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A weather integrated neural symbolic dispatch framework for power system operational security
The paper proposes WINDS, a power system dispatch framework that pairs neural learning for weather-driven uncertainty with symbolic reasoning that enforces security constraints. In tests on a controlled experiment and measured wind and load data from Denmark's DK1 zone, it reports operational cost reductions of 11.4% to 26.7% versus climatology-based dispatch and over 80% fewer constraint violations than forecast-only dispatch. The results are presented as a proof of concept.
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