多相关性联合驱动的高维水-风-光场景生成方法

Abstract With the high proportion of clean energy connected to the grid, accurately characterizing its uncertainty emerges as a pivotal challenge for the planning and optimizing Hydro-Wind-Photovoltaic (HWP) multi-energy complementary systems. To address the complex modeling requirements of HWP resources in terms of high-dimensional variables and spatiotemporal stochastic dependencies, this study proposes a novel high-dimensional scenario generation method, jointly driven by multiple correlations. Firstly, the temporal autocorrelation models based on Gaussian mixture model (GMM) were constructed alongside the spatial cross-correlation model utilizing Copula functions, with synergistic modeling of multiple correlations being achieved through cumulative distribution functions. Second, the accuracy and reliability of the constructed models were validated through evaluation of root mean square errors between empirical data distributions and theoretical model distributions, supplemented by Kolmogorov-Smirnov goodness-of-fit tests. Then, based on established multiple correlations modeling framework and integrated with inverse transform sampling, daily-scale scenario sets were generated for streamflow, wind power output, and photovoltaic (PV) output. Finally, the performance of the proposed method was comprehensively evaluated through multiple metrics from diverse perspectives. The novelty of this work lies in: (1) The synergistic GMM-Copula modeling mechanism enables decoupled modeling of multiple correlations characteristics among HWP resources, with time-varying parameters reflecting dynamic evolution of correlations; (2) The application of the conditional distribution strategy reduced modeling complexity and improved computational feasibility, effectively addressing the challenges of modeling high-dimensional temporal variables and high-dimensional resource variables. Applied to the Xiluodu Hydropower Station and its associated wind-PV resources, the proposed method demonstrates superior performance in preserving statistical properties, describing multiple correlations, and characterizing uncertainties, thereby enhancing the accuracy and practicality of scenario generation. This advancement provides robust data support for optimizing the dispatch of HWP multi-energy complementary systems.