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Risk assessment of wind droughts over India


Affiliations
1 Divecha Centre for Climate Change, Indian Institute of Science, Bengaluru 560 012, India
2 Blackett Laboratory, Department of Physics, Imperial College, London SW7 2AZ, United Kingdom; Department of Physics and Grantham Institute – Climate Change and Environment, Imperial College, Exhibition Rd, South Kensington, London SW7 2BU,, United Kingdom
3 Blackett Laboratory, Department of Physics, Imperial College, London SW7 2AZ, United Kingdom; Department of Physics and Grantham Institute – Climate Change and Environment, Imperial College, Exhibition Rd, South Kensington, London SW7 2BU, United Kingdom
4 Divecha Centre for Climate Change and Centre for Atmospheric and Oceanic Sciences, Indian Institute of Science, Bengaluru 560 012, India
 

Wind power growth makes it essential to simulate weather variability and its impacts on the electricity grid. Low-probability, high-impact weather events such as a wind drought are important but difficult to identify based on limited historical datasets. A stochastic weather generator, Imperial College Weather Generator (IMAGE), is employed to identify extreme events through long-period simulations. IMAGE captures mean, spatial correlation and seasonality in wind speed and estimates return periods of extreme wind events over India. Simulations show that when Rajasthan experiences wind drought, southern India continues to have wind, and vice versa. Regional grid-scale wind droughts could be avoided if grids are strongly interconnected across the country.

Keywords

Decarbonization, grid interconnections, risk assessment, stochastic weather generators, wind drought.
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  • Ministry of New and Renewable Energy, Annual Report 2018–19, Government of India, 2019; https://mnre.gov.in/img/documents/ uploads/file_f-1608040317211.pdf

  • Guttikunda, S. K. and Jawahar, P., Atmospheric emissions and pollution from the coal-fired thermal power plants in India. Atmos. Environ., 2014, 92, 449–460; http://www.indiaairquality.info/wpcontent/uploads/docs/2014-08-AE-Emissions-Health-Coal-PPs-India.pdf

  • Chaturvedi, R. K., Gangopadhyay, A., Seshadri, A. and Hiremath, M., Co-benefits of power sector decarbonisation for air quality and human health in India. Policy Brief, Divecha Centre for Climate Change, Indian Institute of Science, Bengaluru, 2018; http://dccc.iisc.ac.in/assets/pdf/Policy_Brief_January_2018.pdf

  • Staffell, I. and Pfenninger, S., The increasing impact of weather on electricity supply and demand. Energy, 2018, 145, 65–78; https://doi.org/10.1016/j.energy.2017.12.051.

  • Brayshaw, D. J., Troccoli, A., Fordham, R. and Methven, J., The impact of large scale atmospheric circulation patterns on wind power generation and its potential predictability: a case study over the UK. Renew. Energy, 2011, 36, 2087–2096; https://doi.org/10.1016/j.renene.2011.01.025.

  • Sparks, N. J., Hardwick, S. R., Schmid, M. and Toumi, R., IMAGE: a multivariate multisite stochastic weather generator for European weather and climate. Stoch. Environ. Res. Risk Assess., 2018, 32, 771–784; https://link.springer.com/article/10.1007/s00477-0171433-9

  • Parlange, M. B. and Katz, R. W., An extended version of the Richardson model for simulating daily weather variables. J. Appl. Meteorol., 2000, 39, 610–622; https://doi.org/10.1175/1520-045039.5.610.

  • Higham, N., Computing a nearest symmetric positive semidefinite matrix. Linear Algebra Appl., 1988, 103, 103–118; https://dx.doi.org/10.1016/0024-3795(88)90223-6.

  • ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate. Copernicus Climate Change Service Climate Data Store, 2020; https://cds.climate.copernicus.eu/cdsapp#!/home

  • Wind-turbine-models.com. Suzlon S.88-2100 power curve, 2020; https://en.wind-turbine-models.com/turbines/95-suzlon-s.88-2100#powercurve

  • Thomas, S. R., Nicolau, S., Martinez-Alvarado, O., Drew, D. J. and Bloomfield, H. C., How well do atmospheric reanalyses reproduce observed winds in coastal regions of Mexico? Meteorol. Appl., 2021, 28(5); https://doi.org/10.1002/met.2023.

  • Molina, M. O., Gutierrez, C. and Sanchez, E., Comparison of ERA5 surface wind speed climatologies over Europe with observations from the HadISD dataset. Int. J. Climatol., 2021, 41, 4864–4878; https://doi.org/10.1002/joc.7103.

  • Belmonte Rivas, M. and Stoffelen, A., Characterizing ERA-interim and ERA5 surface wind biases using ASCAT. Ocean Sci., 2019, 15, 831–852; https://doi.org/10.5194/os-15-831-2019.

  • Mathew, S., Wind Energy, Fundamentals, Resource Analysis and Economics, Springer, 2016; ISBN-103-540-30905-5.

  • Cowden, J. R., Watkins, D. W. and Mihelcic, J. R., Stochastic rainfall modeling in West Africa: parsimonious approaches for domestic rainwater harvesting assessment. J. Hydrol., 2008, 361(1–2), 64–77; doi:10.1016/j.jhydrol.2008.07.025.

  • Supit, I., van Diepen, C. A., de Wit, A. J. W., Wolf, J., Kabat, P., Baruth, B. and Ludwig, F., Assessing climate change effects on European crop yields using the crop growth monitoring system and a weather generator. Agric. For. Meteorol., 2012, 164, 96–111; doi:10.1016/j.agrformet.2012.05.005.


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  • Risk assessment of wind droughts over India

Abstract Views: 383  |  PDF Views: 144

Authors

A. Gangopadhyay
Divecha Centre for Climate Change, Indian Institute of Science, Bengaluru 560 012, India
N. J. Sparks
Blackett Laboratory, Department of Physics, Imperial College, London SW7 2AZ, United Kingdom; Department of Physics and Grantham Institute – Climate Change and Environment, Imperial College, Exhibition Rd, South Kensington, London SW7 2BU,, United Kingdom
R. Toumi
Blackett Laboratory, Department of Physics, Imperial College, London SW7 2AZ, United Kingdom; Department of Physics and Grantham Institute – Climate Change and Environment, Imperial College, Exhibition Rd, South Kensington, London SW7 2BU, United Kingdom
A. K. Seshadri
Divecha Centre for Climate Change and Centre for Atmospheric and Oceanic Sciences, Indian Institute of Science, Bengaluru 560 012, India

Abstract


Wind power growth makes it essential to simulate weather variability and its impacts on the electricity grid. Low-probability, high-impact weather events such as a wind drought are important but difficult to identify based on limited historical datasets. A stochastic weather generator, Imperial College Weather Generator (IMAGE), is employed to identify extreme events through long-period simulations. IMAGE captures mean, spatial correlation and seasonality in wind speed and estimates return periods of extreme wind events over India. Simulations show that when Rajasthan experiences wind drought, southern India continues to have wind, and vice versa. Regional grid-scale wind droughts could be avoided if grids are strongly interconnected across the country.

Keywords


Decarbonization, grid interconnections, risk assessment, stochastic weather generators, wind drought.

References





DOI: https://doi.org/10.18520/cs%2Fv122%2Fi10%2F1145-1153