  In this paper, we introduce a method to optimally estimate time-varying
frequency bias. Current industry practice is to assume that frequency bias is
changing only on annual basis. We suggest that this improved time-dependent
bias estimate can be used to reduce the cost of frequency regulation needed to
meet industry standards requested by the North American Electric Reliability
Corporation (NERC). Optimization of time-varying frequency bias is posed as a
parameter estimation (calibration) problem whose implementation utilizes online
system measurements. It is further shown how this result can be used to
estimate intra-dispatch load deviations. This knowledge is needed to estimate
more accurately regulation reserve needed, and to therefore reduce overall
regulation cost. Methods can be introduced to give incentives to demand
response to participate in frequency regulation. Overall, we show the
importance of incorporating knowledge of physics-based models for data-enabled
parameter estimation of physical systems.
