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The increase in distributed energy resources and flexible electricityconsumers has turned TSO-DSO coordination strategies into a challengingproblem. Existing decomposition/decentralized methods apply divide-and-conquerstrategies to trim down the computational burden of this complex problem, butrely on access to proprietary information or fail-safe real-time communicationinfrastructures. To overcome these drawbacks, we propose in this paper aTSO-DSO coordination strategy that only needs a series of observations of thenodal price and the power intake at the substations connecting the transmissionand distribution networks. Using this information, we learn the price responseof active distribution networks (DN) using a decreasing step-wise function thatcan also adapt to some contextual information. The learning task can be carriedout in a computationally efficient manner and the curve it produces can beinterpreted as a market bid, thus averting the need to revise the currentoperational procedures for the transmission network. Inaccuracies derived fromthe learning task may lead to suboptimal decisions. However, results from arealistic case study show that the proposed methodology yields operatingdecisions very close to those obtained by a fully centralized coordination oftransmission and distribution.

Learning the price response of active distribution networks for TSO-DSO coordination