Distributionally robust low-carbon economic dispatch for electric-hydrogen coupled system under high-penetration renewables

This paper proposes a two-stage distributionally robust low-carbon economic dispatch model for an electric-hydrogen coupled system with integrated hydrogen production, storage, and utilization technologies under renewable energy generation uncertainties. In the first-stage, day-ahead scheduling decisions for thermal units, battery energy storage systems, and hydrogen energy storage systems are optimized based on wind farm and photovoltaic power forecasts. Motivated by the availability of abundant historical renewable energy data, a data-driven ambiguity set based on the Wasserstein metric is constructed to quantitatively characterize renewable power forecast errors. In the second-stage, an affine policy is employed to establish coupling relationships among day-ahead decisions, uncertain parameters, and real-time recourse variables, enabling real-time adjustments of the day-ahead schedules. The objective function aims to minimize the operating cost, carbon emission cost, and renewable energy curtailment penalty of the electric-hydrogen coupled system under uncertainties. By leveraging strong duality theory, the proposed two-stage distributionally robust optimization (T-DRO) model is equivalently reformulated into a tractable mixed-integer linear programming (MILP) problem. Numerical experiments on the modified IEEE 14-bus system and IEEE 118-bus system demonstrate the effectiveness and operational flexibility of the proposed model, and further show that the developed T-DRO approach outperforms conventional robust and stochastic optimization methods in terms of robustness.