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Ensuring the frequency stability of electric grids with increasing renewableresources is a key problem in power system operations. In recent years, anumber of advanced controllers have been designed to optimize frequencycontrol. These controllers, however, almost always assume that the net load inthe system remains constant over a sufficiently long time. Given theintermittent and uncertain nature of renewable resources, it is becomingimportant to explicitly consider net load that is time-varying.  This paper proposes an adaptive approach to frequency control in powersystems with significant time-varying net load. We leverage the advances inshort-term load forecasting, where the net load in the system can be accuratelypredicted using weather and other features. We integrate these predictions intothe design of adaptive controllers, which can be seamlessly combined with mostexisting controllers including conventional droop control and emerging neuralnetwork-based controllers. We prove that the overall control architectureachieves frequency restoration decentralizedly. Case studies verify that theproposed method improves both transient and frequency-restoration performancescompared to existing approaches.

Leveraging Predictions in Power System Frequency Control: an Adaptive Approach