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Unit Commitment using Genetic Algorithm for Thermal Units


Affiliations
1 M.E Power systems Engineering, University College of Engineering, BIT Campus–Tiruchirapalli-620024, India
2 Department of Electrical Engineering, University College of Engineering, BIT Campus–Tiruchirapalli,620024, India
     

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Unit commitment is a complex decision making process because of multiple constraints which must not be violated while finding the optimal or near optimal commitment schedule. The paper discusses the application of genetic algorithm to determine the short term commitment order of thermal units in power generation. The objective of the optimal commitment is to determine the on / off states of the units in the system to meet the load demand and spinning reserve requirement at each time period, such that the overall cost of generation is minimized, while satisfying various operational constraints. The paper examines the feasibility of using genetic algorithms, and report preliminary results in determining a near optimal commitment order of thermal units in a studied power system.

Keywords

Unit Commitment, Spinning Reserve, Genetic Algorithm, Load Demand, AFLC.
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  • Unit Commitment using Genetic Algorithm for Thermal Units

Abstract Views: 274  |  PDF Views: 2

Authors

G. Saravanan
M.E Power systems Engineering, University College of Engineering, BIT Campus–Tiruchirapalli-620024, India
H. Habeebullah Sait
Department of Electrical Engineering, University College of Engineering, BIT Campus–Tiruchirapalli,620024, India

Abstract


Unit commitment is a complex decision making process because of multiple constraints which must not be violated while finding the optimal or near optimal commitment schedule. The paper discusses the application of genetic algorithm to determine the short term commitment order of thermal units in power generation. The objective of the optimal commitment is to determine the on / off states of the units in the system to meet the load demand and spinning reserve requirement at each time period, such that the overall cost of generation is minimized, while satisfying various operational constraints. The paper examines the feasibility of using genetic algorithms, and report preliminary results in determining a near optimal commitment order of thermal units in a studied power system.

Keywords


Unit Commitment, Spinning Reserve, Genetic Algorithm, Load Demand, AFLC.