  This paper proposes a learning-based approach for the coordination of
transmission and distribution operations. Given a series of observations of the
nodal price and the power intake at the main substation of a distribution grid,
we construct the nonincreasing piecewise constant function that best explains
the response of the grid to the electricity price. In order to captures changes
in this response, we make the inference process conditional on some easily
accessible contextual information. The learning task can be carried out in a
computationally efficient manner and the curve it produces can be naturally
interpreted as a market bid whereby the distributed energy resources in the
grid can directly participate in the wholesale electricity markets, thus
averting the need to revise the current operational procedures for the
transmission network. We consider a realistic case study to compare our
approach with alternative ones, including a fully centralized coordination of
transmission and distribution, for different levels of grid congestion at
distribution.
