Solar generation facilities are inherently spatially distributed and therefore aggregate solar irradiance in both space and time, smoothing its variability. To represent the spatiotemporal aggregation process, most existing studies focus on the reduced correlation in solar irradiance throughout a plant's spatial distribution. In this paper, we derived a cloud advection model that is instead based upon lagging correlations between upwind/downwind portions of a distributed plant, induced by advection of a fixed cloud pattern over the plant. We use the model to calculate a plant transfer function that can be used to predict the smoothing of the time series. The model was validated using the distributed HOPE-Melpitz measurement dataset, which consisted of 50 solar irradiance sensors at 1 s temporal resolution over a 3 × 2 km2 bounding area. The initial validation showed that the advection-based model outperforms other models at predicting the smoothed irradiance time series during manually identified, advection dominated conditions. We also conducted validation on the model against additional advection dominated periods in the dataset that were identified algorithmically. The cloud advection model's performance compared well to models in literature, but degraded slightly as larger cross-wind plant distributions were investigated. The results in this paper highlight the need to incorporate advection effects on spatial aggregation during advection dominated conditions. Future development of spatiotemporal aggregation models is needed to unify advective models with existing correlation reduction models and to identify regimes where each dominate.
All Science Journal Classification (ASJC) codes
- Renewable Energy, Sustainability and the Environment