Open Science Research Excellence
%0 Journal Article
%A Mohammed I. Abouheaf and  Sofie Haesaert and  Wei-Jen Lee and  Frank L. Lewis
%D 2012 
%J  International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering
%B World Academy of Science, Engineering and Technology
%I International Science Index 67, 2012
%T Q-Learning with Eligibility Traces to Solve Non-Convex Economic Dispatch Problems
%U http://waset.org/publications/17298
%V 67
%X Economic Dispatch is one of the most important power system management tools. It is used to allocate an amount of power generation to the generating units to meet the load demand. The Economic Dispatch problem is a large scale nonlinear constrained optimization problem. In general, heuristic optimization techniques are used to solve non-convex Economic Dispatch problem. In this paper, ideas from Reinforcement Learning are proposed to solve the non-convex Economic Dispatch problem. Q-Learning is a reinforcement learning techniques where each generating unit learn the optimal schedule of the generated power that minimizes the generation cost function. The eligibility traces are used to speed up the Q-Learning process. Q-Learning with eligibility traces is used to solve Economic Dispatch problems with valve point loading effect, multiple fuel options, and power transmission losses.

%P 723 - 730