Mitigating uncertainties: robust stochastic optimal operation of inter-regional hydro-wind-solar hybrid systems

The growing deployment of large-scale hydro-wind-solar hybrid systems at the basin scale calls for effective inter-regional coordination strategies. However, managing such systems under spatiotemporal renewable uncertainties poses significant challenges. This study proposes a robust stochastic optimization framework for inter-regional hydro-wind-solar hybrid system operations, integrating a multi-objective formulation, Markov-chain-based uncertainty representation, and stochastic dynamic programming with successive approximation to capture global uncertainties while reducing spatial complexity. Four operational schemes are designed for cross-comparison, including regional versus inter-regional and deterministic versus stochastic configurations. The framework is applied to the clean energy base of the upper Yellow River in China. The results indicate that inter-regional coordination consistently outperforms regional strategies, yielding additional power generation of 0.27 and 0.17 billion kWh under deterministic and stochastic conditions, respectively. Moreover, inter-regional operation exhibits enhanced robustness under stochastic optimization, as reflected by reduced variability in annual average generation, lower standard deviations of final reservoir water levels, and improved robustness indicators. These improvements arise from the mitigation of global uncertainties through coordinated operation, which further enhances downstream operational benefits by 2.0 %. Overall, the proposed framework provides a robust decision-making tool for optimizing inter-regional hydro-wind-solar hybrid systems under uncertainties.