  The integration of renewable energy sources (RES) into microgrids poses
challenges to reliable and economic operation due to the inherent uncertainty
and volatility of RES. Contrary to the previous dispatch methods that require
precise predictions of RES, this paper proposes a novel prediction-free and
data-driven coordinated dispatch framework for reliable microgrid operations.
In the offline stage, ex-post optimal dispatch sequences are generated based on
historical dispatch from "God' s-eye view". The sequences offer a global
reference and are sequentially updated based on the newly observed data.
Subsequently, we propose an adaptive virtual-queue-based online convex
optimization (OCO) method to generate the real-time control policy of
microgrid, which aim to minimize the instant operation cost while tracking the
offline reference. We provide theoretical proof that the proposed method
outperforms the existing OCO methods and admits sublinear dynamic regret bound
and sublinear hard cumulative constraint violation bound for OCO with
time-varying constraints. Case study illustrates that the proposed method
outperforms state-of-the-art methods in terms of economic optimality,
computational efficiency, and security.
