DocumentCode
2223653
Title
Multi-agent approach for profit based unit commitment
Author
Sharma, Deepak ; Srinivasan, Dipti ; Trivedi, Anupam
Author_Institution
Dept. of Electr. & Comput. Eng., Nat. Univ. of Singapore (NUS), Singapore, Singapore
fYear
2011
fDate
5-8 June 2011
Firstpage
2527
Lastpage
2533
Abstract
Deregulation in the electricity market offers freedom to the generator companies (GENCOs) to schedule their generators in order to maximize their profit without actually satisfying the load and the reserve requirements. Various techniques have been developed for solving the profit based unit commitment (PBUC) problem. Among them, the multi-agent approach is different where each generator unit is referred to as an intelligent agent. In this paper, we develop a new multi-agent approach for PBUC problem in which the rule based intelligence is provided to the independent system operator (ISO) agent. Intelligence of generator agents (GenAgents) is limited to maximize their profit for the given demand and reserve using real-parametric genetic algorithm (GA) and share the results with ISO agent. In this approach, ISO agent commits the maximum profit generating GenAgents for every hour while satisfying the up/down time constraints. ISO agent also asks other GenAgents to calculate their profit for the remaining demand and reserve. The simulation results of 10 units problem for two payment methods are shown and compared with other techniques.
Keywords
genetic algorithms; knowledge based systems; multi-agent systems; power engineering computing; power markets; electricity market; generator agents intelligence; generator companies; independent system operator agent; intelligent agent; multiagent approach; profit based unit commitment problem; real-parametric genetic algorithm; rule based intelligence; Equations; Generators; Genetic algorithms; ISO; ISO standards; Mathematical model; Time factors;
fLanguage
English
Publisher
ieee
Conference_Titel
Evolutionary Computation (CEC), 2011 IEEE Congress on
Conference_Location
New Orleans, LA
ISSN
Pending
Print_ISBN
978-1-4244-7834-7
Type
conf
DOI
10.1109/CEC.2011.5949932
Filename
5949932
Link To Document