In recent years, the contribution of photovoltaic (PV) power production to the electric grid has been increasing. Still, a number of challenges remain for a reliable and efficient integration of solar energy. While conventional electric power generated by gas turbines can be adjusted to follow the grid load, the stochastic nature of solar radiation makes it difficult to control the PV output, which hinders its integration in the grid. Accurate solar forecasts help grid operators integrate solar energy by enhancing power quality and reducing grid operation costs. Following the development of sky imager hardware and algorithms at UC San Diego, we present a variety of models and methodologies to reduce sky imager forecasts errors by improving the accuracy of meteorological parameters, compensating the power mismatch caused by solar forecasts errors, and mitigating the impact of solar forecast errors on real world grid planning and operations. First, a low-cost instrument for measuring local cloud motion vectors (CMVs) was developed. Three algorithms for estimating local cloud base height (CBH) using a single sky imager paired with either distributed ground irradiance sensors or measured CMVs were then designed and tested. Since sky imager forecasts are often used in conjunction with other instruments for measuring CBH, cloud velocity, and/or solar irradiance measurements, our approaches decrease instrumentation costs and logistical complexity. More importantly, through these algorithms, local measurements improve sky imager forecasts by adding information that is unobservable from a single sky imager. Second, integrating battery systems into a PV plant can compensate the power imbalance caused by solar forecast errors. Battery system size can be optimized by determining the energy reserve required to offset the possible maximum power ramp. Because passing cloud shadows are the main cause of the power ramps, a simple model based on physics variables that are available globally can determine the worst power ramp rates. Local CMV measurements enable even more accurate maximum ramp rate estimates. The key merit of the method is that it is universally applicable in the absence of high frequency measurements. Finally, issues when integrating imperfect solar forecasts in grid operations are evaluated. Both physics-based forecasts and Numerical Weather Prediction (NWP) based machine learning forecasts that are commonly utilized in the grid operations exhibit autocorrelated forecast errors. First, a deterministic valley-filling problem through EV charging is formulated to investigate how the autocorrelated forecast errors increase peak demand and cause grid net load variability. Then a corrective optimization framework is proposed to minimize the deviation of the realistic valley filling solutions from the ideal solutions. In addition, with the goal of operational deployment, stochastic programming incorporating real time updates of solar forecast and EV charge events to address real-world uncertainty is employed. The optimal valley filling problem is solved in an innovative way and executed under a predictive control scheme in the presence of autocorrelated forecast errors. The proposed corrective stochastic optimization framework successfully mitigates the impact of autocorrelated forecasts errors on grid operations.

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.

Americans' safety, productivity, comfort, and convenience depend on the reliable supply of electric power. The electric power system is a complex "cyber-physical" system composed of a network of millions of components spread out across the continent. These components are owned, operated, and regulated by thousands of different entities. Power system operators work hard to assure safe and reliable service, but large outages occasionally happen. Given the nature of the system, there is simply no way that outages can be completely avoided, no matter how much time and money is devoted to such an effort. The system's reliability and resilience can be improved but never made perfect. Thus, system owners, operators, and regulators must prioritize their investments based on potential benefits. Enhancing the Resilience of the Nation's Electricity System focuses on identifying, developing, and implementing strategies to increase the power system's resilience in the face of events that can cause large-area, long-duration outages: blackouts that extend over multiple service areas and last several days or longer. Resilience is not just about lessening the likelihood that these outages will occur. It is also about limiting the scope and impact of outages when they do occur, restoring power rapidly afterwards, and learning from these experiences to better deal with events in the future.

Renewable Energy Forecasting: From Models to Applications provides an overview of the state-of-the-art of renewable energy forecasting technology and its applications. After an introduction to the principles of meteorology and renewable energy generation, groups of chapters address forecasting models, very short-term forecasting, forecasting of extremes, and longer term forecasting. The final part of the book focuses on important applications of forecasting for power system management and in energy markets. Due to shrinking fossil fuel reserves and concerns about climate change, renewable energy holds an increasing share of the energy mix. Solar, wind, wave, and hydro energy are dependent on highly variable weather conditions, so their increased penetration will lead to strong fluctuations in the power injected into the electricity grid, which needs to be managed. Reliable, high quality forecasts of renewable power generation are therefore essential for the smooth integration of large amounts of solar, wind, wave, and hydropower into the grid as well as for the profitability and effectiveness of such renewable energy projects. Offers comprehensive coverage of wind, solar, wave, and hydropower forecasting in one convenient volume Addresses a topic that is growing in importance, given the increasing penetration of renewable energy in many countries Reviews state-of-the-science techniques for renewable energy forecasting Contains chapters on operational applications