Mohammed I. Abouheaf and Sofie Haesaert and Wei-Jen Lee and Frank L. Lewis QLearning with Eligibility Traces to Solve NonConvex Economic Dispatch Problems
723 - 730
2012
6
7
International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering http://waset.org/publications/17298
http://waset.org/publications/67
World Academy of Science, Engineering and Technology
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 nonconvex Economic Dispatch problem. In this paper, ideas from Reinforcement Learning are proposed to solve the nonconvex Economic Dispatch problem. QLearning 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 QLearning process. QLearning with eligibility traces is used to solve Economic Dispatch problems with valve point loading effect, multiple fuel options, and power transmission losses.
International Science Index 67, 2012