  The increase in distributed energy resources and flexible electricity
consumers has turned TSO-DSO coordination strategies into a challenging
problem. Existing decomposition/decentralized methods apply divide-and-conquer
strategies to trim down the computational burden of this complex problem, but
rely on access to proprietary information or fail-safe real-time communication
infrastructures. To overcome these drawbacks, we propose in this paper a
TSO-DSO coordination strategy that only needs a series of observations of the
nodal price and the power intake at the substations connecting the transmission
and distribution networks. Using this information, we learn the price response
of active distribution networks (DN) using a decreasing step-wise function that
can also adapt to some contextual information. The learning task can be carried
out in a computationally efficient manner and the curve it produces can be
interpreted as a market bid, thus averting the need to revise the current
operational procedures for the transmission network. Inaccuracies derived from
the learning task may lead to suboptimal decisions. However, results from a
realistic case study show that the proposed methodology yields operating
decisions very close to those obtained by a fully centralized coordination of
transmission and distribution.
