2020 was the most active wildfire season in recent history. This study leverages solar models to quantify the solar impacts of wildfire smoke in western North America 2001 – November 2020. We observe a sharp increase in the number of days impacted by aerosol events. Record deviations in clear sky DNI are found at the Hanford, CA, Boulder, CO, and Desert Rock, NV study locations. Total sunlight (GHI) for September was diminished by up to 20% in some locations; California’s Central Valley and parts of the Columbia River Basin were hardest hit by the smoke. At the Hanford study location, the Aug.- Oct. 2020 deviations in modeled energy output totaled -5.9% of the historical annual average (2001-2019). The analysis demonstrates that wildfires are an important risk to production for solar projects in western North America.

Grid-connected solar power generation, either dispersed or centralized, has developed and grown at the margin of a core of dispatchable and baseload conventional generation. Its economics and management have required ever more versatile and precise historical and operational solar resource information with increasing penetration. Operational solar forecasts have become central to both TSO/RTO and distribution operations in regions with significant solar penetration, for e.g., energy markets, ramp and power quality management.

We introduce firm solar forecasts as a strategy to operate optimally overbuilt solar power plants in conjunction with optimally sized storage systems so as to make up for any power prediction errors, and hence entirely remove load balancing uncertainty emanating from grid-connected solar fleets. A central part of this strategy is the plant overbuilding that we term implicit storage. We show that strategy, while economically justifiable on its own account, is an effective entry step to achieving least-cost ultra-high solar penetration where firm power generation will be a prerequisite. We demonstrate that in the absence of an implicit storage strategy, ultra-high solar penetration would be vastly more expensive. Using the New York Independent System Operator (NYISO) as a case study, we determine current and future costs of firm forecasts for a comprehensive set of scenarios in each ISO electrical region, comparing centralized vs. decentralized production and assessing load flexibility’s impact. We simulate the growth of the strategy from firm forecast to firm power generation. We conclude that ultra-high solar penetration enabled by the present strategy, whereby solar would firmly supply the entire NYISO load, could be achieved locally at electricity production costs comparable to current NYISO wholesale market prices.

We present the perfect forecast concept as both an effective forecast validation metric and an operational strategy to integrate increasing amounts of variable solar power generation on power grids. The costs incurred in transforming imperfect into perfect predictions define the new metric: these include the costs of backup storage and output curtailment necessary to make-up for any over/under predictions. We illustrate the concept with the most recent version of the SUNY forecast model for hour-ahead and day-ahead forecast examples with single power plants as well as distributed PV fleets. We show that delivering perfect predictions – i.e., fully eliminating grid-operators uncertainty -is achievable at small operational cost. Most importantly, we show that a perfect forecast strategy with optimized least-cost storage and overbuild/curtailment is an effective first step of a longterm strategy to cost-optimally transform variable PV generation into firm, effectively dispatchable generation capable of displacing conventional dispatchable and baseload generation. Key-words: solar resource, irradiance, forecast, storage, high-penetration, firm power generation

The SUNY solar irradiance forecast model is implemented in the SolarAnywhere platform. In this article, we evaluate its latest version and present a fully independent validation for climatically distinct individual US locations as well as one extended region. In addition to standard performance metrics such as mean absolute error or forecast skill, we apply a new operational metric that quantifies the lowest cost of operationally achieving perfect forecasts. This cost represents the amount of solar production curtailment and backup storage necessary to correct all over/under-prediction situations. This perfect forecast metric applies a recently developed algorithm to optimally transform intermittent renewable power generation into firm power generation with the optimal - least-cost – amount of curtailment and energy storage. We discuss how perfect forecast logistics can gradually evolve and scale up into firm solar power generation logistics, with the objective of cost-optimally displacing conventional [dispatchable] power generation.

Solar resource measurements play a critical role in the assessment of long-term energy yield and the valuation of PV systems. Recent valuation methods have focused on the probability of exceedance statistic (PXX) for annual insolation as part of computing project risk. For example. P90 is the annual insolation value exceeded 90% of the time. However, the small sample size of annual insolation values for a given location increases uncertainty in the distribution. To assess the distribution of annual insolation values, we aggregate ground-and satellite-based data for the continental United States from 1961-2017, aggregate the data regionally and by climate zone, and report variability statistics for each location. Overall, the P99 annual insolation values were found to range from -2 to -8% of the P50 value across the continental United States.

This article introduces a new version of the SUNY solar forecast model, as implemented in the software SolarAnywhere®. Like the existing version, this new version is intended for direct, out-of-the-box application throughout North America without requiring training/feedback from measured data. The existing version was recently identified by EPRI as most accurate among thirteen operational models after an independent evaluation in two climatically distinct US regions. This new version shows further measurable performance improvements and operational functionality with capability of using historical satellite data for model training purposes. The advantage of incorporating historical satellite data is that it allows this forecast application to scale to all sizes of PV systems (large utility-scale down to individual rooftop) without the requirement of site-measured inputs. This new approach exhibits a 1-5% root mean square error (RMSE) improvement over the forecast horizon up to two-days in advance. Index Terms —solar resource, irradiance, solar forecasting, PV fleet forecasting.

In this article we show how a state-of-the-art satellite irradiance model - SolarAnywhere - is capable of undetected calibration issues at a trusted reference ground truth irradiance measurement station. This evidence suggests that the best satellite models have now achieved a degree of accuracy and versatility that makes them an acceptable, if not a preferred choice, for solar energy engineering applications ranging from long-term site characterization and system monitoring.

In this article, we present a state-of-the-art longtern energy prediction methodology to minimize risk in the project investment. We applied the algorithm to the SolarAnywhere data over 19 years period. he algorithm systematically creates synthetic years from the period of dataset to draw distribution that best describes the data. We conducted the experiment for projects under different climatic conditions. We compared the result with several theoretical distribution models. The results show that the method is so robust that it can predict the real production data at a site with high level of accuracy and provide confidence in the investment.

This article evaluates the accuracy of solar energy forecasts as a function of geographic footprint ranging from a single point to regions spanning several hundred km. The forecast models that are evaluated include SolarAnywhere®, ECMWF, GFS, HRRR, NDFD and satellite-based cloud motion. The forecast time horizons range from one hour ahead to 2 days ahead. In addition, a new accuracy metric is introduced: this metric quantifies the cost of remedying forecast errors with backup generation if the forecasts overpredict, or with curtailment in case of underprediction.

Solar resource uncertainty has the potential to significantly contribute to the financial risk of a PV project. This uncertainty has been studied in depth for satellite-derived global horizontal irradiance (GHI), which are often used as long-term average dataset for project valuations. Resource uncertainty, however, is not as well quantified in the plane of the array of the solar modules. This paper presents results that quantify the residual errors in plane of array irradiance (POAI) at locations where ground-measured GHI and POAI instruments are installed. Reductions in error to the modeled PAOI are observed when using satellite derived GHI that has been tuned with ground measured GHI for reduction in respective model errors.

Increasing adoption of Solar Photovoltaic (PV) generation at the distribution level poses several changes for the reliable operation of electrical power distribution systems. The addition of a significant amount of PV to a distribution network can introduce a variety of operational problems, including steady-state overvoltages, reverse flows, voltage flicker and excessive controller movement among others. These adverse impacts can be mitigated through a variety of equipment upgrades which represent a cost to either the electric utility or the owner of the PV site. The study performed here aims to quantify the levels of PV generation which present operational problems on a distribution circuit and how those problems might be alleviated. The study performed here included 20 distribution feeders selected from Pepco Holdings, Inc. (PHI) service territory. These feeders are located in the states of Delaware, Maryland and New Jersey. A hosting capacity study was performed on each feeder to determine how much additional PV it could support in its current configuration. Several improvements were then performed on these circuits including phase balancing, capacitor redesign, reducing the voltage regulator set points, fixed power factor operation on the PV inverters and the installation of battery storage. After each of these improvements the hosting capacity of the circuit was reevaluated in order to determine how that particular improvement impacted the amount of PV that could be hosted by the circuit. Each of these improvements represents a real cost in terms of labor and equipment in order to be implemented. They are expected to provide a benefit in terms of the amount of additional PV generation which can be safely interconnected to the distribution feeder. A cost benefit analysis was performed in order to evaluate the expected costs of each feeder improvement and how each one was able to increase the PV hosting capacity of each feeder. It is hoped that these results can be utilized by other distribution utilities in order to understand how they can improve the hosting capacity of their feeders and facilitate the deployment of more PV generation at the distribution level. Admittedly other utility companies will most likely have different feeder architectures and differing labor and equipment costs so the realized cost benefit numbers may be significantly different.

We approach the issue of short-term PV output intermittency from a management standpoint by determining the cost of actively mitigating it using “shock-absorbing” short-term energy buffers. Using a case study in Central California as experimental support, we determine this cost of as a function of (1) the amount of variability mitigation; (2) the considered variability time scale, (3) the PV resource's geographical footprint, and (4) the availability of accurate solar forecasts. We show that, in a plausible operational context, the cost of mitigating variability across time scales ranging from one minute to a couple of hours could be kept below 25-35 cents per installed PV kW.

In 2010, the National Renewable Energy Laboratory (NREL), Southern California Edison (SCE), Quanta Technology, Satcon Technology Corporation, Electrical Distribution Design (EDD), and Clean Power Research (CPR) teamed to analyze the impacts of high penetration levels of photovoltaic (PV) systems interconnected onto the SCE distribution system. This project was designed specifically to benefit from the experience that SCE and the project team would gain during the installation of 500 megawatts (MW) of utility-scale PV systems (with 1-5 MW typical ratings) starting in 2010 and completing in 2015 within SCE's service territory through a program approved by the California Public Utility Commission (CPUC). This report provides the findings of the research completed under the project to date.

Validation and operational improvements to the existing SUNY satellite-to-solar irradiance model through incorporation of four of the geostationary satellite infrared (IR) channels are presented herein. The SUNY model is the gridded data set used by NREL in the National Solar Radiation Database (NSRDB) and is available commercially through the Clean Power Research software, SolarAnywhere® Data. This improved model addresses the present model's limitations when representing the irradiance conditions in circumstances of snow cover, high ground reflectance and persistent cloud cover. Improvements in the satellite-to-solar irradiance model have been realized using the IR channels to detect snow conditions and modulate the model background to more accurately reflect irradiance conditions.

We approach the issue of short-term PV output intermittency from a management standpoint by determining the cost of actively mitigating it using “shock-absorbing” short-term energy buffers. Using three case studies in California, Hawaii and the southern US as experimental support, we determine this cost as a function of (1) the desired amount of variability mitigation; (2) the considered variability time scale, (3) the PV resource’s geographical footprint, and (4) the availability of accurate solar forecasts. We show that, in a plausible operational context, the cost of mitigating variability across time scales ranging from one minute to a couple of hours could be kept below 25-35 cents per installed PV kW.