Distributionally Robust Ideal Power Circulation with Contextual Details

The researchers Adrián Esteban-Pérez and Juan M. Morales develop a distributionally durable chance-constrained solution of the Optimal Power Flow problem (OPF), where the system operator can leverage contextual data. For this purpose, they utilized an ambiguity collection based on probability trimmings and optimum transportation. This way, the dispatch service is safeguarded against the incomplete understanding of the relationship between the OPF issue and the context conveyed by a sample of their joint probability distribution.

They offer an exact reformulation of the proposed distributionally robust chance-constrained OPF problem under the common conditional-value-at-risk estimate. Using numerical experiments run on a customized IEEE-118 bus network with wind unpredictability, they demonstrate how the power system can considerably benefit from considering the widely known statistical dependency between the point forecast of wind power results and its linked prediction error.

Moreover, the experiments conducted also expose that the distributional robustness given on the OPF solution by our probability-trimmings-based strategy is superior to that bestowed by different approaches regarding expected cost and system reliability.

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