Wind power variability and power system reserves in South Africa
DOI:
https://doi.org/10.17159/2413-3051/2018/v29i1a2067Keywords:
variable generation, forecast errors, ramp rates, power curve estimation, fluctuationsAbstract
Variable renewable generation, primarily from wind and solar, introduces new uncertainties in the operation of power systems. This paper describes and applies a method to quantify how wind power development will affect the use of short-term automatic reserves in the future South African power system. The study uses a scenario for wind power development in South Africa, based on information from the South African transmission system operator (Eskom) and the Department of Energy. The scenario foresees 5% wind power penetration by 2025. Time series for wind power production and forecasts are simulated, and the duration curves for wind power ramp rates and wind power forecast errors are applied to assess the use of reserves due to wind power variability. The main finding is that the 5% wind power penetration in 2025 will increase the use of short-term automatic reserves by approximately 2%.
Downloads
References
State of renewable energy in South Africa. De-partment of Energy, South Africa, 2015. http://www.gov.za/sites/www.gov.za/files/State%20of%20Renewable%20Energy%20in%20 South%20Africa_s.pdf .
Electricity generated and available for distribution (Preliminary). Statistical release P4141. Statistics South Africa, February 2015. http://www.statssa. gov.za/publications/P4141/P4141February2015. pdf.
Kristoffersen, J. R. The Horns Rev wind farm and the operational experience with the wind farm main controller. Offshore Wind International Conference, Copenhagen (2005).
Akhmatov, V., Abildgaard, H., Pedersen, J., Eriksen, P.B. Integration of offshore wind power into the Western Danish power system. Offshore Wind International Conference, Copenhagen (2005).
Sørensen, P. E., Cutululis, N. A., Vigueras-Rodriguez, A., Madsen, H., Pinson, P., Jensen, L., Hjerrild, J., Donovan, M. (2008). Modelling of power fluctuations from large offshore wind farms. Wind Energy, 11(1), 29-43. DOI: 10.1002/we.246. http://onlinelibrary.wiley.com/ doi/10.1002/we.246/abstract.
Sørensen, P. E., Cutululis, N. A., Vigueras-Rodriguez, A., Jensen, L.E., Hjerrild, J., Do-novan, M., Madsen, H. Power Fluctuations From Large Wind farms. IEEE Transactions on Power Systems Vol 22 (3) (2007). http://ieeexplore.ieee.org/document/4282056/.
Zavadil R. et. al.. Final Report - 2006 Minnesota Wind Integration Study Volume I. Nov. 2006. EnerNex Corporation. https://www.leg.state.mn. us/edocs/edocs?oclcnumber=80967997.
Meibom, P., Barth, R., Brand, H., Hasche, B., Swider, D., Ravn, H., & Weber, C. (2008). All is-land grid study. Wind variability management studies. Dublin: Department of Enterprise, Trade and Investment. (Workstream 2B). http://www. eirgridgroup.com/site-files/library/EirGrid/ Workstream%202B.pdf.
Wind Aatlas for South Africa – http://www. wasaproject.info/ and http://wasa.csir.co.za/.
Sørensen, P. and Cutululis, N.A. Wind farms’ spatial distribution effect on power system re-serves requirements. 2010 IEEE International Symposium on Industrial Electronic (ISIE), 2010, Bari (IT), 4-7 July. http://ieeexplore.ieee.org/ document/5636304/. DOI: 10.1109/ISIE.2010. 5636304.
Hahmann A.N., Lennard C., Argent B., Badger J., Vincent C.L., Kelly M.C., Volker P.J.H., Refslund J., 2014b. Mesoscale modeling for the wind atlas for South Africa (WASA) Project. Technical Report, DTU Wind Energy, Roskilde, Denmark. http://orbit.dtu.dk/en/publications/ mesoscale-modeling-for-the-wind-atlas-of-south-africa-wasa-project(8d6c8668-c38d-45a9-8640-18e069bf2c97).html.
Hahmann, A.N., Rostkier-Edelstein, D., Warner, T. T., Liu, Y., Vandenberg, F., Babarsky, R. and Swerdlin, S. P. A reanalysis system for the gener-ation of mesoscale climatographies. Journal of Applied Meteorology and Climatology, 2010: 954-972. DOI: 10.1175/2009JAMC2351.1.
Wang, W., Bruyere, C., Duda, M., Dudhia, J., Gill, D., H-C. Lin, H-C., Michaelakes, J., Rizvi, S. and Zhang, X. WRF-ARW Version 3 Modeling System User’s Guide. Mesoscale & Microscale Meteorology Division, National Center for At-mospheric Research, Boulder, USA, 2009. http://www2.mmm.ucar.edu/wrf/users/docs/ us-er_guide_V3.7/ARWUsersGuideV3.7.pdf.
Mellor, G. L. and Yamada, T. Development of a turbulence closure model for geophysical fluid problems, Reviews of Geophysics and Space Physics, 1982: 851-875. http://www.fap.if. usp.br/~hbarbosa/uploads/Teaching/Modclim2010a/MellorYamada1982.pdf.
Hahmann, A.N., Vincent, C.L., Peña, A., Lange, J. and Hasager, C.B., Wind climate estimation us-ing WRF model output: method and model sen-sitivities over the sea. International Journal of Climatology, 2014. DOI: 10.1002/joc.4217.
Dee, D.P. et al., The ERA-Interim reanalysis: con-figuration and performance of the data assimila-tion system. Quarterly Journal of the Royal Me-teorological Society 2011: 553–597. DOI: 10.1002/qj.828.
Reynolds, R. W., Rayner, N. A., Smith, T. M., Stokes, D. C. and Wang, W. Q. An improved in situ and satellite SST analysis for climate. Journal of Climate, 2002: 1609–1625 DOI: 10.1175/1520-0442.
IEC 61400-12-1. Wind turbines – Power perfor-mance measurements of electricity producing wind turbines. Ed. 1.0. IEC Geneva 2017. https://webstore.iec.ch/publication/26603.
Larsén, X., Ott, S., Badger, J., Hahmann, A. and Mann, J. Recipes for correcting the impact of ef-fective mesoscale resolution on the estimation of extreme winds. American Meteorology Society, 2012: 521–533. DOI: http://dx.doi.org/10.1175/JAMC-D-11-090.1.
TWENTIES project. Final Report. October 2013. https://windeurope.org/fileadmin/files/library/ publications/reports/Twenties.pdf.
Cutululis, N., Altiparmakis, A., Litong-Palima, M., Detlefsen, N. and Sørensen, P. Market and sys-tem security impact of the storm demonstration in task-forces TF2. TWENTIES Deliverable D16.6. 2013. http://orbit.dtu.dk/ en/publications/ id(f725d379-984e-4693-81c1-4be7b7cdd365).html.
Söder, L. Simulation of wind speed forecast er-rors for operation planning of multiarea power systems. International Conference on Probabilis-tic Methods Applied to Power Systems, Ames (2004). http://ieeexplore.ieee.org/ document/ 1378776/.
Sørensen, P., Hansen A., and Rosas, P. Wind models for simulation of power fluctuations from wind farms. Journal of Wind Engineering & In-dustrial Aerodynamics, 2002: 1381-1402. http://www.sciencedirect.com/science/article/ pii/S016761050200260X.
WRF wind power forecasts for South Africa. http://veaonline.risoe.dk/wasa/.
Larsen, M. F., Kelley, M. C. and Gage, K. S. Tur-bulence spectra in the upper troposphere and lower stratosphere at periods between 2 hours and 40 days. Journal of the Atmospheric Scienc-es, 1982: 1035–1041. http://journals.ametsoc. org/doi/abs/10.1175/1520-0469(1982) 039%3C1035%3ATSITUT%3E2.0.CO%3B2.
Integrated resource plan for electricity 2010-2030 – Revision 2. Final report. South Africa Department of Energy. March 2011. http://www.energy.gov.za/IRP/irp%20files/IRP2010_2030_Final_Report_20110325.pdf.
Ancillary services technical requirements 2015/16 – 2019/20. Rev. 1 ESKOM 28-10-2014. http://www.eskom.co.za/Whatweredoing/AncilliaryServices/Documents/ TechReq2015.docx.pdf.
Downloads
Published
Issue
Section
License
Copyright (c) 2018 Poul Sorensen, Marisciel Litong-Palima, Andrea N. Hahmann, Schalk Heunis, Marathon Ntusi, Jens Carsten Hansen
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.