Reduced Order Modeling of Large Power Grid Model with POD-DEIM

This paper addresses the issue of computational complexity of a large power system network, specifically the swing dynamics problem. Swing equation is nonlinear model which required mathematical model to be solved for simulating the swing dynamics. It has been seen that numerical computation becomes intractable for such models. This issue can be solved with model order reduction. Dynamics of interest is represented by a minimum size aims to reduce the computational time and memory requirement. Proper Orthogonal Decomposition technique is most often used to reduce computational efforts. However, it does not reduce the size of the nonlinear function. The discrete empirical interpolation method was proposed for POD to overcome the large size of nonlinear function by providing its discrete computations. The POD-DEIM approach is experimented on power grid network model to show significant reduction in computational cost with high degree of accuracy.