The rapid growth of renewable energy penetration has intensified the need for reliable probabilistic forecasts to support grid operations at aggregated (fleet or system) levels. In practice, however, system operators often lack access to fleet-level probabilistic models and instead rely on site-level forecasts produced by heterogeneous third-party providers. Constructing coherent and calibrated fleet-level probabilistic forecasts from such inputs remains challenging due to complex cross-site dependencies and aggregation-induced miscalibration. This paper proposes a calibrated probabilistic aggregation framework that directly converts site-level probabilistic forecasts into reliable fleet-level forecasts in settings where system-level models cannot be trained or maintained. The framework integrates copula-based dependence modeling to capture cross-site correlations with Context-Aware Conformal Prediction (CACP) to correct miscalibration at the aggregated level. This combination enables dependence-aware aggregation while providing valid coverage and maintaining sharp prediction intervals. Experiments on large-scale solar generation datasets from MISO, ERCOT, and SPP demonstrate that the proposed Copula+CACP approach consistently achieves near-nominal coverage with significantly sharper intervals than uncalibrated aggregation baselines.

翻译：可再生能源渗透率的快速增长，加剧了对可靠概率预测的需求，以支持聚合（场站或系统）层面的电网运行。然而，在实践中，系统运营商通常无法获取场站层面的概率模型，而是依赖于由异构第三方供应商提供的站点级预测。由于复杂的跨站点依赖性和聚合导致的校准偏差，从此类输入构建一致且校准良好的场站层面概率预测仍然具有挑战性。本文提出了一种校准的概率聚合框架，在无法训练或维护系统级模型的情况下，直接将站点级概率预测转换为可靠的场站级预测。该框架集成了基于Copula的依赖关系建模以捕捉跨站点相关性，并结合上下文感知的共形预测来校正聚合层面的校准偏差。这种组合实现了依赖感知的聚合，同时提供了有效的覆盖范围并保持了锐利的预测区间。在来自MISO、ERCOT和SPP的大规模太阳能发电数据集上的实验表明，所提出的Copula+CACP方法始终能实现接近名义水平的覆盖范围，且其预测区间比未经校准的聚合基线方法显著更为锐利。