Online Data-Stream-Driven Distributionally Robust Optimal Energy Management for Hydrogen-Based Multimicrogrids

Longyan Li, Chao Ning, Haifeng Qiu, Wenli Du, Zhaoyang Dong

Abstract: The hydrogen-based multimicrogrid (HMMG) has emerged as a game changer for energy transition. However, it encounters new challenges in tackling uncertainty data streams stemming from intermittent renewable energy and load. This article presents a multiple-time-scale HMMG energy management framework. In the day-ahead stage, the optimal scheduling is determined. The deviations of day-ahead predictions are redressed by intraday rescheduling. To accommodate the uncertainty data streams, a novel data-stream-driven distributionally robust model predictive control is proposed for the HMMG real-time operation. Specifically, a Dirichlet process mixture model is leveraged to construct an online-updated ambiguity set, which adequately characterizes the multimodality and local moment information of uncertainties. Based upon this ambiguity set, the data-stream-driven distributionally robust model predictive control enhances the real-time tracking performance of energy storage references. Its salient feature is the capability of greatly reducing conservatism while ensuring probabilistic operational constraints even under time-varying uncertainty distributions. Since this real-time operation is an intractable infinite-dimensional optimization problem, a novel constraint-tightening technique is proposed to address the computational challenge. Case studies demonstrate that the proposed approach offers advantages over state-of-the-art methods in out-of-sample performance.