Grid operators in areas with lots of solar generation need solar power forecasts to support decision making. Most forecasts that are available are deterministic, and most of the ones that are probabilistic are for single plants, meaning don't have combined probabilities for aggregations of all the plants in a region. Additionally, few methods for producing probabilistic forecasts are easy to replicate or deploy for grid operators. This work presents a method for producing regional probabilistic forecasts from an existing deterministic forecast using open source tools and freely available data. The method is demonstrated using a deterministic forecast that is produced with similar tools and data. Reference code to replicate this work, which is freely available, is also introduced.

Subhourly changes in solar irradiance can lead to energy models being biased high if realistic distributions of irradiance values are not reflected in the resource data and model. This is particularly true in solar facility designs with high inverter loading ratios (ILRs). When resource data with sufficient temporal and spatial resolution is not available for a site, synthetic variability can be added to the data that is available in an attempt to address this issue. In this work, we demonstrate the use of anonymized commercial resource datasets with synthetic variability and compare results with previous estimates of model bias due to inverter clipping and increasing ILR.

Solar forecast error can be represented in electric utility production cost modeling to assess impacts of uncertainty in different scenarios. We use reforecasts (a.k.a., historical forecasts, hindcasts) that were developed to be concurrent with synthetic solar generation profiles (matching each hour in the same year), along with asynchronous forecasts (i.e., with errors remapped from different hours and/or years), to explore impacts on model results. We demonstrate that production cost increases non-linearly with forecast error, and that our method for remapping asynchronous forecast errors to a solar generation profile produces results that are similar to the concurrent forecasts, but with about 20\% lower cost impacts due to forecast error.

We explore the use of distributed PV power measurements for real-time short-term forecasting of the maximum potential power output of a utility-scale PV power plant, to support future incorporation of PV plants in automatic generation control (AGC) systems. PV plants operating under AGC may run in a curtailed state but must be capable of accurately forecasting their maximum potential non-curtailed power output, or potential high limit (PHL), 10-20 seconds in advance – despite variable environmental conditions. One forecast approach estimates PHL from a subset of inverters, designated reference inverters, which are never curtailed. We propose an alternative approach using in-situ I V tracers to measure maximum power point of reference modules distributed throughout a PV plant. This would allow for greater sampling of variations in irradiance, module temperature, soiling, and albedo, permitting better PHL prediction accuracy. To study potential benefits of this method, we analyzed a one-year data set from an approximately 100 MW PV power plant in the US Southeast, using non-curtailed string combiner outputs as a proxy for distributed module-level power measurements. We compare the reference inverter and distributed power measurement approaches. Results indicate that for highly flexible operation, substantial improvement in prediction accuracy can be achieved using distributed power measurements compared to using a low fraction of reference inverters. We review the study methodology, conclusions, and shortcomings and discuss prospective future work.

Flexible solar operation, where solar photovoltaic (PV) plants follow up- and down-regulation signals, has significant potential to improve integration of solar into power grids. To optimize operation, it is important to accurately estimate the potential maximum power output, or potential high limit (PHL), of a plant in real time during periods where output has been reduced. As the PHL cannot be directly measured while a plant is curtailed, and it is driven by highly variable weather and plant conditions, model-based estimation methods are subject to errors. An estimation method using a subset of a plant as a reference has been developed by NREL. Here, we evaluate a version of that method using data from several utility-scale plants in the Southeast US spanning up to a full year.

As renewable energy penetration on the grid increases, requirements are being placed on PV owners and operators to limit power ramp rates. PV power ramping is an issue for grid stability since generation-load balance must be continually met, and when a large PV resource significantly increases or decreases, another resource must compensate to ensure matching. Traditional dispatchable resources have a limited ability to respond quickly. Therefore, to aid in grid stability, ramp rate limitations have been imposed on PV plants. This work addresses the question of how much fast-responding storage is needed to mitigate high ramp rates of PV plants, and how much benefit is there from short-term power forecasting in terms of reducing the storage requirement. The results provide a baseline estimate for system planners and designers. Furthermore, the storage controller design and optimization are given, along with the open-source code, such that others can tailor the simulation to their specific plant and weather profile. results from studying a 100 MW PV plant power production profile show a reduction in ramp-rate violations from 10% of yearly intervals to below 1% with 12 minutes of storage. With forecasting, the same level of smoothing is achieved with a 5-minute rated storage. A sensitivity analysis shows the impacts of varying constraints, such as storage power rating, PV system size for geographic smoothing, forecast window length, and the ramp-rate limit magnitude.