Direct Normal Irradiance (DNI) is one of the most used quantities to quantify the magnitude of solar energy resource. The concept of DNI is often interpreted differently for ground measurements and solar forecasting by numerical weather prediction (NWP) models, leading to substantial bias during evaluation of DNI forecasts especially under cloudy-sky conditions. To eliminate the bias, we use the Fast All-sky Radiation Model for Solar applications with DNI (FARMS-DNI) to provide a physics-based solution of solar radiation in the circumsolar region. The FARMS-DNI is implemented in the Weather Research and Forecasting model with solar extensions (WRF-Solar) to forecast day-ahead DNI in the north America. By comparing with conventional predictions from WRF-Solar and satellite observations from the National Solar Radiation Data Base (NSRDB), we found significant improvements in our prediction of DNI.

Solar Energy Forecasting and Resource Assessment is a vital text for solar energy professionals, addressing a critical gap in the core literature of the field. As major barriers to solar energy implementation, such as materials cost and low conversion efficiency, continue to fall, issues of intermittency and reliability have come to the fore. Scrutiny from solar project developers and their financiers on the accuracy of long-term resource projections and grid operators' concerns about variable short-term power generation have made the field of solar forecasting and resource assessment pivotally important. This volume provides an authoritative voice on the topic, incorporating contributions from an internationally recognized group of top authors from both industry and academia, focused on providing information from underlying scientific fundamentals to practical applications and emphasizing the latest technological developments driving this discipline forward. - The only reference dedicated to forecasting and assessing solar resources enables a complete understanding of the state of the art from the world's most renowned experts. - Demonstrates how to derive reliable data on solar resource availability and variability at specific locations to support accurate prediction of solar plant performance and attendant financial analysis. - Provides cutting-edge information on recent advances in solar forecasting through monitoring, satellite and ground remote sensing, and numerical weather prediction.

Electrical energy plays vital role in our socio-economic activity and therefore ensuring the reliability of the electric grid, from the generation, transmission and distribution level is critical. In order to maintain the power system parameter viz., frequency, voltage, etc., optimally, balancing of generation and consumption is very much essential. However, solar energy is infirm power by nature this is due to cloud cover / other local phenomena. Hence, Photovoltaic (PV) power generation brings a significant challenge to the grid operator due to the variability of the solar energy. The complexity of this challenge in terms of planning and dispatch ability of PV resources, aggravates with the high penetration of solar energy into the electric grid. In this setting, reliable solar radiation forecasting models based on accurate and quality input data become essential. In order to develop a suitable model for predicting solar radiation, quality historical / real time measurement is also needed. Under this study NIWE and NREL jointly developed / tested short-term solar forecasting frameworks using a smart persistence and physics-based smart persistence models for intra-hour forecasting of solar radiation (PSPI) and benchmarked 9 different data imputation techniques in 15 Solar Radiation Resource Assessment (SRRA) stations, located at different parts of India. During any measurement campaign, due to various technical reasons, we may miss few observations. However, the missing observation often reduce the performance of any forecasting model. Therefore, suitable data imputation method would assist us to obtain continuous observation of solar radiation. A station-by-station and method-by-method analysis was carried out to understand the performance of each model. Based on our analysis, among all the data imputation methods, the Kalman data imputation method is better for Indian Weather condition. In addition, Kalman StructTS, Linear, Stine and Arima methods yield slightly inferior accuracy compared to Kalman, but outperform the other methods. The extended solar radiation data are used by solar forecasting models to provide the prediction of solar radiation at 15 SRRA stations. As far as short term forecasting model is concerned, the PSPI model outperforms the Smart Persistence model. However, the forecast error is increases with the forecasting horizon.

This presentation provides a high-level overview of a fast radiative transfer model for solar resource assessment and forecasting.

This presentation provides a high-level overview of a fast radiative transfer model for solar resource assessment and forecasting.

The smart grid initiative, integrating advanced sensing technologies, intelligent control methods, and bi-directional communications into the contemporary electricity grid, offers excellent opportunities for energy efficiency improvements and better integration of distributed generation, coexisting with centralized generation units within an active network. A large share of the installed capacity for recent renewable energy sources already comprises insular electricity grids, since the latter are preferable due to their high potential for renewables. However, the increasing share of renewables in the power generation mix of insular power systems presents a significant challenge to efficient management of the insular distribution networks, mainly due to the variability and uncertainty of renewable generation. More than other electricity grids, insular electricity grids require the incorporation of sustainable resources and the maximization of the integration of local resources, as well as specific solutions to cope with the inherent characteristics of renewable generation. Insular power systems need a new generation of methodologies and tools to face the new paradigm of large-scale renewable integration. Smart and Sustainable Power Systems: Operations, Planning, and Economics of Insular Electricity Grids discusses the modeling, simulation, and optimization of insular power systems to address the effects of large-scale integration of renewables and demand-side management. This practical book: Describes insular power systems, renewable energies, uncertainty, variability, reserves, and demand response Examines state-of-the-art forecasting techniques, power flow calculations, and scheduling models Covers probabilistic and stochastic approaches, scenario generation, and short-term operation Includes comprehensive testing and validation of the mathematical models using real-world data Explores electric price signals, competitive operation of distribution networks, and network expansion planning Smart and Sustainable Power Systems: Operations, Planning, and Economics of Insular Electricity Grids provides a valuable resource for the design of efficient methodologies, tools, and solutions for the development of a truly sustainable and smart grid.