Unraveling wind and solar energy uncertainties: Modeling, quantification and optimization for integrated energy systems

With the penetration of wind and solar energy into integrated energy systems (IESs), uncertainties arising from the intermittency and variability of wind and solar energy have emerged as critical barriers to system stability and operational efficiency. This review examines the methodologies for modeling, analyzing, and optimizing wind and solar energy uncertainties in IESs, discussing around marginal probability distribution modeling, joint probability analysis, discrete scenario generation, uncertainty quantification, and optimization frameworks. First, marginal modeling classifies parametric and non-parametric, emphasizing their roles in capturing univariate stochastic characteristics of wind and solar. Then, Joint distribution modeling employs copula theory and machine-learning frameworks to characterize spatio-temporal dependencies. Thirdly, discrete scenario generation methods are analyzed, encompassing sampling strategies and scenario reduction algorithms that balance computational tractability and the representativeness of extreme output events. Fourth, uncertainty quantification is covered in three framework-aligned dimensions: statistical characteristic methods, probability distribution indicators, and scenario analysis approaches. Then, the review demonstrates uncertainty-aware optimization in IESs applications, including scheduling and configuration, highlighting their impact on enhancing system resilience and economic efficiency. Finally, the review summarizes current challenges—including modeling spatiotemporal dependencies, non-standardized uncertainty indices, and optimization trade-offs in IESs—and proposes recommendations such as advanced hybrid frameworks, standardized evaluation, and adaptive optimization methods.