Managing Distributed Flexibility Under Uncertainty by Combining Deep Learning With Duality

In modern power systems, small distributed energy resources (DERs) are considered a valuable source of flexibility towards accommodating high penetration of Renewable Energy Sources (RES). In this paper we consider an economic dispatch problem for a community of DERs, where energy management decisions are made online and under uncertainty. We model multiple sources of uncertainty such as RES, wholesale electricity prices as well as the arrival times and energy needs of a set of Electric Vehicles. The economic dispatch problem is formulated as a multi-agent Markov Decision Process. The difficulties lie in the curse of dimensionality and in guaranteeing the satisfaction of constraints under uncertainty. A novel method, that combines duality theory and deep learning, is proposed to tackle these challenges. In particular, a Neural Network (NN) is trained to return the optimal dual variables of the economic dispatch problem. By training the NN on the dual problem instead of the primal, the number of output neurons is dramatically reduced, which enhances the performance and reliability of the NN. Finally, by treating the resulting dual variables as prices, each distributed agent can self-schedule, which guarantees the satisfaction of its constraints. As a result, our simulations show that the proposed scheme performs reliably and efficiently.