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Scenario generation is an effective data-driven method for solvingchance-constrained optimization while ensuring desired risk guarantees with afinite number of samples. Crucial challenges in deploying this technique in thereal world arise due to the absence of appropriate risk-tuning models tailoredfor the desired application. In this paper, we focus on designing efficientscenario generation schemes for economic dispatch in power systems. We proposea novel scenario generation method based on filtering scenarios using ambientwind conditions. These filtered scenarios are deployed incrementally in orderto meet desired risk levels while using minimum resources. In order to studythe performance of the proposed scheme, we illustrate the procedure on casestudies performed for both 24-bus and 118-bus systems with real-world windpower forecasting data. Numerical results suggest that the proposedfilter-and-increment scenario generation model leads to a precise and efficientsolution for the chance-constrained economic dispatch problem.

Efficient Scenario Generation for Chance-constrained Economic Dispatch Considering Ambient Wind Conditions