DocumentCode :
1180224
Title :
Application of Actor-Critic Learning Algorithm for Optimal Bidding Problem of a Genco
Author :
Gajjar, G. R. ; Khaparde, S. A. ; Nagaraju, P. ; Soman, S. A.
Author_Institution :
IIT Bombay, India
Volume :
22
Issue :
11
fYear :
2002
Firstpage :
55
Lastpage :
55
Abstract :
The optimal bidding for generation companies (GenCo) in the deregulated power market is an involved task. The problem is formulated in the framework of the Markov decision process (MDP), a discrete stochastic optimization method. When the time span considered is 24 hours, 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 is employed. An optimal strategy is devised to maximize the profit. A market-clearing system is included in the formulation. Simulation cases of three, seven, and ten participants are considered, and the results obtained are discussed.
Keywords :
Computer networks; Costs; Distributed computing; Economic forecasting; Fuzzy sets; Optimization methods; Power generation; Power generation economics; Power markets; Stochastic processes; Energy auction; Markov decision process; actor-critic learning algorithm; bidding strategies;
fLanguage :
English
Journal_Title :
Power Engineering Review, IEEE
Publisher :
ieee
ISSN :
0272-1724
Type :
jour
DOI :
10.1109/MPER.2002.4311813
Filename :
4311813
Link To Document :
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