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Numerical weather prediction (NWP), when accessible, is a crucial input to short-term solar power forecasting. WRF-Solar, the first NWP model specifically designed for solar energy applications, has shown promising predictive capability. Nevertheless, few attempts have been made to investigate its performance under high aerosol loading, which attenuates incoming radiation significantly. The North China Plain is a polluted region due to industrialization, which constitutes a proper testbed for such investigation. In this paper, aerosol direct radiative effect (DRE) on three surface shortwave radiation components (i.e., global, beam, and diffuse) during five heavy pollution episodes is studied within the WRF-Solar framework. Results show that WRF-Solar overestimates instantaneous beam radiation up to 795.3 W m−2 when the aerosol DRE is not considered. Although such overestimation can be partially offset by an underestimation of the diffuse radiation of about 194.5 W m−2, the overestimation of the global radiation still reaches 160.2 W m−2. This undesirable bias can be reduced when WRF-Solar is powered by Copernicus Atmosphere Monitoring Service (CAMS) aerosol forecasts, which then translates to accuracy improvements in photovoltaic (PV) power forecasts. This work also compares the forecast performance of the CAMS-powered WRF-Solar with that of the European Centre for Medium-Range Weather Forecasts model. Under high aerosol loading conditions, the irradiance forecast accuracy generated by WRF-Solar increased by 53.2% and the PV power forecast accuracy increased by 6.8%. Significance Statement: Numerical weather prediction (NWP) is the "go-to" approach for achieving high-performance day-ahead solar power forecasting. Integrating time-varying aerosol forecasts into NWP models effectively captures aerosol direct radiation effects, thereby enhancing the accuracy of solar irradiance forecasts in heavily polluted regions. This work not only quantifies the aerosol effects on global, beam, and diffuse irradiance but also reveals the physical mechanisms of irradiance-to-power conversion by constructing a model chain. Using the North China Plain as a testbed, the performance of WRF-Solar on solar power forecasting on five severe pollution days is analyzed. This version of WRF-Solar can outperform the European Centre for Medium-Range Weather Forecasts model, confirming the need for generating high spatial–temporal NWP. [ABSTRACT FROM AUTHOR]

Ensemble numerical weather prediction (NWP) is the backbone of the state-of-the-art solar forecasting for horizons ranging between a few hours and a few days. Dynamical ensemble forecasts are generated by perturbing the initial condition, and thereby obtaining a set of equally likely trajectories of the future weather. Generating dynamical ensemble forecasts demands extensive knowledge of atmospheric science and significant computational resources. Hence, the task is often performed by international and national weather centers and space agencies. Solar forecasters, on the other hand, are primarily interested in post-processing those ensemble forecasts disseminated by weather service providers, as to arrive at forecasts of solar power output. To facilitate the uptake of ensemble NWP forecasts in solar power forecasting research, this paper offers an archived dataset from the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System, over a four-year period (2017–2020) and over an extensive geographical region (e.g., most of Europe and North America), under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Two case studies are presented to demonstrate the usage of the dataset. One case study elaborates how ensemble forecasts can be summarized and calibrated, which constitute two common forms of probabilistic forecast post-processing. The other demonstrates how the dataset can be used in solar power forecasting applications, which compares machine learning with the physical model chain in terms of their irradiance-to-power conversion capability. The Python code used to produce the results shown in this paper is made available on GitHub. • Four years of ECMWF EPS forecasts are offered under CC BY 4.0 license. • Dynamical ensemble GHI forecasts are provided over US and Europe. • The dataset facilitates a range of solar forecasting applications. • Two case studies on post-processing and model chain construction are included. [ABSTRACT FROM AUTHOR]

A high-quality solar power forecasting system that strictly adheres to grid regulations is valuable for system operators to formulate strategies for power system scheduling. Some grid operators, therefore, enact penalty schemes for the forecasts submitted by photovoltaic (PV) plant owners, as a means to fortify truthful and high-quality forecast submissions. From the perspectives of both plant owners and grid operators, this study inquires into the quality-to-value mapping of solar forecasts in the context of power system day-ahead scheduling. A physics-based solar power forecasting method is presented, which consists of two steps. Firstly, ensemble numerical weather prediction (NWP) is summarized into point forecasts. Then irradiance is converted to power via a physical model chain. The results reveal that the two-step physics-based forecasting method has an advantage over a winning method in Global Energy Forecasting Competition 2014 in terms of several accuracy measures. Subsequently, the economics of solar forecasting is quantified through performing day-ahead scheduling on a modified IEEE 30-bus system with PV and battery storage. It is demonstrated that, by respecting the statistical theory on consistency and elicitability when extracting point forecasts from NWP ensembles, both power system operators and PV plant owners can benefit profoundly in terms of cost savings. The former sees fewer needs for reserves, while the latter is less penalized. The data and Python code used to produce the results are also provided to enhance the reproducibility of this work. [Display omitted] • Solar power forecasts are integrated into a power system day-ahead scheduling framework. • The statistical theory on consistency and elicitability is used to summarize ensemble NWP. • Achieved 5% cost savings on power system reserve needs. • Achieved up to 82% cost savings on penalty of submitted forecasts. [ABSTRACT FROM AUTHOR]

Global horizontal irradiance (GHI) forecasting at intra-day horizons of up to 12-h ahead is vital to grid integration of solar photovoltaics, but has been fundamentally difficult for all methods that do not involve numerical weather prediction (NWP), since non-NWP methods are unable to extrapolate the data to a horizon beyond a fews hours. The European Centre for Medium-Range Weather Forecasts (ECMWF) and the National Oceanic and Atmospheric Administration (NOAA) are the most representative weather centers in Europe and America, respectively. To understand their operational impact and value to grid integration, the ECMWF's High Resolution (HRES) model and two models from NOAA, namely, Rapid Refresh (RAP) and High-Resolution Rapid Refresh (HRRR), are validated through the Murphy–Winkler distribution-oriented verification framework, over the year 2020, at seven locations. All forecasts are retrieved at native horizontal resolutions—9 km for HRES, 13 km for RAP, and 3 km for HRRR; it depicts the "off-the-shelf" scenario if these forecasts are to be utilized by end users. Three simple linear correction methods, each being statistically optimal in its own respect, are used to post-process the raw forecasts. It was found that 1–12-h-ahead ECMWF's HRES forecasts have a significantly lower root mean square error (14.0–33.7%) as compared to NOAA's HRRR (19.0–53.2%) and RAP (19.2–45.9%). Even after the large biases in HRRR and RAP forecasts are removed, those post-processed versions are still inferior to the raw HRES forecasts. • Twelve-hour-ahead operational solar forecasts from ECMWF and NOAA are verified. • Murphy–Winkler distribution forecast verification framework is used. • Three optimal linear post-processing schemes are proposed and discussed. • ECMWF forecasts are found to be of higher quality than the NOAA forecasts. • The normalized RMSE of ECMWF's High Resolution forecasts ranges from 14% to 34%. [ABSTRACT FROM AUTHOR]

Weather is often found to be a key driving factor for power generation and energy consumption. To capture the effects of weather, many energy forecasting practices, such as load forecasting, renewable power generation forecasting, gas and electricity price forecasting, and power distribution systems outage forecasting, would rely on numerical weather prediction (NWP). In the academic literature, however, energy forecasting models have often been developed based on settings of ex post forecasting, where the actual observations of weather variables during the forecasted period are being used. Such gap between academic research and field practices is partly due to the shortage of historical weather forecasts. To that end, an NWP forecast dataset from the European Centre for Medium-Range Weather Forecasts (ECMWF) High Resolution (HRES) model, as available in the ECMWF's Archive Catalogue, is offered to the energy forecasting community under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Since the raw data is massive in size, a subset which is thought sufficient for energy forecasting research purposes is provided through this article. Four years (2017–2020) of HRES forecasts of 14 frequently used weather variables, over the geographical region bounded by 63° N, − 126 °W, 21° S, and 36° E (most of Europe and North America), on a 0. 5 °by 0. 5 °longitude/latitude grid, are released in the form of NetCDF files. This dataset is able to support a variety of aforementioned energy forecasting tasks. In addition to introducing various means to utilize the dataset, this article provides a set of case studies on post-processing of day-ahead solar forecasts. The R code being used to produce the results shown in this article is also made available, so that the readers can reproduce this case study as well as adopt the code for other relevant studies. • Four years of operational forecasts from the ECMWF HRES model are offered. • The dataset allows energy forecasting applications such as solar, wind, or load forecasting. • ECMWF operational model constitutes the world's best global NWP model to date. • Case studies on solar forecast post-processing are included to exemplify the usage of the dataset. [ABSTRACT FROM AUTHOR]

The ability to forecast solar irradiance plays an indispensable role in solar power forecasting, which constitutes an essential step in planning and operating power systems under high penetration of solar power generation. Since solar radiation is an atmospheric process, solar irradiance forecasting, and thus solar power forecasting, can benefit from the participation of atmospheric scientists. In this review, the two fields, namely, atmospheric science and power system engineering are jointly discussed with respect to how solar forecasting plays a part. Firstly, the state of affairs in solar forecasting is elaborated; some common misconceptions are clarified; and salient features of solar irradiance are explained. Next, five technical aspects of solar forecasting: (1) base forecasting methods, (2) post-processing, (3) irradiance-to-power conversion, (4) verification, and (5) grid-side implications, are reviewed. Following that, ten potential research topics for atmospheric scientists are enumerated; they are related to (1) data and tools, (2) numerical weather prediction, (3) forecast downscaling, (4) large eddy simulation, (5) dimming and brightening, (6) aerosols, (7) spatial forecast verification, (8) multivariate probabilistic forecast verification, (9) predictability, and (10) extreme weather events. Last but not least, a pathway towards ultra-high PV penetration is laid out, based on two recently proposed concepts of firm generation and firm forecasting. It is concluded that the collaboration between the atmospheric science community and power engineering community is necessary if we are to further increase the solar penetration while maintaining the stability and reliability of the power grid, and to achieve carbon neutrality in the long run. • Common pitfalls of solar forecasting research are given, with remedies proposed. • Five technical aspects of solar forecasting are discussed, with a special focus on grid-integration implications. • Ten potential research topics for atmospheric scientists are identified. • A pathway towards ultra-high PV penetration is laid out. [ABSTRACT FROM AUTHOR]