A new genetic algorithm to optimize synchronous machine controls in statically reduced power system models

The development of new power grid components and solutions requires dynamic grid simulations, including vast transmission grids. These simulations are often prohibitively computationally expensive. Static model reduction is a common solution to reduce the complexity of grid models. However, these reduction techniques do not consider the control structure of synchronous machines. As a result, the models lose the ability to be simulated dynamically. To address this issue, we introduce a new genetic algorithm that optimizes these controls in previously reduced models. This enables dynamic simulations for models previously reduced with arbitrary static reduction methods. The new method optimizes the selection and parameters of synchronous machine controls to approximate the original system behavior. We evaluate the new method using a standard IEEE benchmark model and demonstrate its applicability with a real-world transmission grid model with more than 300 nodes.