The diurnal nature of solar power is made uncertain by variable cloud cover and the influence of atmospheric conditions on irradiance scattering processes. Its forecasting has become increasingly important to the unit commitment and dispatch process for efficient scheduling of generators in power system operations. This presentation is an overview of a study that examines the value of improved solar forecasts on Bulk Power System Operations.

The diurnal nature of solar power is made uncertain by variable cloud cover and the influence of atmospheric conditions on irradiance scattering processes. Its forecasting has become increasingly important to the unit commitment and dispatch process for efficient scheduling of generators in power system operations. This study examines the value of improved solar power forecasting for theIndependent System Operator-New England system. The results show how 25% solar power penetration reduces net electricity generation costs by 22.9%.

The diurnal nature of solar power is made uncertain by variable cloud cover and the influence of atmospheric conditions on irradiance scattering processes. Its forecasting has become increasingly important to the unit commitment and dispatch process for efficient scheduling of generators in power system operations. This study examines the value of improved solar power forecasting for the Independent System Operator-New England system. The results show how 25% solar power penetration reduces net electricity generation costs by 22.9%.

This book dispels common myths about electricity and electricity policy and reveals how government policies manipulate energy markets, create hidden costs, and may inflict a net harm on the American people and the environment. Climate change, energy generation and use, and environmental degradation are among the most salient—and controversial—political issues today. Our country's energy future will be determined by the policymakers who enact laws that favor certain kinds of energy production while discouraging others as much as by the energy-production companies or the scientists working to reduce the environmental impact of all energy production. The Reality of American Energy: The Hidden Costs of Electricity provides rare insights into the politics and economics surrounding electricity in the United States. It identifies the economic, physical, and environmental implications of distorting energy markets to limit the use of fossil fuels while increasing renewable energy production and explains how these unseen effects of favoring renewable energy may be counterproductive to the economic interests of American citizens and to the protection of the environment. The first two chapters of the book introduce the subject of electricity policy in the United States and to enable readers to understand why policymakers do what they do. The remainder of the book examines the realities of the major electricity sources in the United States: coal, natural gas, nuclear, hydrodynamic, wind, biomass, solar, and geothermal. Each of these types of energy sources is analyzed in a dedicated chapter that explains how the electricity source works and identifies how politics and public policy shape the economic and environmental impacts associated with them.

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.

This book provides a detailed roadmap of technical, economic, and institutional actions by the wind industry, the wind research community, and others to optimize wind's potential contribution to a cleaner, more reliable, low-carbon, domestic energy generation portfolio, utilizing U.S. manu-facturing and a U.S. workforce. The roadmap is intended to be the beginning of an evolving, collaborative, and necessarily dynamic process. It thus suggests an approach of continual updates at least every two years, informed by its analysis activities. Roadmap actions are identified in nine topical areas, introduced below.