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Application of actor-critic learning algorithm for optimal bidding problem of a GenCo

DSpace at IIT Bombay

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Title Application of actor-critic learning algorithm for optimal bidding problem of a GenCo
 
Creator GAJJAR, GR
KHAPARDE, SA
NAGARAJU, P
SOMAN, SA
 
Subject markov processes
optimization
algorithms
learning
game theory
 
Description The optimal bidding for Genco in a deregulated power market is an involved task. The problem is formulated in the framework of Markov decision process (MDP), a discrete stochastic optimization method. When the time span considered is 24 h, the temporal difference method becomes attractive for application. The cumulative profit over the span is the objective function to be optimized. The temporal difference technique and actor-critic learning algorithm are employed. An optimal strategy is devised to maximize the profit. The market-clearing system is included in the formulation. Simulation cases of three, seven, and ten participants are considered and the obtained results are discussed.
 
Publisher IEEE
 
Date 2008-11-24T05:40:23Z
2011-11-25T16:03:15Z
2011-12-26T13:05:24Z
2011-12-27T05:52:12Z
2008-11-24T05:40:23Z
2011-11-25T16:03:15Z
2011-12-26T13:05:24Z
2011-12-27T05:52:12Z
2003
 
Type Article
 
Identifier IEEE Transactions on Power Engineering Review 18(1), 11-18
0885-8950
http://dx.doi.org/10.1109/TPWRS.2002.807041
http://hdl.handle.net/10054/131
http://dspace.library.iitb.ac.in/xmlui/handle/10054/131
 
Language en_US