A hybrid approach based on PSO and EP for proficient solving of Unit Commitment Problem

Unit Commitment Problem (UCP) is a nonlinear mixed integer optimization problem used in the scheduling operation of power system generating units subjected to demand and reserve requirement constraints for achieving minimum operating cost. The task of the UC problem is to determine the on/off state of the generating units at every hour interval of the planning period for optimally transmitting the load and reserve among the committed units. The importance for the necessity of a more effective optimal solution to the UCP problem is increasing with the regularly varying demand. Hereby, we propose a hybrid approach which solves the unit commitment problem subjected to necessary constraints and gives the optimal commitment of the units. The possible combination of demand and their corresponding optimal generation schedule can be determined by the PSO algorithm. Being a global optimization technique, Evolutionary Programming (EP) for solving Unit Commitment Problem, operates on a method, which encodes each unit's operating schedule with respect to up/down time. When the demand over a time horizon is given as input to the network it successfully gives the schedule of each unit's commitment that satisfies the demands of all the periods and results in minimum total cost.

[1]  Chien-Lin Huang,et al.  Applying Particle Swarm Optimization to Schedule Order Picking Routes in a Distribution Center , 2007 .

[2]  Wentao Li,et al.  AN IMPROVED PARTICLE SWARM OPTIMIZATION ALGORITHM FOR PATTERN SYNTHESIS OF PHASED ARRAYS , 2008 .

[3]  William D'haeseleer,et al.  Long-term Unit Commitment optimisation for large power systems: unit decommitment versus advanced priority listing , 2002 .

[4]  Lei Wang,et al.  A Fuzzy Adaptive Programming Method of Particle Swarm Optimization , 2005, TENCON 2005 - 2005 IEEE Region 10 Conference.

[5]  Walter L. Snyder,et al.  Dynamic Programming Approach to Unit Commitment , 1987, IEEE Transactions on Power Systems.

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

[7]  Manuel A. Matos,et al.  Constrained unit commitment and dispatch optimization , 2006 .

[8]  M. R. Mohan,et al.  An evolutionary programming-based tabu search method for solving the unit commitment problem , 2004, IEEE Transactions on Power Systems.

[9]  Rehab F. Abdel-Kader Particle Swarm Optimization for Constrained Instruction Scheduling , 2008, VLSI Design.

[10]  X. Guan,et al.  The conditions for obtaining feasible solutions to security-constrained unit commitment problems , 2005 .

[11]  C. Sagastizábal,et al.  Solving the unit commitment problem of hydropower plants via Lagrangian Relaxation and Sequential Quadratic Programming , 2005 .

[12]  David C. Keezer,et al.  MEMS Switches and SiGe Logic for Multi-GHz Loopback Testing , 2008, VLSI Design.

[13]  Rabab M. Ramadan,et al.  FACE RECOGNITION USING PARTICLE SWARM OPTIMIZATION-BASED SELECTED FEATURES , 2009 .

[14]  Richi Nayak,et al.  A hybrid neural network and simulated annealing approach to the unit commitment problem , 2000 .

[15]  Belgin Emre Turkay,et al.  Evolutionary Algorithms for the Unit Commitment Problem , 2008 .

[16]  Grzegorz Dudek,et al.  Adaptive simulated annealing schedule to the unit commitment problem , 2010 .

[17]  D. P. Kothari,et al.  Security Constrained UCP with Operational and Power Flow Constraints , 2009 .

[18]  Hong-Tzer Yang,et al.  Evolutionary programming based economic dispatch for units with non-smooth fuel cost functions , 1996 .

[19]  Q. H. Wu,et al.  Power system optimal reactive power dispatch using evolutionary programming , 1995 .

[20]  R. P. Kumudini Devi,et al.  Hybrid Evolutionary Programming Approach to Multi-Area Unit Commitment with Import and Export Constraints , 2009 .

[21]  Mohamed Ben Messaoud,et al.  Nonlinear adaptive filters based on Particle Swarm Optimization , 2009 .

[22]  Claudio Gentile,et al.  Solving Nonlinear Single-Unit Commitment Problems with Ramping Constraints , 2006, Oper. Res..

[23]  Ali Keles,et al.  Binary differential evolution for the unit commitment problem , 2007, GECCO '07.

[24]  Muhammad Murtadha Othman,et al.  Evolutionary Programming Based Technique for Secure Operating Point Identification in Static Voltage Stability Assessment , 2008 .

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

[26]  Chiang-Tsung Huang,et al.  Dynamic security constrained multi-area unit commitment , 1991 .

[27]  M. Janga Reddy,et al.  Multipurpose Reservoir Operation Using Particle Swarm Optimization , 2007 .

[28]  K. Rameshkumar,et al.  Comparative evaluation of Particle Swarm Optimization Algorithms for Data Clustering using real world data sets , 2008 .

[29]  C. Christober Asir Rajan,et al.  Multi-Area Unit Commitment Using Hybrid Particle Swarm Optimization Technique with Import and Export Constraints , 2009 .

[30]  Mohd Wazir Mustafa,et al.  Structured genetic algorithm technique for unit commitment problem , 2009 .

[31]  Bipul Syam Purkayastha,et al.  HYBRID PSO/ SELF-ADAPTIVE EVOLUTIONARY PROGRAMS FOR ECONOMIC DISPATCH WITH NONSMOOTH COST FUNCTION , 2009 .