TY - JOUR
T1 - Learning the Price Response of Active Distribution Networks for TSO-DSO Coordination
AU - Mmorales, J. M.
AU - Pineda, S.
AU - Dvorkin, Y.
N1 - Funding Information:
This work was supported in part by the European Research Council (ERC) under the EU Horizon 2020 research and innovation program Grant Agreement 755705
Publisher Copyright:
© 1969-2012 IEEE.
PY - 2022/7/1
Y1 - 2022/7/1
N2 - 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.
AB - 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.
KW - Ders market integration
KW - Distribution network
KW - Price-responsive consumers
KW - Statistical learning
KW - Tso-dsos coordination
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U2 - 10.1109/TPWRS.2021.3127343
DO - 10.1109/TPWRS.2021.3127343
M3 - Article
AN - SCOPUS:85133159886
VL - 37
SP - 2858
EP - 2868
JO - IEEE Transactions on Power Systems
JF - IEEE Transactions on Power Systems
SN - 0885-8950
IS - 4
ER -