Please use this identifier to cite or link to this item:
|Title:||Application of actor-critic learning algorithm for optimal bidding problem of a GenCo|
|Citation:||IEEE Transactions on Power Engineering Review 18(1), 11-18|
|Abstract:||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.|
|Appears in Collections:||Article|
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.