine is used to solve unit commitment problem. Sine cosine algorithm (SCA) is an innovative population centered optimization algorithm that has been used for solving the unit commitment optimization problems bounded by some constraints centered on the concept of a mathematical model of the sine and cosine functions. This paper offers the solution of unit commitment optimization problems of the electric power system by using the SCA, as UCP is linked optimization as it has both binary and continuous variables, the strategy adopted to tackle both variables is different. In this paper, proposed sine cosine algorithm searches allocation of generators (units that participate in generation to take upload) and once units are decided, allocation of generations (economic load dispatch) is done by mixed integer quadratic programming. The feasibility and efficacy of operation of SCA algorithm are verified for small- and medium-power systems, in which results for 4 unit, 5 unit, 6 unit, 7 unit, 10 units, 19 unit, 20 unit and 40 units are evaluated. The 10 generating units are evaluated with 5% and 10% spinning reserve. The results obviously show that the suggested method gives the superior type of solutions as compared to other algorithms.

[1]  MirjaliliSeyedali,et al.  Grasshopper Optimisation Algorithm , 2017 .

[2]  S. M. Shahidehpour,et al.  A multi-stage intelligent system for unit commitment , 1992 .

[3]  Erik Valdemar Cuevas Jiménez,et al.  A novel evolutionary algorithm inspired by the states of matter for template matching , 2013, Expert Syst. Appl..

[4]  S. Najafi,et al.  A New Heuristic Algorithm for Unit Commitment Problem , 2012 .

[5]  Seyedali Mirjalili,et al.  SCA: A Sine Cosine Algorithm for solving optimization problems , 2016, Knowl. Based Syst..

[6]  Xin-She Yang,et al.  Firefly algorithm, stochastic test functions and design optimisation , 2010, Int. J. Bio Inspired Comput..

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

[8]  A. Ebenezer Jeyakumar,et al.  Hybrid PSO–SQP for economic dispatch with valve-point effect , 2004 .

[9]  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.

[10]  C. Beltran,et al.  Unit Commitment by Augmented Lagrangian Relaxation: Testing Two Decomposition Approaches , 2002 .

[11]  Seema Singh,et al.  Multi-objective unit commitment using search space-based crazy particle swarm optimisation and normal boundary intersection technique , 2016 .

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

[13]  Natalio Krasnogor,et al.  Nature‐inspired cooperative strategies for optimization , 2009, Int. J. Intell. Syst..

[14]  S. Oren,et al.  Solving the Unit Commitment Problem by a Unit Decommitment Method , 2000 .

[15]  C.D. Vournas,et al.  Unit Commitment by an Enhanced Simulated Annealing Algorithm , 2006, 2006 IEEE PES Power Systems Conference and Exposition.

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

[17]  Gerald B. Sheblé,et al.  Unit commitment by genetic algorithm with penalty methods and a comparison of Lagrangian search and genetic algorithm—economic dispatch example , 1996 .

[18]  C.-P. Cheng,et al.  Unit commitment by annealing-genetic algorithm , 2002 .

[19]  Xin-She Yang,et al.  Firefly Algorithm, Lévy Flights and Global Optimization , 2010, SGAI Conf..

[20]  Xin Yao,et al.  Evolutionary programming made faster , 1999, IEEE Trans. Evol. Comput..

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

[22]  I. J. Raglend,et al.  Solution of unit commitment problem using Shuffled Frog Leaping Algorithm , 2012, 2012 International Conference on Computing, Electronics and Electrical Technologies (ICCEET).

[23]  Tomonobu Senjyu,et al.  Fuzzy quantum computation based thermal unit commitment strategy with solar-battery system injection , 2011, 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011).

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

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

[26]  Tomonobu Senjyu,et al.  A unit commitment problem by using genetic algorithm based on unit characteristic classification , 2002, 2002 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.02CH37309).

[27]  Ning Zhang,et al.  A fuzzy chance-constrained program for unit commitment problem considering demand response, electric vehicle and wind power , 2015 .

[28]  MirjaliliSeyedali Moth-flame optimization algorithm , 2015 .

[29]  Nasser Sadati,et al.  Unit Commitment Using Particle Swarm-Based-Simulated Annealing Optimization Approach , 2007, 2007 IEEE Swarm Intelligence Symposium.

[30]  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.

[31]  G. M. Casolino,et al.  Combined cycle unit commitment in a changing electricity market scenario , 2015 .

[33]  Hatim S. Madraswala,et al.  Genetic algorithm solution to unit commitment problem , 2016, 2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES).

[34]  Hossein Nezamabadi-pour,et al.  BGSA: binary gravitational search algorithm , 2010, Natural Computing.

[35]  Zhang Hong-peng,et al.  SOCIAL EVOLUTIONARY PROGRAMMING BASED UNIT COMMITMENT , 2004 .

[36]  D. P. Kothari,et al.  A solution to unit commitment problem using fire works algorithm , 2016 .

[37]  Xin-She Yang,et al.  Flower pollination algorithm: A novel approach for multiobjective optimization , 2014, ArXiv.

[38]  F. Albuyeh,et al.  Evaluation of Dynamic Programming Based Methods and Multiple area Representation for Thermal Unit Commitments , 1981, IEEE Transactions on Power Apparatus and Systems.

[39]  Vikram Kumar Kamboj,et al.  Implementation of hybrid harmony search/random search algorithm for single area unit commitment problem , 2016 .

[40]  W. Ongsakul,et al.  Unit commitment by enhanced adaptive Lagrangian relaxation , 2004, IEEE Transactions on Power Systems.

[41]  Sohrab Khanmohammadi,et al.  A new three-stage method for solving unit commitment problem , 2010 .

[42]  Tomonobu Senjyu,et al.  Emerging solution of large-scale unit commitment problem by Stochastic Priority List , 2006 .

[43]  William D'haeseleer,et al.  Enhanced priority list unit commitment method for power systems with a high share of renewables , 2013 .

[44]  杨林峰,et al.  An improved priority list and neighborhood search method for unit commitment , 2015 .

[45]  Ran Quan,et al.  An improved priority list and neighborhood search method for unit commitment , 2015 .

[46]  Xin-She Yang,et al.  A New Metaheuristic Bat-Inspired Algorithm , 2010, NICSO.

[47]  Narayana Prasad Padhy,et al.  Unit commitment using hybrid models: a comparative study for dynamic programming, expert system, fuzzy system and genetic algorithms , 2001 .

[48]  R. K. Ursem Multi-objective Optimization using Evolutionary Algorithms , 2009 .

[49]  K. Manivannan,et al.  Neural Based Tabu Search method for solving unit commitment problem , 2002 .

[50]  Andrew Lewis,et al.  Grasshopper Optimisation Algorithm: Theory and application , 2017, Adv. Eng. Softw..

[51]  S. Chusanapiputt,et al.  A solution to unit commitment problem using hybrid ant system/priority list method , 2008, 2008 IEEE 2nd International Power and Energy Conference.

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

[53]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[54]  F. N. Lee,et al.  Short-term thermal unit commitment-a new method , 1988 .

[55]  T. O. Ting,et al.  Methodological Priority List for Unit Commitment Problem , 2008, 2008 International Conference on Computer Science and Software Engineering.

[56]  Junzo Watada,et al.  Re-scheduling the unit commitment problem in fuzzy environment , 2011, 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011).

[57]  Seyedali Mirjalili,et al.  Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems , 2015, Neural Computing and Applications.

[58]  Xiaohui Yuan,et al.  Application of quantum-inspired binary gravitational search algorithm for thermal unit commitment with wind power integration , 2014 .

[59]  H.K.M. Youssef,et al.  A two-stage genetic based technique for the unit commitment optimization problem , 2008, 2008 12th International Middle-East Power System Conference.

[60]  Jong-Bae Park,et al.  Thermal Unit Commitment Using Binary Differential Evolution , 2009 .

[61]  Andrew Lewis,et al.  The Whale Optimization Algorithm , 2016, Adv. Eng. Softw..

[62]  I. Jacob Raglend,et al.  Solution of emission constrained Unit Commitment problem using Shuffled Frog Leaping Algorithm , 2013 .

[63]  Lan Fei,et al.  A Solution to the Unit Commitment Problem Based on Local Search Method , 2009, 2009 International Conference on Energy and Environment Technology.

[64]  Seema Singh,et al.  Advanced three-stage pseudo-inspired weight-improved crazy particle swarm optimization for unit commitment problem , 2016 .

[65]  D. P. Kothari,et al.  Power system optimization , 2004, 2012 2nd National Conference on Computational Intelligence and Signal Processing (CISP).

[66]  Sangwook Lee,et al.  Binary Particle Swarm Optimization with Bit Change Mutation , 2007, IEICE Trans. Fundam. Electron. Commun. Comput. Sci..

[67]  M. Rashidinejad,et al.  An implementation of harmony search algorithm to unit commitment problem , 2010 .

[68]  Andrew Lewis,et al.  Grey Wolf Optimizer , 2014, Adv. Eng. Softw..

[69]  Russell C. Eberhart,et al.  A discrete binary version of the particle swarm algorithm , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[70]  Wei Xiong,et al.  An Improved Particle Swarm Optimization Algorithm for Unit Commitment , 2008, 2008 International Conference on Intelligent Computation Technology and Automation (ICICTA).

[71]  Marko Čepin,et al.  Multi-objective unit commitment with introduction of a methodology for probabilistic assessment of generating capacities availability , 2015, Eng. Appl. Artif. Intell..

[72]  Xin-She Yang,et al.  Flower Pollination Algorithm for Global Optimization , 2012, UCNC.

[73]  Marte A. Ramírez-Ortegón,et al.  An optimization algorithm inspired by the States of Matter that improves the balance between exploration and exploitation , 2013, Applied Intelligence.

[74]  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.

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

[76]  Lixin Tang,et al.  An Improved Binary Particle Swarm Optimization for Unit Commitment Problem , 2010, 2010 Asia-Pacific Power and Energy Engineering Conference.

[77]  R. Naresh,et al.  A modified binary artificial bee colony algorithm for ramp rate constrained unit commitment problem , 2015 .

[78]  Jong-Bae Park,et al.  A New Quantum-Inspired Binary PSO for Thermal Unit Commitment Problems , 2009, 2009 15th International Conference on Intelligent System Applications to Power Systems.

[79]  Kevin Barraclough,et al.  I and i , 2001, BMJ : British Medical Journal.

[80]  Seyed Mohammad Mirjalili,et al.  Multi-Verse Optimizer: a nature-inspired algorithm for global optimization , 2015, Neural Computing and Applications.

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

[82]  Allen J. Wood,et al.  Power Generation, Operation, and Control , 1984 .

[83]  P. Sriyanyong,et al.  Unit commitment using particle swarm optimization combined with Lagrange relaxation , 2005, IEEE Power Engineering Society General Meeting, 2005.

[84]  Gerald B. Sheblé,et al.  Unit commitment literature synopsis , 1994 .

[85]  Hossam Faris,et al.  Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems , 2017, Adv. Eng. Softw..

[86]  J. Pal,et al.  A new genetic approach for solving the unit commitment problem , 2004, 2004 International Conference on Power System Technology, 2004. PowerCon 2004..

[87]  Marshall L. Fisher,et al.  The Lagrangian Relaxation Method for Solving Integer Programming Problems , 2004, Manag. Sci..

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

[89]  Chu Kiong Loo,et al.  Solving Unit Commitment Problem Using Hybrid Particle Swarm Optimization , 2003, J. Heuristics.

[90]  Nan Liu,et al.  The defect of the Grey Wolf optimization algorithm and its verification method , 2019, Knowl. Based Syst..

[91]  Xin-She Yang,et al.  Cuckoo Search via Lévy flights , 2009, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC).

[92]  Raca Todosijevic,et al.  VNS based heuristic for solving the Unit Commitment problem , 2012, Electron. Notes Discret. Math..

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

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

[95]  Hossein Nezamabadi-pour,et al.  GSA: A Gravitational Search Algorithm , 2009, Inf. Sci..

[96]  Seyed Mohammad Mirjalili,et al.  The Ant Lion Optimizer , 2015, Adv. Eng. Softw..

[97]  Seyed Mohammad Mirjalili,et al.  Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm , 2015, Knowl. Based Syst..

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