  Wind power uncertainty poses significant challenges for automatic generation
control (AGC) systems. It can enhance control performances to explicitly
consider wind power uncertainty distributions within controller design.
However, widely accepted wind uncertainties usually follow non-Gaussian
distributions which may lead to complicated stochastic AGC modeling and high
computational burdens. To overcome the issue, this paper presents a novel
Ito-theory-based model for the stochastic control problem (SCP) of AGC systems,
which reduces the computational burden of optimization considering non-Gaussian
wind power uncertainty to the same scale as that for deterministic control
problems. We present an Ito process model to exactly describe non-Gaussian wind
power uncertainty, and then propose an SCP based on the concept of stochastic
assessment functions (SAFs). Based on a convergent series expansion of the SAF,
the SCP is reformulated as a certain deterministic control problem without
sacrificing performance under non-Gaussian wind power uncertainty. The
reformulated control problem is proven as a convex optimization which can be
solved efficiently. A case study demonstrates the efficiency and accuracy of
the proposed approach compared with several conventional approaches.
