Distributionally robust scheduling of electric‑hydrogen integrated energy systems based on pipeline-road coordinated hydrogen transportation

Electrolytic water hydrogen production is an effective method for achieving the absorption of excess renewable energy and peak shaving and valley filling in the power system. However, when facing large-scale and long-distance hydrogen transportation needs, existing hydrogen transportation strategies struggle to transport hydrogen economically and flexibly from hydrogen production plants (HPPs) to various hydrogen users. To this end, this paper proposes a distributionally robust optimization (DRO) scheduling model for the electric‑hydrogen integrated energy system (EHIES) based pipeline-road collaborative hydrogen transportation (PRCHT). Firstly, by analyzing the transportation mechanism of hydrogen-blended pipelines and combining the relationship between the pipeline's storage and the gas pressure at both ends, this work constructs a quasi-dynamic transportation model for natural gas hydrogen blending with a variable hydrogen blending ratio. Next, by employing the improved McCormick technique and piecewise linearization method, the quasi-dynamic model is transformed into a mixed-integer linear programming (MILP) model. Furthermore, by integrating the trailer-based hydrogen transportation model, a PRCHT model is developed. Finally, considering the high uncertainty in wind power output, a DRO scheduling model for the integrated electricity‑hydrogen energy system based on Wasserstein distance is proposed. The DRO model is then transformed into a MILP problem using the conditional value-at-risk (CVaR) approximation method. The simulation results demonstrate that the proposed scheduling model reduces the total system cost by 19.43 % compared to the constant hydrogen blending ratio benchmark, while preventing 22.68 % of potential hydrogen load shedding relative to the natural-gas-pipeline-exclusive transport model. Meanwhile, the employed algorithm improves computational efficiency and achieves a robust optimization of the scheduling decisions by balancing system robustness and economic performance.