To address the variability in the scheduling timescale of electric and hydrogen energy and the high uncertainties caused by the high proportion of renewable energy, this paper proposes a two-layermulti-timescale roll...
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To address the variability in the scheduling timescale of electric and hydrogen energy and the high uncertainties caused by the high proportion of renewable energy, this paper proposes a two-layer multi-timescale rolling optimization method for electric-hydrogen hybrid energy storage systems (EH-HESS) considering renewable energy uncertainties. In the upper-layer long-timescaleoptimization, a fuzzy set of wind turbine (WT) and photovoltaic (PV) generation forecast errors is established based on the Wasserstein distance, and a distribution robust optimization (DRO) model is proposed for energy scheduling. In the lower-layer short-timescaleoptimization, typical scenarios are generated for the potential distribution of WT and PV generation. After receiving the upper-layer scheduling results, a short-timescale power scheduling model is established. The lower-layer scheduling results are passed back to the upper-layer to achieve a two-layer multi-timescale rolling optimization. Simulations show that the proposed method can achieve inter-day transfer of hydrogen energy, reduce the daily imbalanced electric energy and effectively suppress the fluctuation of WT and PV generation. Moreover, the method demonstrates notable economic benefits, higher by 21.71% and 18.87% respectively, than singletimescale energy storage and robust optimization (RO), enabling a balance between economy and robustness. Finally, a detailed sensitivity analysis is conducted to show the effect of the fuzzy set radius on the scheduling results.
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