  Ensuring the frequency stability of electric grids with increasing renewable
resources is a key problem in power system operations. In recent years, a
number of advanced controllers have been designed to optimize frequency
control. These controllers, however, almost always assume that the net load in
the system remains constant over a sufficiently long time. Given the
intermittent and uncertain nature of renewable resources, it is becoming
important to explicitly consider net load that is time-varying.
  This paper proposes an adaptive approach to frequency control in power
systems with significant time-varying net load. We leverage the advances in
short-term load forecasting, where the net load in the system can be accurately
predicted using weather and other features. We integrate these predictions into
the design of adaptive controllers, which can be seamlessly combined with most
existing controllers including conventional droop control and emerging neural
network-based controllers. We prove that the overall control architecture
achieves frequency restoration decentralizedly. Case studies verify that the
proposed method improves both transient and frequency-restoration performances
compared to existing approaches.
