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The massive diffusion of renewable power generators in existing power grids introduces large uncertainties in power system operation, hindering their hosting capacity, and introducing several critical issues in network management. To address these challenging issues, weather-based optimal power flow has been recognized as one of the most promising enabling methodology for increasing the system flexibility by exploiting the real power components loadability. Anyway, the deployment of this technique in a real operation scenario could be seriously compromised due to the effects of data uncertainty, which could sensibly affect both the generated/demanded power profiles, and the components thermal modeling. In this context, the research for reliable techniques aimed at representing and managing these uncertainties represents one of the most relevant problem to solve. Armed with such a vision, this paper advocates the role of Affine Arithmetic in reliable solving weather-based OPF problems in the presence of multiple and correlated uncertainties. Experimental results obtained on a real case study, which is based on a congested portion of a transmission system characterized by a massive pervasion of wind generators, will be presented and discussed in order to assess the benefits deriving by the application of the proposed method.

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