Managing Distributed Flexibility under Uncertainty by Combining Deep
Learning with Duality
Abstract
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.