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

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

Desarrollo de un algoritmo de aprendizaje por refuerzo profundo para resolver el despacho hidrotérmico colombiano considerando escenarios hidrológicos y de demanda bajo incertidumbre

El despacho económico es un problema de optimización ampliamente analizado en el sector eléctrico, que busca hacer el mejor uso de los recursos disponibles para satisfacer la demanda a mínimo costo. Este problema presenta un gran reto en su solución debido a la incertidumbre de múltiples parámetros, como la demanda de energía eléctrica, y para el caso colombiano es de especial interés la incertidumbre hidrológica por su alta dependencia en centrales hidroeléctricas. Dado que el despacho económico se asemeja a un problema de decisiones secuenciales, es posible modelar el problema como un proceso de decisión de Markov, lo que permite incorporar en la modelación la incertidumbre de los parámetros de interés. El presente proyecto propone una modelación del modelo de despacho económico colombiano como un proceso de decisión de Markov, considerando la incertidumbre en la demanda y la hidrología. Luego, a través de algoritmos de aprendizaje reforzado profundo se determina una política óptima y robusta para dar un mejor manejo a los recursos disponibles frente al manejo de la demanda energética.Economic dispatch is a widely analyzed optimization problem in the electricity sector, which seeks to make the best use of available resources to meet demand at minimum cost. This problem has a great complexity in its solution due to the uncertainty of multiple parameters, being of special interest the hydrological uncertainty for the Colombian case due to its high dependence on hydroelectric plants. In this paper, we view economic dispatch as a multistage decision making problem and propose a Reinforcement Learning to solve the Colombian economic dispatch problem considering hydrological scenarios, due to its ability to handle uncertainty and sequential decisions. The policy performance of our algorithm is compared with classic deterministic method. The main advantage of our method is it can learn from a robust policy to deal the inflow and load demand scenarios