  This paper studies the long-term energy management of a microgrid
coordinating hybrid hydrogen-battery energy storage. We develop an approximate
semi-empirical hydrogen storage model to accurately capture the power-dependent
efficiency of hydrogen storage. We introduce a prediction-free two-stage
coordinated optimization framework, which generates the annual state-of-charge
(SoC) reference for hydrogen storage offline. During online operation, it
updates the SoC reference online using kernel regression and makes operation
decisions based on the proposed adaptive virtual-queue-based online convex
optimization (OCO) algorithm. We innovatively incorporate penalty terms for
long-term pattern tracking and expert-tracking for step size updates. We
provide theoretical proof to show that the proposed OCO algorithm achieves a
sublinear bound of dynamic regret without using prediction information.
Numerical studies based on the Elia and North China datasets show that the
proposed framework significantly outperforms the existing online optimization
approaches by reducing the operational costs and loss of load by around 30% and
80%, respectively. These benefits can be further enhanced with optimized
settings for the penalty coefficient and step size of OCO, as well as more
historical references.
