tested for provable performance. Next, flywheels are placed at each bus with wind generators, which are

the potential disturbance locations. Then, a variable speed drive controller for flywheels is designed

using time-scale separation and nonlinear passivity-based control logic. Switches in the power electronics

interfacing between the flywheel and the rest of the power grid are controlled in order to regulate both the

flywheel speed and the power electronic currents. The controller set points are chosen so that the

flywheel absorbs the wind power disturbance and the rest of the system is minimally affected. Finally,

the power electronics are sized to ensure that the flywheel can handle a certain range of disturbances.

Due to the complex nature of large interconnected power systems, automated methods are

implemented for both the modeling and control of power systems. An automated approach is presented

for symbolically deriving the dynamic model of power systems using the Lagrangian formulation from

classical mechanics, where the model is described in terms of the energy functions of the system.

Another automated method is introduced for symbolically deriving the control law using passivity-based

control, where the control law is derived from desired closed-loop energy functions.

Finally, in the actual implementation of flywheels, one major design challenge is to support the high

speed rotor. A passive magnetic bearing design is presented and the resultant magnetic fields and forces

are computed, demonstrating that stable levitation of the flywheel in all directions is achieved.