RT Journal Article
T1 Distributionally Robust Optimal Power Flow with Contextual Information
A1 Esteban-Pérez, Adrián
A1 Morales-González, Juan Miguel
K1 Energía eólica -- distribución
AB In this paper, we develop a distributionally robust chance-constrained formulation of the Optimal Power Flow problem (OPF) whereby the system operator can leverage contextual information. For this purpose, we exploit an ambiguity set based on probability trimmings and optimal transport through which the dispatch solution is protected against the incomplete knowledge of the relationship between the OPF uncertainties and the context that is conveyed by a sample of their joint probability distribution. We provide a tractable reformulation of the proposed distributionally robust chance-constrained OPF problem under the popular conditional-value-at-risk approximation. By way of numerical experiments run on a modified IEEE-118 bus network with wind uncertainty, we show how the power system can substantially benefit from taking into account the well-known statistical dependence between the point forecast of wind power outputs and its associated prediction error. Furthermore, the experiments conducted also reveal that the distributional robustness conferred on the OPF solution by our probability-trimmings-based approach is superior to that bestowed by alternative approaches in terms of expected cost and system reliability.
PB Elsevier B. V.
YR 2022
FD 2022-10
LK https://hdl.handle.net/10630/25267
UL https://hdl.handle.net/10630/25267
LA spa
NO Adrián Esteban-Pérez, Juan M. Morales, Distributionally Robust Optimal Power Flow with Contextual Information, European Journal of Operational Research (2022), doi: https://doi.org/10.1016/j.ejor.2022.10.024
NO European Research Council (755705); Ministerio de Ciencia e Innovación del Gobierno de España (PID2020-115460GB-I00/AEI/10.13039/501100011033); Junta de Andalucía y fondos FEDER (P20 00153); Universidad de Málaga
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026