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The goal of this research is to understand the economics of anticipated large-scale changes in the electric system. 86 million different combinations of renewable generation (wind and solar), natural gas, and three storage types (hydrogen storage, electric vehicles equipped with vehicle-to-grid (V2G) technology, and building heat) are modeled within the PJM Interconnection. The corresponding electric systems are then operated and constrained to meet the load every hour over four years. The total cost of each energy system is calculated, both with and without externalities, to find the least cost energy systems. Using today's costs of conventional and renewable electricity and without adding any externalities, the cost-minimum system includes no renewable generation, but does include EVs. When externalities are included, however, the most cost-effective to system covers 50% of the electric load with renewable energy and runs reliably without need for either new conventional generation or purpose-built storage. The three novel energy policy implications of this research are: (1) using today's cost of renewable electricity and estimates of externalities, it is cost effective to implement 240 GW of renewable electricity to meet 50% of the total electric load; (2) there is limited need to construct new natural gas power plants, especially from a system-wide perspective; and (3) existing coal plants may still be useful to the energy system, and instead of being retired, should be repurposed to occasionally provide generation.

Summary A decade of research has shown that numerical weather prediction (NWP)‐modeled wind speeds can be highly sensitive to the inputs and setups within the NWP model. For wind resource characterization applications, this sensitivity is often addressed by constructing a range of setups and selecting the one that best validates against observations. However, this approach is not possible in areas that lack high‐quality hub height observations, especially offshore wind areas. In such cases, techniques to quantify and disseminate confidence in NWP‐modeled wind speeds in the absence of observations are needed. We address this need in the present study and propose best practices for quantifying the spread in NWP‐modeled wind speeds. We implement an ensemble approach in which we consider 24 different setups to the Weather Research and Forecasting (WRF) model. We construct the ensemble by considering variations in WRF version, WRF namelist, atmospheric forcing, and sea surface temperature (SST) forcing. Our analysis finds that the standard deviation produces more consistent estimates compared to the interquartile range and tends to be the more conservative estimator for ensemble variability. We further find that model spread increases closer to the surface and on shorter time scales. Finally, we explore methods to attribute total ensemble variability to the different ensemble components (e.g., atmospheric forcing and SST product) and find that contributions by components also vary depending on time scale. We anticipate that the methods and results presented in this paper will provide a reasonable foundation for further research into ensemble‐based wind resource data sets.

Wind energy has been growing steadily in the U.S. and worldwide in the past decades. As wind farms are projected to increase in size and number, however, concerns are rising about possible undesirable effects of wind turbines near the Earth's surface. The literature is highly divided about what these effects could be, including warming, cooling, both, or neither. Only one mechanism, however, has been widely accepted (but never tested) to explain how wind turbines affect the lower boundary layer, namely that turbulence generated in wind turbine wakes enhances vertical mixing near the ground. Wakes are plume-like volumes downwind of wind turbines that are characterised by lower wind speeds and higher turbulent kinetic energy (TKE) than the undisturbed upwind flow. The few observational campaigns that have measured changes in near-surface properties by wind turbines have not provided an answer with respect to vertical mixing near the ground. To fill this knowledge gap, the VER-TEX (VERTical Enhanced miXing) measurement campaign was conducted in August-October 2016 near a large wind turbine in coastal Delaware, using 15 surface flux towers, a 50-m meteorological tower, a radiometer, and two scanning lidars. During VERTEX, lidar scans and a wake detection algorithm were used to detect wake events and identify which sites were affected by the wake of the wind turbine. TKE, momentum and heat fluxes near the ground were compared between the sites below the wake and those outside of it. Preliminary findings based on two case studies (30 August and 20 September 2016) suggest a lack of enhancement of vertical mixing near the ground.

The goal of this study is to evaluate the effects of anthropogenic climate change on air quality, in particular on ozone, during the summer in the U.S. mid-Atlantic region. First, we establish a connection between high-ozone (HO) days, defined as those with observed 8-h average ozone concentration greater than 70 parts per billion (ppb), and certain weather patterns, called synoptic types. We identify four summer synoptic types that most often are associated with HO days based on a 30-yr historical period (1986–2015) using NCEP–NCAR reanalysis. Second, we define thresholds for mean near-surface temperature and precipitation that characterize HO days during the four HO synoptic types. Next, we look at climate projections from five models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) for the early and late midcentury (2025–34 and 2045–54) and analyze the frequency of HO days. We find a general increasing trend, weaker in the early midcentury and stronger in the late midcentury, with 2 and 5 extra HO days per year, respectively, from 16 in 2015. These 5 extra days are the result of two processes. On one hand, the four HO synoptic types will increase in frequency, which explains about 1.5–2 extra HO days. The remaining 3–3.5 extra days are explained by the increase in near-surface temperatures during the HO synoptic types. Future air quality regulations, which have been successful in the historical period at reducing ozone concentrations in the mid-Atlantic, may need to become stricter to compensate for the underlying increasing trends from global warming.

The large wind resource and population centers along the Mid-Atlantic Coast of the United States make it an attractive region to develop offshore wind power. Understanding and accurately predicting the meteorology offshore is fundamental to efficient integration of these future wind farms into an electrical grid. Particular interest is focused on anticipating and reducing errors associated with wind ramps, which are characterized by rapid, sustained changes in wind speed over a period of hours. Using meteorological observations from a coastal buoy, we characterize 428 wind ramp-ups between 2005 and 2012 in terms of frequency, duration and magnitude, time and date of occurrence, and large-scale synoptics. From this group, we select 24 case studies that represent typical and extreme ramp-ups at this location and then model the impact that the forecasting error of these ramp-ups could have on the electrical grid. The case studies are modeled with the Weather Research and Forecasting (WRF) model and compared with nearby buoy observations to assess forecast errors in ramp-up timing, shape, and magnitude. WRF frequently simulated ramp-ups too early, relative to observations, and often predicted the pre-ramp-up wind speed to be too high or the post-ramp-up wind speed to be too low. In the case of August 14, 2007, all three forecast errors occurred. The simulated impact on the grid is calculated by comparing the errors in forecast power with concurrent electrical load data in PJM's Mid-Atlantic Area Council-East (PJM-E) region. The largest impacts on the electrical grid were found to occur in the winter or at night in the summer. This framework allows for the identification of events that would potentially cause problems for the electrical grid in the PJM-E region.

Rotor-layer wind resource and turbine available power uncertainties prior to wind farm construction may contribute to significant increases in project risk and costs. Such uncertainties exist in part due to limited offshore wind measurements between 40 and 250 m and the lack of empirical methods to describe wind profiles that deviate from a priori, expected power law conditions. In this article, we introduce a novel wind profile classification algorithm that accounts for nonstandard, unexpected profiles that deviate from near power law conditions. Using this algorithm, offshore Doppler wind lidar measurements in the Mid-Atlantic Bight are classified based on goodness-of-fit to several mathematical expressions and relative speed criteria. Results elucidate the limitations of using power law extrapolation methods to approximate average wind profile shape/shear conditions, as only approximately 18% of profiles fit well with this expression, while most consist of unexpected wind shear. Further, results demonstrate a relationship between classified profile variability and coastal meteorological features, including stability and offshore fetch. Power law profiles persist during unstable conditions and relatively weaker northeasterly flow from water (large fetch), whereas unexpected classified profiles are prevalent during stable conditions and stronger southwesterly flow from land (small fetch). Finally, the magnitude of the discrepancy between hub-height wind speed and rotor equivalent wind speed available power estimates varies by classified wind-profile type. During unexpected classified profiles, both a significant overprediction and underprediction of hub-height wind available power is possible, illustrating the importance of accounting for site-specific rotor-layer wind shear when predicting available power. KEYWORDS atmospheric stability, coastal meteorology, Doppler wind lidar, offshore wind resource, rotor equivalent wind speed, turbine available power INTRODUCTIONOffshore wind (OSW) power provides an enormous clean electricity resource to help mitigate anthropogenic climate change and stimulate the economy. There is a significant opportunity to generate OSW power since most of the global resource is untapped with 90% of the 12 GW installed capacity worldwide concentrated in the relatively small geographic region of Northern Europe. 1 Global investment in OSW development reached a record high in 2016, signaling an important transition in the industry toward accelerating expertise 2 ; however, preconstruction energy yield uncertainty is a reoccurring challenge that contributes to significant project risk and could delay the technology's cost competitiveness. 3Inaccuracies in estimating turbine performance prior to wind farm construction contributes to preconstruction energy yield uncertainty. 3 It is well known that vertical wind speed shear across a turbine's rotor layer contributes to power performance uncertainty (eg, previous studies 4-8 ). To help reduce this uncertainty, a rotor equivalent wind speed (REW...

The reliable integration of wind energy into modern-day electricity systems heavily relies on accurate short-term wind forecasts. We propose a spatiotemporal model called AIRU-WRF (short for the AI-powered Rutgers University Weather Research & Forecasting), which fuses numerical weather predictions (NWPs) with local observations in order to make wind speed forecasts that are short-term (minutes to hours ahead), and of high resolution, both spatially (site-specific) and temporally (minute-level). In contrast to purely data-driven methods, we undertake a "physics-guided" machine learning approach which captures salient physical features of the local wind field without the need to explicitly solve for those physics, including: (i) modeling wind field advection and diffusion via physically meaningful kernel functions, (ii) integrating exogenous predictors that are both meteorologically relevant and statistically significant; and (iii) linking the multi-type NWP biases to their driving meso-scale weather conditions. Tested on real-world data from the U.S. North Atlantic where several offshore wind projects are in-development, AIRU-WRF achieves notable improvements, in terms of both wind speed and power, relative to various forecasting benchmarks including physics-based, hybrid, statistical, and deep learning methods.