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This study explores various scenarios and flexibility mechanisms to integrate high penetrations of renewable energy into the United States (US) power grid. A linear programming model-POWER-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 renewable portfolio standard (RPS) 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 ~729GW) and California (peak load ~62GW) find each RPS target feasible from a planning perspective, but with 2x the cost and 3x 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.

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This 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.

The purpose of this two-part study is to model the effects of large penetrations of offshore wind power into a large electric system using realistic wind power forecast errors and a complete model of unit commitment, economic dispatch, and power flow. The chosen electric system is PJM Interconnection, one of the largest independent system operators in the U.S. with a generation capacity of 186 Gigawatts (GW). The offshore wind resource along the U.S. East Coast is modeled at five build-out levels, varying between 7 and 70 GW of installed capacity, considering exclusion zones and conflicting water uses. This paper, Part I of the study, describes in detail the wind forecast error model; the accompanying Part II describes the modeling of PJM's sequencing of decisions and information, inclusive of day-ahead, hour-ahead, and real-time commitments to energy generators with the Smart-ISO simulator and discusses the results. Wind forecasts are generated with the Weather Research and Forecasting (WRF) model, initialized every day at local noon and run for 48 hours to provide midnight-to-midnight forecasts for one month per season. Due to the lack of offshore wind speed observations at hub height along the East Coast, a stochastic forecast error model for the offshore winds is constructed based on forecast errors at 23 existing PJM onshore wind farms. PJM uses an advanced, WRF-based forecast system with continuous wind farm data assimilation. The implicit (and conservative) assumption here is that the future forecast

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1] 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. Citation: Dvorak, M.

[1] Wind energy represents the nearest term cost-effective renewable energy source. While efforts have been made to assess wind energy potential over land around the world, offshore wind energy resources are largely unexplored, in part because these regions have relatively sparse wind observations. In this study, the wind energy potential offshore of the California coast is evaluated using a welltested high-resolution numerical model dataset. We found that along the coastline, the low-level winds exhibit strong spatial variation and are characterized by alternating windspeed maxima and minima near coastal promontories associated with the interaction between the marine boundary layer and coastal topography. Further analysis highlights the enormous and reliable wind energy development potential in these persistent offshore windspeed maxima.