Decentralized distributionally robust chance-constrained operation of integrated electricity and hydrogen transportation networks

Hydrogen energy offers a promising pathway to decarbonizing future energy systems. Renewable energy-based hydrogen production and road transportation-based hydrogen delivery have strengthened the coupling between the power distribution network (PDN) and the hydrogen transportation network (HTN). In this article, we propose a decentralized distributionally robust chance-constrained (DRCC) method for the integrated electricity and hydrogen transportation network (IEHTN) to foster synergies between PDN and HTN. First, an optimal scheduling framework is developed to coordinate hydrogen production and transportation in the IEHTN, where multiple operational states of water electrolyzers and the time-constrained tube trailer routing are modeled. Second, a data-driven DRCC approach is proposed to model the uncertain behaviors of renewable power generation and hydrogen demand. A conditional value-at-risk approximation is then employed to convert the DRCC problem to a tractable mixed-integer linear program that can be solved by the off-the-shelf solver. Third, an alternating direction method of multipliers (ADMM) based solving scheme is designed to solve the problem in a distributed manner with limited information exchange between PDN and HTN. To address the non-convexity brought by binary variables in the ADMM, a tractable alternating optimization procedure (AOP) strategy is utilized to guarantee the convergence of solution. Finally, case studies are conducted on an IEHTN composed of the modified IEEE 33-bus PDN and 52-node HTN to verify the validity of the proposed method. •A decentralized framework coordinates the power and hydrogen transportation networks.•Data-driven distributionally robust chance-constrained model trade-offs risk and cost.•ADMM with alternating optimization procedure solves the MILP with a guarantee of convergence.