Distributionally Robust Scheduling Model of Integrated Electricity and Hydrogen Energy Networks With Peer-to-Peer Trading

The development of hydrogen systems and their coupling with electric power distribution systems have become crucial for supporting the transition toward sustainable energy development. However, the deep coupling between these systems enabled by integrated energy stations (IESs) and peer-to-peer (P2P) trading mechanisms is insufficiently explored. The present work addresses this issue by proposing a distributionally robust bi-level optimized scheduling model for integrated electricity and hydrogen systems, incorporating P2P trading. The upper-level model determines optimal trading P2P strategies for electricity, hydrogen, and the reserve capacity of IESs, while the lower-level model seeks to optimize the operating costs of the hydrogen and power distribution networks and adjusts their trading prices dynamically based on locational marginal prices. Photovoltaic output uncertainty is addressed via distributionally robust optimization, that protects against the worst-case empirical distribution. Efficient computation is achieved by combining column-and-constraint generation and the alternating direction method of multipliers. The results of case studies based on the IEEE 33-node electric distribution network and a 20-node hydrogen network demonstrate that P2P trading facilitated between IESs by the bi-level optimization model reduces IES costs. Furthermore, the reserve capacity provided by IESs improves the stability of the coupled system. Accordingly, the proposed model provides support for the collaborative optimization of multiple energy sources and market-based trading.