A Wasserstein metric distributionally robust chance-constrained peer aggregation energy sharing mechanism for hydrogen-based microgrids considering low-carbon drivers

Abstract

Hydrogen-based multi-energy microgrids (H-MEMGs), serving as integrated energy systems incorporating hydrogen, electricity, thermal, and cooling energy, have emerged as pivotal infrastructures for accelerating energy transition and achieving carbon neutrality. This study proposes a novel low-carbon driven energy sharing framework with adaptive pricing mechanisms to address the critical need for balancing economic viability and environmental sustainability. The proposed framework establishes three key contributions: 1) A non-cooperative game-theoretic foundation enabling peer-to-peer energy transactions with carbon-embedded pricing signals, 2) A privacy-preserving coordination mechanism through a virtual energy sharing center that facilitates iterative information exchange while protecting sensitive operational data, and 3) A data-driven distributionally robust optimization model employing Wasserstein metrics and conditional value-at-risk techniques to manage renewable energy uncertainties. To ensure computational efficiency and data security, we develop a distributed algorithm based on Brouwer's fixed-point theorem that enables decentralized decision-making without compromising individual microgrid's privacy. Simulation results highlight three key advantages: a 5.04 % reduction in carbon intensity relative to conventional pricing schemes, and 6.43 % cost savings via dynamic price-responsive coordination, the data-driven distributionally robust chance-constrained (DRCC) method exhibits outstanding out-of-sample performance and strong adaptability to uncertainty. The proposed methodology provides a scalable solution for coordinating interconnected microgrids in low-carbon energy ecosystems.