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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