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This paper identifies the location of an “ideal” offshore wind energy (OWE) grid on the U.S. East Coast that would (1) provide the highest overall and peak‐time summer capacity factor, (2) use bottom‐mounted turbine foundations (depth ≤50 m), (3) connect regional transmissions grids from New England to the Mid‐Atlantic, and (4) have a smoothed power output, reduced hourly ramp rates and hours of zero power. Hourly, high‐resolution mesoscale weather model data from 2006–2010 were used to approximate wind farm output. The offshore grid was located in the waters from Long Island, New York to the Georges Bank, ≈450 km east. Twelve candidate 500 MW wind farms were located randomly throughout that region. Four wind farms (2000 MW total capacity) were selected for their synergistic meteorological characteristics that reduced offshore grid variability. Sites likely to have sea breezes helped increase the grid capacity factor during peak time in the spring and summer months. Sites far offshore, dominated by powerful synoptic‐scale storms, were included for their generally higher but more variable power output. By interconnecting all 4 farms via an offshore grid versus 4 individual interconnections, power was smoothed, the no‐power events were reduced from 9% to 4%, and the combined capacity factor was 48% (gross). By interconnecting offshore wind energy farms ≈450 km apart, in regions with offshore wind energy resources driven by both synoptic‐scale storms and mesoscale sea breezes, substantial reductions in low/no‐power hours and hourly ramp rates can be made.

Abstract This study provides estimates of photovoltaic (PV) panel optimal tilt angles for all countries worldwide. It then estimates the incident solar radiation normal to either tracked or optimally tilted panels relative to horizontal panels globally. Optimal tilts are derived from the National Renewable Energy Laboratory’s PVWatts program. A simple 3rd-order polynomial fit of optimal tilt versus latitude is derived. The fit matches data better above 40° N latitude than do previous linear fits. Optimal tilts are then used in the global 3-D GATOR-GCMOM model to estimate annual ratios of incident radiation normal to optimally tilted, 1-axis vertically tracked (swiveling vertically around a horizontal axis), 1-axis horizontally tracked (at optimal tilt and swiveling horizontally around a vertical axis), and 2-axis tracked panels relative to horizontal panels in 2050. Globally- and annually-averaged, these ratios are ∼1.19, ∼1.22, ∼1.35, and ∼1.39, respectively. 1-axis horizontal tracking differs from 2-axis tracking, annually averaged, by only 1–3% at most all latitudes. 1-axis horizontal tracking provides much more output than 1-axis vertical tracking below 65° N and S, whereas output is similar elsewhere. Tracking provides little benefit over optimal tilting above 75° N and 60° S. Tilting and tracking benefits generally increase with increasing latitude. In fact, annually averaged, more sunlight reach tilted or tracked panels from 80 to 90° S than any other latitude. Tilting and tracking benefit cities of the same latitude with lesser aerosol and cloud cover. In sum, for optimal utility PV output, 1-axis horizontal tracking is recommended, except for the highest latitudes, where optimal tilting is sufficient. However, decisions about panel configuration also require knowing tracking equipment and land costs, which are not evaluated here. Installers should also calculate optimal tilt angles for their location for more accuracy. Models that ignore optimal tilting for rooftop PV and utility PV tracking may underestimate significantly country or world PV potential.

This is a study to quantify U.S. wind power at 80 m (the hub height of large wind turbines) and to investigate whether winds from a network of farms can provide a steady and reliable source of electric power. Data from 1327 surface stations and 87 soundings in the United States for the year 2000 were used. Several methods were tested to extrapolate 10‐m wind measurements to 80 m. The most accurate, a least squares fit based on twice‐a‐day wind profiles from the soundings, resulted in 80‐m wind speeds that are, on average, 1.3–1.7 m/s faster than those obtained from the most common methods previously used to obtain elevated data for U.S. wind power maps, a logarithmic law and a power law, both with constant coefficients. The results suggest that U.S. wind power at 80 m may be substantially greater than previously estimated. It was found that 24% of all stations (and 37% of all coastal/offshore stations) are characterized by mean annual speeds ≥6.9 m/s at 80 m, implying that the winds over possibly one quarter of the United States are strong enough to provide electric power at a direct cost equal to that of a new natural gas or coal power plant. The greatest previously uncharted reservoir of wind power in the continental United States is offshore and nearshore along the southeastern and southern coasts. When multiple wind sites are considered, the number of days with no wind power and the standard deviation of the wind speed, integrated across all sites, are substantially reduced in comparison with when one wind site is considered. Therefore a network of wind farms in locations with high annual mean wind speeds may provide a reliable and abundant source of electric power.

Jurisdictions throughout the world are contemplating greenhouse gas (GHG) mitigation strategies that will enable meeting long-term GHG targets. Many jurisdictions are now focusing on the 2020–2050 timeframe. We conduct an inter-model comparison of nine California statewide energy models with GHG mitigation scenarios to 2050 to better understand common insights across models, ranges of intermediate GHG targets (i.e., for 2030), necessary technology deployment rates, and future modeling needs for the state. The models are diverse in their representation of the California economy: across scenarios with deep reductions in GHGs, annual statewide GHG emissions are 8–46 % lower than 1990 levels by 2030 and 59–84 % lower by 2050 (not including the Wind-Water-Solar model); the largest cumulative reductions occur in scenarios that favor early mitigation; non-hydroelectric renewables account for 30–58 % of electricity generated for the state in 2030 and 30–89 % by 2050 (not including the Wind-Water-Solar model) ; the transportation sector is decarbonized using a mix of energy efficiency gains and alternative-fueled vehicles; and bioenergy is directed almost exclusively towards the transportation sector, accounting for a maximum of 40 % of transportation energy by 2050. Models suggest that without new policies, emissions from non-energy sectors and from high-global-warming-potential gases may alone exceed California’s 2050 GHG goal. Finally, future modeling efforts should focus on the: economic impacts and logistical feasibility of given scenarios, interactive effects between two or more climate policies, role of uncertainty in the state’s long-term energy planning, and identification of pathways that achieve the dual goals of criteria pollutant and GHG emission reduction.

ABSTRACTThis study characterized the annual mean US East Coast (USEC) offshore wind energy (OWE) resource on the basis of 5 years of high‐resolution mesoscale model (Weather Research and Forecasting–Advanced Research Weather Research and Forecasting) results at 90 m height. Model output was evaluated against 23 buoys and nine offshore towers. Peak‐time electrical demand was analyzed to determine if OWE resources were coincident with the increased grid load. The most suitable locations for large‐scale development of OWE were prescribed, on the basis of the wind resource, bathymetry, hurricane risk and peak‐time generation potential. The offshore region from Virginia to Maine was found to have the most exceptional overall resource with annual turbine capacity factors (CF) between 40% and 50%, shallow water and low hurricane risk. The best summer resource during peak time, in water of ≤ 50 m depth, is found between Long Island, New York and Cape Cod, Massachusetts, due in part to regional upwelling, which often strengthens the sea breeze. In the South US region, the waters off North Carolina have adequate wind resource and shallow bathymetry but high hurricane risk. Overall, the resource from Florida to Maine out to 200 m depth, with the use of turbine CF cutoffs of 45% and 40%, is 965–1372 TWh (110–157 GW average). About one‐third of US or all of Florida to Maine electric demand can technically be provided with the use of USEC OWE. With the exception of summer, all peak‐time demand for Virginia to Maine can be satisfied with OWE in the waters off those states. Copyright © 2012 John Wiley & Sons, Ltd.

Summary Global warming, air pollution, and energy insecurity are three of the greatest problems facing humanity. To address these problems, we develop Green New Deal energy roadmaps for 143 countries. The roadmaps call for a 100% transition of all-purpose business-as-usual (BAU) energy to wind-water-solar (WWS) energy, efficiency, and storage by 2050 with at least 80% by 2030. Our studies on grid stability find that the countries, grouped into 24 regions, can match demand exactly from 2050 to 2052 with 100% WWS supply and storage. We also derive new cost metrics. Worldwide, WWS energy reduces end-use energy by 57.1%, aggregate private energy costs from $17.7 to $6.8 trillion/year (61%), and aggregate social (private plus health plus climate) costs from $76.1 to $6.8 trillion/year (91%) at a present value capital cost of ∼$73 trillion. WWS energy creates 28.6 million more long-term, full-time jobs than BAU energy and needs only ∼0.17% and ∼0.48% of land for new footprint and spacing, respectively. Thus, WWS requires less energy, costs less, and creates more jobs than does BAU.

Abstract This study explores various scenarios and flexibility mechanisms to integrate high penetrations of renewable energy into the US (United States) power grid. A linear programming model – POWER (Power system Optimization With diverse Energy Resources) – is constructed and used to (1) quantify flexibility cost-benefits of geographic aggregation, renewable overgeneration, storage, and flexible electric vehicle charging, and (2) compare pathways to a fully renewable electricity system. Geographic aggregation provides the largest flexibility benefit with ∼5–50% cost savings, but each region's contribution to the aggregate RPS (renewable portfolio standard) target is disproportionate, suggesting the need for regional-and-resource-specific RPS targets. Electric vehicle charging yields a lower levelized system cost, revealing the benefits of demand-side flexibility. However, existing demand response price structures may need adjustment to encourage optimal flexible load in highly renewable systems. Two scenarios with RPS targets from 20% to 100% for the US (peak load ∼729 GW) and California (peak load ∼62 GW) find each RPS target feasible from a planning perspective, but with 2× the cost and 3× the overgeneration at a 100% versus 80% RPS target. Emission reduction cost savings for the aggregated US system with an 80% versus 20% RPS target are roughly $200 billion/year, outweighing the $80 billion/year cost for the same RPS range.