Approximate and Reinforcement Learning techniques to solve non-convex Economic Dispatch problems

Economic Dispatch is one of the power systems management tools. It is used to allocate an amount of power generation to the generating units to meet the active load demands. The Economic Dispatch problem is a large-scale nonlinear constrained optimization problem. In this paper, two novel techniques are developed to solve the non-convex Economic Dispatch problem. Firstly, a novel approximation of the non-convex generation cost function is developed to solve non-convex Economic Dispatch problem with the transmission losses. This approximation enables the use of gradient and Newton techniques to solve the non-convex Economic Dispatch problem. Secondly, Q-Learning with eligibility traces technique is adopted to solve the non-convex Economic Dispatch problem with valve point loading effects, multiple fuel options, and power transmission losses. The eligibility traces are used to speed up the Q-Learning process. This technique showed superior results compared to other heuristic techniques.