Interval optimization scheduling strategy for multi-energy complementary system considering renewable uncertainties

Abstract

The large-scale integration of wind and solar power into power systems poses significant challenges due to their inherent intermittency, variability, and uncertainty. To address this challenge, this paper proposes an interval optimization scheduling strategy for a multi-energy complementary system considering renewable uncertainties. First, considering both economic performance and environmental sustainability, a bi-objective optimal scheduling model for the hydro-wind-solar-hydrogen complementary system is proposed. Two objectives are to minimize the total system operating cost and to minimize total carbon emissions, which are traded off through a weighted sum approach. To cope with the uncertainties of renewable energy outputs, a FEDformer-based forecasting method is applied to generate reliable prediction intervals. Then, an interval optimization approach is adopted to handle these uncertainties, ensuring practical applicability for day-ahead scheduling. The proposed model is validated in a multi-energy complementary clean energy base in southwest China. The results demonstrate that (1) the interval optimization scheduling strategy outperforms deterministic methods in robustness and risk reduction, and allows flexible conservativeness control. (2) A clear trade-off between carbon emissions and operating costs is revealed, offering useful guidance for system operators and policymakers.