Distributed Model Predictive Control Based Secondary Frequency Regulation for a Microgrid With Massive Distributed Resources

Abstract

Controllable distributed resources can offer great potential benefits to the power systems since they possess considerable operation flexibility. However, a high-dimensional mathematical problem is emerged when modeling the massive distributed resources (DRs) with heterogeneous parameters. In light of this, based on the aggregation and disaggregation processes of massive DRs of small capacity, a model predictive control (MPC) based strategy considering incremental operation cost of various controllable devices is proposed for the real-time secondary frequency regulation in an islanded microgrid. The proposed strategy is implemented in a distributed framework using a neurodynamic-based approach, which only requires the information exchanging among neighboring units. Simulation results illustrate that the proposed strategy can efficiently manage massive DRs to maintain the system frequency and achieve a satisfactory economic performance, which indicates its promising application value in the field of microgrid frequency regulation.