Approximating Trajectory Constraints With Machine Learning - Microgrid Islanding With Frequency Constraints

In this paper, we introduce a deep learning aided constraint encoding method to tackle the frequency-constraint microgrid scheduling problem. The nonlinear function between system operating condition and frequency nadir is approximated by using a neural network, which admits an exact mixed-integer formulation (MIP). This formulation is then integrated with the scheduling problem to encode the frequency constraint. With the stronger representation power of the neural network, the resulting commands can ensure adequate frequency response in a realistic setting in addition to islanding success. The proposed method is validated on a modified 33-node system. Successful islanding with a secure response is simulated under the scheduled commands using a detailed three-phase model in Simulink. The advantages of our model are particularly remarkable when the inertia emulation functions from wind turbine generators are considered.

Automated summary

This paper introduces a deep learning aided constraint encoding method to address the frequency-constraint microgrid scheduling problem, using a neural network to approximate the nonlinear relationship between system operating condition and frequency. By integrating this representation with the scheduling problem, the resulting commands can ensure adequate frequency response and islanding success. The method is validated on a modified 33-node system, showing particularly remarkable advantages when considering inertia emulation functions from wind turbine generators.