PNNL

Abstract

The varying topological configurations, generator commitments and dispatches, and dynamic load demand lead to changing system strengths during the operations of networked microgrids. When the system strengths significantly change, the fixed control gains at large devices may result in unsatisfactory system performance; this necessitates the tuning of the control gains at large devices to adapt to the changing system strengths. In this paper, observer-based reinforcement learning (RL) is utilized to automatically tune the proportional-integral (PI) gains of phase lock loop (PLL) controller of grid-following (GFL) inverters to adapt to the changing strengths of microgrids and networked microgrids. The RL agent in this framework augments an observer predicting the static strength and the upper-bound of the dynamic strength, from which the RL control policy will adjust accordingly to tune the PLL controller’s gains toward the changing static and dynamic system strengths. Also, to enhance the control performance, the recently introduced Barrier function-based RL framework is leveraged for the design of reward function to prevent the high frequency nadir. An operational 26kV electric distribution system, which is modeled as networked microgrids, is used to illustrate the need and effectiveness of the proposed RL-tuned controls.