Medium- and long-term contract energy decision-making for hydro–wind–solar complementary systems under multivariate uncertainty

The hydro-wind-solar (HWS) complementary systems face a complex energy management problem that requires the coordination of renewable energy utilization, contracted electricity decisions, spot market flexibility, and deviation risk under multiple uncertainties when participating in electricity markets. Therefore, this paper develops a two-stage distributionally robust decision-making framework for annual contract electricity allocation in HWS system. First, a correlation model is established to capture the uncertainty of streamflow, wind, and solar power, and spot price scenarios are generated based on historical data. Second, a stage-wise adaptive weighting scenario reduction method (SAWM-SR) is proposed to preserve the temporal and seasonal characteristics of original scenarios. Finally, a two-stage distributionally robust optimization model is developed and compared with stochastic optimization, robust optimization, and deterministic strategies. Case study of the HWS system in Yalong River basin in Southwest China shows that: The proposed SAWM-SR method outperforms the traditional FBSR method in preserving distribution characteristics, fluctuation levels, and variable correlations of the original scenarios. Besides, market settlement revenue is not monotonically related to annual contracted volume, but depends on the matching among monthly contracts, generation capability, and spot–contract price spreads. And the DRO strategy reduces reliance on empirical distributions, worst-case scenarios, and price distribution, while maintaining high average and tail revenues and controlling recovery risks. This study can provide support for renewable energy management and market participation of HWS systems in medium- and long-term electricity markets.

Duke Scholars

Author Dalia Patino-Echeverri Environmental Social Systems