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Delegates are invited to meet and discuss with the poster presenters in this topic directly after the session 'How does the wind blow behind wind turbines and in wind farms?' taking place on Tuesday, 11 March 2014 at 16:30-18:00. The meet-the-authors will take place in the poster area.

Jaclyn Frank AWS Truepower, United States of America
Co-authors:
Jaclyn Frank (1) F P Ken Pennock (1) John Daniel (2) Eduardo Ibanez (3) José Vidal (1)
(1) AWS Truepower, Albany, United States of America (2) ABB Inc., Raleigh, United States (3) NREL, Boulder, United States of America

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Presenter's biography

Biographies are supplied directly by presenters at EWEA 2014 and are published here unedited

As a senior research scientist at AWS Truepower in Albany, NY, Jaclyn Frank has served as the technical lead on several wind and solar integration studies in North America. With degrees in meteorology and atmospheric sciences and five years experience in the renewable energy field, her main focus has been the mesoscale modeling, resource to power conversion, and validation of data sets used in grid integration studies. In addition, she has helped develop methods to simulate the wind and solar resource on a 1-second timescale.

Abstract

Toward 20% wind energy by 2030: site selection, resource, and production modeling for the United States National Offshore Wind Energy Grid Interconnection Study

Introduction

The United States Department of Energy (DOE)-funded National Offshore Wind Energy Grid Interconnection Study (NOWEGIS) is a collaborative effort between ABB, the National Renewable Energy Laboratory (NREL), AWS Truepower, and Duke Energy. A goal of this study is to understand how the costs of energy and deployment timelines can be reduced to reach the DOE’s 20% Wind Energy by 2030 [1]. This paper will discuss the development of a robust production profile data set, including the site selection process, modeling the wind resource, and synthesizing realistic production profiles for over 54 GW of potential offshore wind energy.

Approach

This study builds upon previous work such as the Eastern Wind Integration and Transmission Study [2], the Renewable Energy Deployment System [ReEDS; 3], the Bureau of Ocean Energy Management (BOEM) wind energy areas, and the Michigan Great Lakes Wind Council Wind Resource Areas [4] to identify locations of future offshore wind farms. Areas in the Atlantic Ocean, Great Lakes, Gulf of Mexico, and the Pacific Ocean within 50 nautical miles from shore were considered. A GIS-based site selection algorithm was employed to exclude areas from development due to environmental, governmental, or technical constraints, and the best locations for future wind farms were selected based on cost of energy. Water depth was not a constraint, but a cost multiplier was applied to potential sites with depths greater than 30 m. The result was over 209 potential offshore wind sites. The wind resource at these areas was modeled with a mesoscale numerical weather prediction model at a 2-8 km horizontal and 10-minute temporal resolution for the years 2004-2006. Modeled wind speeds were validated with data from 16 elevated offshore monitoring platforms to ensure realistic results. Wind production profiles were then synthesized at each of the 209 sites using a composite turbine power curve composed of several commercially available turbines suitable for offshore development. The variability of individual and aggregated offshore wind sites was compared with that of onshore wind farms. Results help assess integration impacts and evaluate future deployment scenarios. The ongoing study is supervised by a technical review committee comprised of regional system operators, industry groups, and governmental entities.

Main body of abstract

NREL’s ReEDS model has been used to better understand the location and timing for the development of wind energy to fulfill the U.S. DOE’s 20% Wind Energy by 2030 goal. ReEDS is a generation and transmission capacity expansion model of the electrical grid of the United States. It estimates the cost of transmission expansion and operational integration of a wide variety of conventional and renewable generation as well as storage options and demand-side technologies. The resulting capacity buildout amounted to 304 GW of wind energy by 2030, including 54 GW of offshore wind. The ReEDS model also delineated the likely zones of development.

Based on the ReEDS development zones and targets, AWS Truepower invoked a GIS-based site selection algorithm to identify likely locations for offshore wind farms within these zones. AWS Truepower’s 200-m wind resource maps along with GIS layers from the National Oceanic and Atmospheric Administration, United States Department of Defense, Black and Veatch [5], and other sources were used to identify or exclude areas. When excluded areas conflicted with areas identified by the BOEM as potential wind energy areas, the BOEM areas superseded the exclusions. Care was taken to ensure that all areas currently being considered for offshore development were included in this study. In all, 209 sites in the Atlantic, Pacific, Gulf, and Great Lakes totaling more than 134 GW were identified. Sites generally ranged from 300-1000 MW and were located within 50 n mi from the coast. The sites were ranked by cost of energy based on predicted production and distance to tramsmission, and the lowest cost sites were retained, resulting in 54 GW of potential offshore wind sites. Preliminary results suggest that the cost of energy at these sites ranges from 171-258 dollars per megawatt hour.

A mesoscale numerical weather prediction model was then employed to model the wind speeds at the locations of the selected sites. The simulations spanned three years (2004-2006) and were run at 10-minute temporal resolution. Most areas were simulated at 2-km horizontal resolution, but areas where offshore wind was less likely due to wind resource and/or technical constraints such as Florida and the Pacific Coast were modeled at 8-km resolution. Diurnal and monthly mean patterns in modeled wind speeds were compared to measurements from elevated offshore platforms at 16 locations to ensure that the model provided realistic results.

A 7.5-MW composite power curve was constructed using several commercially available turbines. The composite turbine had a 165-m rotor diameter and was used at a 100-m hub height. With this composite power curve and an assumed density of 2.75 MW/km^2 (10x10 rotor diameter spacing), the modeled wind resource was converted to power at each of the selected sites. Standard electrical, wake, and availability losses were assumed, and high wind hysteresis was taken into account to estimate net power production. Ramp frequency distributions were examined on a 10-minute and hourly basis and compared to results from previous land-based wind studies.

The resulting 10-minute net power time series were used by the study team to better understand the variability and uncertainty in future offshore wind and to assess the applicability of current wind integration methods and deployment scenarios.


Conclusion

To this date, no utility-scale wind farms have been constructed offshore the United States. Yet most of the major population centers lie along the coast near high quality wind resource areas. This study is one of several to efforts of the United States Department of Energy to better understand the feasibility of offshore wind in the United States and remove barriers to its construction.

Offshore wind measurements are more difficult and costly to obtain than their onshore counterparts. In the absence of measurements, modeled data sets are useful to understand the impacts of integrating large amounts of wind into the electrical grid. This paper describes the process of developing such data sets and ensuring that they are realistic representations of production from future offshore wind farms. A GIS-based site selection algorithm was used to identify and locate 54+ GW of potential offshore wind sites corresponding to the ReEDS model wind buildout scenarios for 2030. A numerical weather prediction model was used to provide synthetic time series of the wind resource at these locations, which were validated with data from elevated offshore monitoring stations and then converted to net power profiles.

The resulting data sets were used to facilitate further study into how increasing levels of wind energy can be better integrated into the United States electrical grid. The data are also useful to help understand the costs of integrating offshore wind, the impacts on reserves, and the potential reduction and emissions and how those costs might be reduced by technology or infrastructure improvements.



Learning objectives
This paper will discuss some of the offshore wind initiatives in the United States. The audience will learn about the site selection, resource modeling, and power conversion methodologies employed for a large national study aimed at understanding how to integrate more offshore wind power into the United States electrical grid.


References
[1] U.S. Department of Energy, 20% Wind Energy by 2030: Increasing Wind Energy’s Contribution to U.S. Electricity Supply, Washington, DC: July 2008, www.20percentwind.org/20percent_wind_energy_report_revOct08.pdf
[2] M. Brower, Development of Eastern Regional Wind Resource and Wind Plant Output Datasets, NREL/SR-550-46764, Golden, CO: National Renewable Energy Laboratory, 2009, http://www.nrel.gov/electricity/transmission/pdfs/aws_truewind_final_report.pdf.
[3] W. Short et al., "Regional Energy Deployment System,” NREL/TP-6A20-46534, Golden, CO: National Renewable Energy Laboratory, 2011, 94 pp., www.nrel.gov/docs/fy12osti/46534.pdf.
[4] Michigan Great Lakes Wind Council, “Wind Resource Areas,” 2010, 9 pp., http://www.michiganglowcouncil.org/WRA%20Brochure_Dec2010_web.pdf
[5] Black & Veatch, Technology Characterization for Renewable Energy Electricity Futures Study: GIS Database of Offshore Wind Resource Competing Uses and Environmentally Sensitive Areas, Overland Park, KS: 2010.