Hydrogen-enabled multi-energy microgrids: Coordinated planning and operation under renewable uncertainty

Hydrogen-enabled multi-energy microgrids are emerging as a key enabler of reliable and low-carbon energy systems, yet their coordinated planning and operation remain fundamentally challenged by deep renewable uncertainty across multiple time scales. This paper proposes a novel multi-level hierarchical optimization framework for networked microgrids where a deep learning-based probabilistic forecasting engine is the cornerstone for managing multi-timescale uncertainties. At its core, a Sequence-to-Sequence (Seq2Seq) Recurrent Neural Network (RNN) model with a Bahdanau attention mechanism generates high-fidelity probabilistic forecasts for renewable generation and load. These forecasts are not merely point predictions; they directly parameterize the entire optimization hierarchy. The median forecast drives nominal scheduling, while the upper and lower quantiles (0.1 and 0.9) define time-varying, data-driven uncertainty sets for a two-stage robust optimization model solved via Column-and-Constraint Generation (C&CG). This tight integration of forecasting and optimization ensures decisions are robust against worst-case realizations. The framework seamlessly coordinates long-term strategic investment with real-time model predictive control, integrating hybrid hydrogen-battery storage, electric vehicle fleets, and Power-to-X (P2X) facilities. Simulation results on a modified IEEE 33-bus system demonstrate that the RNN-driven framework reduces net present cost by 15% and loss-of-load probability by 74% compared to a traditional base case. The forecasting model itself achieves a Mean Absolute Percentage Error (MAPE) of 3.2% for load and 8.5% for solar generation, proving that accurate probabilistic forecasting is critical for unlocking significant economic and reliability improvements in modern multi-energy systems.