Wind Power Analysis Using Non-Standard Statistical Models

1. Wind Power Analysis Using Non-Standard Statistical Models Niall McCoy School of Electrical Systems Engineering Prof Jonathan Blackledge 15th February 2013

2. Introduction • Name: • Niall McCoy. • Qualifications: • Degree in Electrical Engineering 2008 (DIT); • Degree in Energy Management 2010 (DIT); • Chartered & Professional Engineer 2012 (EI). • Company & Position: • Electrical Engineer of Wind Prospect Group, based in Carrickmines, Co Dublin. • Roles & responsibilities: • International Project Management & Electrical Design. • Academic Works: • Commenced Part-Time PhD with DIT October 2011; • Published one academic paper to date in December 2012 in association with Prof Jonathan Blackledge; “Analysis of Wind Velocity and the Quantification of Wind Turbulence in Rural and Urban Environments using the Levy Index and Fractal Dimension - 2012”

3. Agenda • Project Background • Research Methodologies • Current Industry Standards • The Urban vs Rural Resource • Next Steps

4. Project Background • Project Background • Research Methodologies • Current Industry Standards • The Urban vs Rural Resource • Next Steps

5. Why is Wind Energy Analysis Important? Currently circa 2,158MW of installed wind generation on the system. Current energy demand of 35,532GWh¹. Target of 40% system demand to be via renewable energy by 2020, 35% of which to be wind. Resulting in a requirement of circa 39,852GWh (SD in 2020)² where 35% must be sourced from wind generation. Applying capacity factors of 0.31, the required installed capacity for wind generation by 2020 is 5,178MW¹. ¹ EirGrid 2013 ² Wind Prospect 2013 Le Tene Maps 2013

6. Why is Wind Energy Analysis Important? Main reason – Wind Farm Development & Financial Risk; • Wind farm developments require capital investment to be developed; • Financial return is directly linked to the wind speeds at the site; • Financial risk is amplified in the energy prediction due to the relationship between turbine output and wind speed; • To avoid financial disadvantages, uncertainties must be minimised in: • Wind resource assessment • Power curve performance (Turbine output at specific wind speed) • Turbine availability

7. What has Financial Risk to do with Wind Energy Analysis? • To have confidence in an investment, you need confidence in the wind resource and associated studies; • Wind studies are performed to understand that return on your investment; • All current wind studies carry a degree of uncertainty and potential for error. All stages of the wind study aim to minimise uncertainty, resulting in a “best guess”; • Therefore the Aim of the Project • Design a more accurate model of wind energy analysis with reduced errors; • Provide a reduced risk profile to investors; • Increase funding access to wind projects and increase wind energy penetration on the Irish system.

8. Research Methodologies • Project Background • Research Methodologies • Current Industry Standards • The Urban vs Rural Resource • Next Steps

9. Non-Standard Statistical Models Betz’s Law – Windpower.de 1999 Illustration of Betz’s Law – Windpower.de 1999 • In order to find a more accurate forecasting model for wind energy at a potential wind farm location are number of equations have been looked at; • Non-Gaussian model for simulating wind velocity data; • Levy distribution for the statistical characteristics of wind velocity; • Thus, deriving a stochastic fractional diffusion equation for the wind velocity as a function of time whose solution is characterised by the Levy index; • Eventually deriving both to establish Levy index using Betz law to understand the energy output of a specific turbine. http://eleceng.dit.ie/blackledge/index.php?uid=516&page=publications

10. Current Industry Standards • Project Background • Research Methodologies • Current Industry Standards • The Urban vs Rural Resource • Next Steps

11. Current Measurement Systems Met Tower Measurement System WTG Measurement System NRG System 2010 Vestas 2013

12. Power Law vs Log Law Profiles • Power law profile: • α = wind shear coefficient • Log law profile • Requires knowledge of u* and z0 • Both must be estimated

13. Data Sources & Sets • Fully calibrated industry standard anemometers; • 10 minute average data set from 80m metrological mast, with cup anemometers located at heights of, 50m, 65m, 80m & 82.5m;

14. The Urban vs Rural Resource • Project Background • Research Methodologies • Current Industry Standards • The Urban vs Rural Resource • Next Steps

15. The Urban vs Rural Resource – u(z) denotes the wind speed at height z – u*friction velocity – κ the Von Karman constant – z height above the earth’s surface – d displacement height – z0 height above the earth’s surface roughness Mertens 2006

16. The Urban vs Rural Resource • Main Aim of the paper; • To quantify rural and urban areas in terms of the Levy index using data generated from industry standard sources; • The emphasise is based on a theoretical basis, where; • gamma= 1 for 'perfect' urban area (i.e. full diffusion) • and = 2 for 'perfect' rural area (i.e. perfect laminar flow). • In practice, 'perfect' never exists but the differences in gamma for the two environments appears to reflect the hypothesis. Greenspec 2011

17. Non-Gaussian results of the Urban & Rural Wind Resource Five rural and five urban sites were analysed through determination of the Levy index over a period of 12 months. The Table show that, bar one anomaly, the trend is that the mean values of the Levy index for the rural sites is consistently higher in comparison to the mean values of the same index for the urban sites. Resulting in the urban-to-rural ratio of 0.9832. Levy index using data generated from industry standard sources

18. Urban vs Rural the Conclusion • In conclusion, it can be stated that the wind resource in the urban environment is curtailed due to the influencing factors such a surface roughness, turbulence intensity, etc... • When a direct comparison is drawn between the urban and rural wind resources at selected location across Ireland and the UK, using similar reference heights, fully calibrated equipment and stochastic models to define the results. It is evident that the rural resource is generally of a higher energy yield when compared to the urban resource. • For the full paper see - http://users.jyu.fi/~timoh/isast2012.pdf http://www.isastorganization.org/index.html

19. Next Steps • Project Background • Research Methodologies • Current Industry Standards • The Urban vs Rural Resource • Next Steps

20. Next Steps Wind Pro & WaSP model Conceptual Non-Gaussian CFD Model • Detailed look at developing a non-Gaussian based energy yield platform model and possibly CFD software; • Challenge current industry energy analysis model accuracy, such as Wind Pro & WaSP, with newly development model/software; • Introduce more complex influences, such as specific types of surface roughness, turbulence intensity, etc..;

21. Q&A Any Questions?