Stephen Rose

Quantifying the hurricane risk to offshore wind turbines

The U.S. Department of Energy has estimated that if the United States is to generate 20% of its electricity from wind, over 50 GW will be required from shallow offshore turbines. Hurricanes are a potential risk to these turbines. Turbine tower buckling has been observed in typhoons, but no offshore wind turbines have yet been built in the United States. We present a probabilistic model to estimate the number of turbines that would be destroyed by hurricanes in an offshore wind farm. We apply this model to estimate the risk to offshore wind farms in four representative locations in the Atlantic and Gulf Coastal waters of the United States. In the most vulnerable areas now being actively considered by developers, nearly half the turbines in a farm are likely to be destroyed in a 20-y period. Reasonable mitigation measures—increasing the design reference wind load, ensuring that the nacelle can be turned into rapidly changing winds, and building most wind plants in the areas with lower risk—can greatly enhance the probability that offshore wind can help to meet the United States’ electricity needs.

Spatial stochastic modeling of sedimentary formations to assess CO2 storage potential.

Carbon capture and sequestration (CCS) is a technology that provides a near-term solution to reduce anthropogenic CO2 emissions to the atmosphere and reduce our impact on the climate system. Assessments of carbon sequestration resources that have been made for North America using existing methodologies likely underestimate uncertainty and variability in the reservoir parameters. This paper describes a geostatistical model developed to estimate the CO2 storage resource in sedimentary formations. The proposed stochastic model accounts for the spatial distribution of reservoir properties and is implemented in a case study of the Oriskany Formation of the Appalachian sedimentary basin. Results indicate that the CO2 storage resource for the Pennsylvania part of the Oriskany Formation has substantial spatial variation due to heterogeneity of formation properties and basin geology leading to significant uncertainty in the storage assessment. The Oriskany Formation sequestration resource estimate in Pennsylvania calculated with the effective efficiency factor, E=5%, ranges from 0.15 to 1.01 gigatonnes (Gt) with a mean value of 0.52 Gt of CO2 (E=5%). The methodology is generalizable to other sedimentary formations in which site-specific trend analyses and statistical models are developed to estimate the CO2 sequestration storage capacity and its uncertainty. More precise CO2 storage resource estimates will provide better recommendations for government and industry leaders and inform their decisions on which greenhouse gas mitigation measures are best fit for their regions.

Is it always windy somewhere? Occurrence of low-wind-power events over large areas

The incidence of widespread low-wind conditions is important to the reliability and economics of electric grids with large amounts of wind power. In order to investigate a future in which wind plants are geographically widespread but interconnected, we examine how frequently low generation levels occur for wind power aggregated from distant, weakly-correlated wind generators. We simulate the wind power using anemometer data from nine tall-tower sites spanning the contiguous United States. The number of low-power hours per year declines exponentially with the number of sites being aggregated. Hours with power levels below 5% of total capacity, for example, drop by a factor of about 60, from 2140 h/y for the median single site to 36 h/y for the generation aggregated from all nine sites; the standard deviation drops by a factor of 3. The systematic dependence of generation-level probability distribution “tails” on both number and power threshold is well described by the theory of Large Deviations. Combining this theory for tail behavior with the normal distribution for behavior near the mean allows us to estimate, without the use of any adjustable parameters, the entire generation duration curve as a function of the number of essentially independent sites in the array.

Quantifying the Hurricane Catastrophe Risk to Offshore Wind Power

The U.S. Department of Energy has estimated that over 50 GW of offshore wind power will be required for the United States to generate 20% of its electricity from wind. Developers are actively planning offshore wind farms along the U.S. Atlantic and Gulf coasts and several leases have been signed for offshore sites. These planned projects are in areas that are sometimes struck by hurricanes. We present a method to estimate the catastrophe risk to offshore wind power using simulated hurricanes. Using this method, we estimate the fraction of offshore wind power simultaneously offline and the cumulative damage in a region. In Texas, the most vulnerable region we studied, 10% of offshore wind power could be offline simultaneously because of hurricane damage with a 100-year return period and 6% could be destroyed in any 10-year period. We also estimate the risks to single wind farms in four representative locations; we find the risks are significant but lower than those estimated in previously published results. Much of the hurricane risk to offshore wind turbines can be mitigated by designing turbines for higher maximum wind speeds, ensuring that turbine nacelles can turn quickly to track the wind direction even when grid power is lost, and building in areas with lower risk.

Reply to Powell and Cocke: On the probability of catastrophic damage to offshore wind farms from hurricanes in the US Gulf Coast

As a result of Powell and Cocke’s letter (1) regarding our paper on the hurricane risk to offshore wind turbines (2), we have reviewed and updated some of our analysis. However, our conclusion remains that wind turbines in hurricane-prone areas face extreme wind conditions significantly different from those for which offshore wind turbines are currently designed; some of this risk can be mitigated through engineered mechanisms: [↵][1]1To whom correspondence should be addressed. E-mail: paulina{at}cmu.edu. [1]: #xref-corresp-1-1

Reduction of wind power variability through geographic diversity

The variability of wind-generated electricity can be reduced by aggregating the outputs of wind generation plants spread over a large geographic area. In this chapter we utilize Monte Carlo simulations to investigate upper bounds on the degree of achievable smoothing and clarify how the degree of smoothing depends on the number of plants and on the size of the geographic area over which they are spread. We model two distinct benefits of geographic diversity that have different behaviors: (1) increased tendency of generation level to lie near its mean and (2) decreased tendency for it to lie near its extremes. Gaussian or normal probability distributions give accurate estimates of the first but underestimate the second. The second benefit, which has particular importance for electric grid reliability, has not been widely treated before. The effect of geographic diversity on wind generation variability has been investigated by many, starting with Thomas (1945). Significant early work was carried out by Molly (1977), Justus and Mikhail (1978), Kahn (1979), Farmer, Newman, and Ashmole (1980), and Carlin and Haslett (1982). Some researchers have focused on particular geographic regions, including the U.S. Midwest (Archer and Jacobson, 2003, 2007; Fisher et al., 2013) and the Nordic countries (Holttinen, 2005). Others have investigated the effect on the frequency spectrum of the generated power (McNerney and Richardson, 1992; Beyer, Luther, and Steinberger-Willms, 1993; Nanahara et al., 2004; Katzenstein, Fertig, and Apt, 2010; Tarroja et al., 2011). Hasche (2010) has modeled how the smoothing benefit saturates as the number of generator sites within a region increases. Some recent investigations have focused on the effects of spreading arrays of wind generators over especially large distances. Kempton et al. (2010) and Dvorak et al. (2012) considered an array of wind farmsExecutive Summary Part 1: Technical and Policy Options 1. Overview 2. Variability and its Prediction 3. Strategies to Reduce or Manage Wind and Solar Variability 4. Improved Planning for Renewable Energy Capacity Expansion 5. New Regulations, Rate Structures, and Standards to Support Variable Energy Resources 6. Policies to Manage Variable Generation at Increased Renewable Contribution Part 2: Scientific Findings 7. The Social Costs and Benefits of Wind Energy: Case Study in the PJM Interconnection 8. Characteristics of Wind, Solar Photovoltaic, and Solar Thermal Power 9. Forecast Error Characteristics of Wind and of Load 10. Day-Ahead Wind Reserve Requirements 11. Year-To-Year Variability in Wind Power 12. Reduction of Wind Power Variability through Geographic Diversity 13. Cycling and Ramping of Fossil Plants, and Reduced Energy Payments 14. Storage to Smooth Variability A. Small-Scale Storage B. Economics of Grid Storage: i. Storage in the Largest Electricity Market, PJM ii. Pumped Hydroelectric Storage in Portugal and Norway iii. Compressed Air Energy Storage iv. Plug-In Hybrid Electric Vehicle (PHEV) Storage v. Smart PHEV Charging 15. The Cost-Effectiveness of Dynamically Limiting Wind Turbines for Secondary Frequency Regulation Capacity 16. Quantifying the Hurricane Risk to Offshore Wind Power Part 3: Review of Large-Scale Wind Integration Studies 17. A Critical Review of Large-Scale Wind Integration Studies in the United States