A Stochastic Linearization Approach to Optimal Primary Control of Power Systems with Generator Saturation

Abstract

Quasilinear Control (QLC) is a set of methods used for analysis and design of systems with nonlinear actuators and sensors. It is based on the method of stochastic linearization, which replaces a nonlinearity by an equivalent gain and bias. Here, we leverage QLC to systematically design an optimal droop controller for primary frequency control of power systems with asymmetric generator saturation and renewable penetration. The droop parameters are found by solving an optimization problem wherein the cost function is a combination of the change in frequency and the actuator input. Simulation studies show that the combined output and control cost is improved compared to a baseline design, and that the systematic design process provides an appropriate response to any change in input or system parameters.