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Please use this identifier to cite or link to this item: http://purl.org/purl/4490

Title: A Reinforcement Learning Approach to Economic Dispatch using Neural Networks
Authors: Jagathy Raj, V P
Jasmin, E A
Imthias Ahamed, T P
Issue Date: Dec-2008
Abstract: This paper presents a Reinforcement Learning (RL) approach to economic dispatch (ED) using Radial Basis Function neural network. We formulate the ED as an N stage decision making problem. We propose a novel architecture to store Qvalues and present a learning algorithm to learn the weights of the neural network. Even though many stochastic search techniques like simulated annealing, genetic algorithm and evolutionary programming have been applied to ED, they require searching for the optimal solution for each load demand. Also they find limitation in handling stochastic cost functions. In our approach once we learn the Q-values, we can find the dispatch for any load demand. We have recently proposed a RL approach to ED. In that approach, we could find only the optimum dispatch for a set of specified discrete values of power demand. The performance of the proposed algorithm is validated by taking IEEE 6 bus system, considering transmission losses
Description: Fifteenth National Power Systems Conference (NPSC), IIT Bombay, December 2008
URI: http://dyuthi.cusat.ac.in/purl/4490
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