Optimal Data-driven Control of Hydrogen Energy Storage System in a Microgrid for Load Restoration

Abstract

Network reconfiguration (NR) has recently received significant attention due to its potential to improve grid resilience by realizing self-healing microgrids (MGs). This paper proposes a new strategy for the real-time frequency regulation of a reconfigurable MG, wherein the model predictive control (MPC) of hydrogen energy storage systems (HESSs) is achieved in coordination with the operations of distributed generators (DGs). This enables HESSs and DGs to compensate more quickly, and preemptively, for a forthcoming variation in load demand due to NR-aided restoration. The HESS model consists of an electrolyzer, tank, and a fuel cell, and has been developed to respond quickly follow rapid changes in system loads. A data-driven HESS model is then developed using dynamic mode decomposition with control (DMDc) to supplement the weakness of the physical model-based control. This data-driven model is implemented in a reconfigurable MG for load restoration and received an updated reference signal determined optimally by the developed model predictive controllers, integrated with feedback loops for primary and secondary frequency control. Simulation case studies are also carried out to validate that the proposed strategy is effective for improving the MG frequency regulation under various conditions of load demand.