Data-Driven Distributionally Robust Electricity-Hydrogen Trading Considering The Carbon Intensity of Hydrogen Production

Longyan Li, Chao Ning

Abstract: This paper proposes a data-driven distributionally robust local electricity-hydrogen market framework, where Hydrogen-based Micro-Grids (HMGs) supply hydrogen to heterogeneous Hydrogen Users (HUs) including hydrogen refueling stations and industrial plants. In this framework, the coordination between HMGs and HUs is cast as a multi-leader multi-follower Stackelberg game. Specifically, HMGs determine an integrated hydrogen-carbon price and carry out electricity trading through a non-cooperative game. Meanwhile, HUs act as followers adjusting hydrogen purchase strategies. Furthermore, the self-dispatching of HMGs and HUs are modeled as distributionally robust optimization problems considering source-load and hydrogen demand uncertainties, respectively. To hedge against these uncertainties, a novel Bayesian nonparametric hybrid ambiguity set is constructed based on local Wasserstein balls and moment information. Finally, a distributed algorithm is developed to solve the market clearing problem. Comparative studies validate that the proposed framework outperforms existing methods, demonstrating a total income improvement of 12.3% and a carbon emission reduction of 11.6%.