Search

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]

This article is primarily concerned with the optimal configuration of multi-energy microgrid in cold climate. Although involving photovoltaic (PV) has been commonplace in works of this sort, one thing that has yet to receive sufficient care is pertained to how exactly PV power output is modeled. Indeed, refined PV modeling has an decisive effect on whether or not the configured microgrid is going to operate as intended in the long run. On this account, physical model chain, a refined way to convert irradiance to PV power output through a series of models, as opposed to the conventional one-equation conversion, is herein emphasized. Then, by adopting the mixed integer linear programming, the configuration of a microgrid with both residential and commercial buildings in cold climate, alongside a rich selection of energy converters and storage devices, is performed. The case study results suggest: (1) configurations based solely on the conventional PV model tend to exaggerate the power production from PV by 10.31%, causing a severe underestimation in total annualized cost by 8.26%; (2) thermoelectric coupling in cold climate favors the inclusion of combined heat and power (CHP) units, which occupy 17% of total capacity; and (3) when heavy carbon emission penalty is invoked, the optimal configuration requires one to double the CHP installation but only add a moderate amount of PV. To support open research, data and Python code used to generate the results are offered as supplementary materials. • A cold-climate microgrid configuration model with refined PV modeling is presented. • Conventional PV modeling underestimates the total annualized cost by 8.26%. • Thermoelectric coupling favors CHP deployment (17% of total capacity) in cold region. • Invoking heavy carbon emission penalty requires doubling the CHP units but only adding a moderate amount of PV. [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]

Fengyun-4A (FY-4A), which is the latest-generation Chinese geostationary meteorological satellite, measures solar reflection and thermal emission with high temporal, spatial, and spectral resolutions. It is expected to be highly beneficial for solar resource assessment and forecasting in China. This study is the first to estimate, using FY-4A and a random forest model, the global horizontal irradiance (GHI) at a 4-km–15-min spatio-temporal resolution over China, as a means to arrive at a solar photovoltaic (PV) resource map. In terms of GHI estimates, the root mean square error and mean bias error between hourly measured and retrieved values are 147.02 (35.2%), −5.64 W/m2 (−1.4%), respectively, whereas the values of daily estimates are 29.20 (18.0%), −2.97 W/m2 (−1.3%). The retrieval accuracy is found much better for instances with solar zenith angles smaller than 60°. Relatively larger errors are found at locations in the Sichuan Basin and northeastern China, which can be attributed to bright surfaces and/or strong cloud transients. With the retrieved irradiance, PV resource is derived through a physical model chain. The annual mean PV resource map suggests that, over most of the west areas, the annual mean effective irradiance exceeds 1700 kWh/m2, with the highest value found in Tibet (around 2000 kWh/m2 per annum). Eastern China has an annual effective irradiance of only 1300–1500 kWh/m2. The region with poorest solar resource is the Sichuan Basin (less than 1100 kWh/m2 per annum). • Solar PV resource map based on FY-4A over China is established for the first time. • An accurate model chain is used to derive PV potential from hourly irradiance estimates. • High-resolution FY-4A-based GHI map sheds new light on PV resourcing over China. [ABSTRACT FROM AUTHOR]

Since directly measuring beam and diffuse irradiance is not feasible on many occasions, one often has to resort to estimating the beam and diffuse irradiance components from the global irradiance, which is known as separation modeling. Separation modeling is essentially a nonlinear regression problem, with the clearness index being the main input and the diffuse fraction being the output. Hundreds of separation models with various complexities have been proposed, among which the Yang4 model was recently validated using worldwide data as the quasi-universal choice for 1-min data. In this work, Yang4 is further improved by regime-dependent fitting, i.e., fitting a separate set of model coefficients for each climatological regime. Different regimes are determined through clustering of cloud cover frequency, aerosol optical depth, and surface albedo climatology maps. The new Yang5 model is able to outperform its predecessor at the 126 stations tested, covering a wide range of climate types. Overall, the normalized root mean square errors for beam normal irradiance (BNI) and diffuse horizontal irradiance (DHI) of Yang5 are 17.55% and 32.92% on average, as compared to 19.13% and 34.94% for the next best model, namely, Yang4. Furthermore, through conducting pairwise Diebold–Mariano tests, Yang5 is shown superior to Yang4 at 110/126 sites for BNI prediction and 93/126 for DHI. [Display omitted] • A concise summary of recent advances in separation modeling is given. • A regime-dependent version of the Yang4 model is proposed. • Regimes are defined by clustering cloud, aerosol, and albedo climatology. • The new Yang5 model outperforms the quasi-universal Yang4. [ABSTRACT FROM AUTHOR]

Current solar forecast verification processes place much attention on performance comparison of a group of competing methods. However, forecast verification ought to further answer how the best method within the group performs relative to the best-possible performance which one can attain under that forecasting situation, which makes the quantification of predictability and forecast skill immediately relevant. Unfortunately, the literature on the quantification of relative performance of solar irradiance has hitherto been lacking, and very few studies have focused on the spatial distributions of predictability and forecast skill of solar irradiance. The predictability and forecast skill of an atmospheric process depend on two concepts: (1) the growth of initial error in unresolved scale of motion, and (2) the forecast performance of the standard of reference. Based upon this formalism, predictability and forecast skill of solar irradiance in the United States are quantified and mapped. Through this study, a couple of common misconceptions in regard to irradiance predictability are refuted, and the original formulation of skill score revived. • A formal discussion on predictability of solar irradiance is presented. • A rigorous way of computing the forecast skill score is emphasized. • Predictability of solar irradiance in the United States is estimated. • Bounds of mean square error of ECMWF irradiance forecasts in the United States are derived. [ABSTRACT FROM AUTHOR]