Characterization of Short-Term Wind Power Variations and Estimation of Reserve Requirements for High Wind Generation Shares

Mohammed Saber Eltohamy, Mohammed Said Abdel Moteleb, Hossam E. A. Talaat, S. F. Mekhamer, Walid A. Omran


The need to deal with variability in wind power output is one of the greatest challenges connected with adopting a considerable amount of wind power into power grids. Power system operators need to acquire more information on this variability, which can be utilized in the mitigation of high ramping events, especially when these events synchronize with a large error in the prediction, ensuring flexibility and reliability in the power system besides the economic considerations. The paper analyses short-term variability in output power using actual data obtained from aggregated wind farms from 2015 to 2020, where power ramping characteristics are described using a variety of measurements. The use of the standard deviation of short-term wind power variation as a reserve measure will be investigated in detail since there is no consensus about the ideal confidence level value as a multiplier of σ, which ranges from 3 to 6 times σ. The paper addresses how large this confidence level should be, as well as developing a data-driven approach for estimating this reserve with increasing wind shares and evaluating the proper distribution of short-term wind variation. The results illustrate that the stochastic variations in wind power can retain many of their characteristics from year to year, even when the share of wind capacity is raised.



Power system flexibility, Wind generation, Renewable energy, Ramp metrics, COVID-19 pandemic


C. D. Yue, C. C. Liu, C. C. Tu, and T. H. Lin, “Prediction of power generation by offshore wind farms using multiple data sources,” Energies, vol. 12, no. 4, pp. 1–24, 2019, doi: 10.3390/en12040700.

H. Díaz and C. Guedes Soares, “Review of the current status, technology and future trends of offshore wind farms,” Ocean Eng., vol. 209, no. April, p. 107381, 2020, doi: 10.1016/j.oceaneng.2020.107381.

International Renewable Energy Agency, “IRENA’s renewable energy statistics,” 2020. [Online]. Available:

J. Lee and F. Zhao, “Global Wind Report 2021,” 2021. [Online]. Available:

IRENA, “Post-COVID recovery: An agenda for resilience, development and equality,” 2020. [Online]. Available: /publications/2020/Jun/Post-COVID-Recovery.

W. P. J. Philippe, S. Zhang, S. Eftekharnejad, P. K. Ghosh, and P. K. Varshney, “Mixed Copula-Based Uncertainty Modeling of Hourly Wind Farm Production for Power System Operational Planning Studies,” IEEE Access, vol. 8, pp. 138569–138583, 2020, doi: 10.1109/ACCESS.2020.3012437.

M. S. Eltohamy, M. S. A. Moteleb, H. E. A. Talaat, S. F. Mekhamer, and W. A. Omran, “Overview of Power System Flexibility Options with Increasing Variable Renewable Generations,” in 6th International Conference on Advanced Control Circuits and Systems (ACCS) & 2019 5th International Conference on New Paradigms in Electronics & Information Technology (PEIT), 2019, pp. 280–292, doi: DOI: 10.1109/ACCS-PEIT48329.2019.9062836.

G. Limpens and H. Jeanmart, “Quantification of electricity storage needs for Belgium energy transition: A sensitivity analysis based on EROI,” ECOS 2018 - Proc. 31st Int. Conf. Effic. Cost, Optim. Simul. Environ. Impact Energy Syst., no. June, 2018.

M. Koivisto, K. Plakas, E. R. Hurtado Ellmann, N. Davis, and P. Sørensen, “Application of microscale wind and detailed wind power plant data in large-scale wind generation simulations,” Electr. Power Syst. Res., vol. 190, no. April 2020, p. 106638, 2021, doi: 10.1016/j.epsr.2020.106638.

G. Ren, J. Liu, J. Wan, Y. Guo, and D. Yu, “Overview of wind power intermittency : Impacts, measurements, and mitigation solutions,” Appl. Energy, vol. 204, pp. 47–65, 2017, doi: 10.1016/j.apenergy.2017.06.098.

B. R. Cheneka, S. J. Watson, and S. Basu, “A simple methodology to detect and quantify wind power ramps,” Wind Energy Sci., no. April, pp. 1–12, 2020, doi: 10.5194/wes-2020-64.

M. Anvari et al., “Short term fluctuations of wind and solar power systems,” New J. Phys., no. 18, 2016, doi: 10.1088/1367-2630/18/6/063027.

A. Upadhyay, B. Hu, J. Li, and L. Wu, “A chance-constrained wind range quantification approach to robust SCUC by determining dynamic uncertainty intervals,” CSEE J. Power Energy Syst., vol. 2, no. 1, pp. 54–64, 2016, doi: 10.17775/cseejpes.2016.00009.

G. Ren, J. Liu, J. Wan, F. Li, Y. Guo, and D. Yu, “The analysis of turbulence intensity based on wind speed data in onshore wind farms,” Renew. Energy, vol. 123, pp. 756–766, 2018, doi: 10.1016/j.renene.2018.02.080.

H. Valizadeh Haghi and S. Lotfifard, “Spatiotemporal modeling of wind generation for optimal energy storage sizing,” IEEE Trans. Sustain. Energy, vol. 6, no. 1, pp. 113–121, 2015, doi: 10.1109/TSTE.2014.2360702.

M. S. Eltohamy, M. S. A. Moteleb, H. E. A. Talaat, S. F. Mekhamer, and W. A. Omran, “Analyzing Wind Power Ramps for High Penetration of Variable Renewable Generation,” in 2019 21st International Middle East Power Systems Conference (MEPCON), Cairo, Egypt., 2019, pp. 768–775, doi: 10.1109/MEPCON47431.2019.9007951.

M. S. Eltohamy, M. S. A. Moteleb, H. E. A. Talaat, S. F. Mekhamer, and W. A. Omran, “Wind Power Ramps Analysis for High Shares of Variable Renewable Generation in Power Systems,” Indones. J. Electr. Eng. Informatics, vol. 8, no. 2, pp. 256–272, 2020, doi: 10.11591/ijeei.v8i2.1984.

M. S. Eltohamy, M. S. A. Moteleb, H. E. A. Talaat, S. F. Mekhamer, and W. A. Omran, “Power System Flexibility Metrics Evaluation and Power Ramping Analysis for High Variable Renewable Generation Shares,” EAI Endorsed Trans. Energy Web, vol. 8, no. 31, pp. 1–23, 2020, doi: 10.4108/eai.13-7-2018.165282.

M. S. Eltohamy, M. S. A. Moteleb, H. E. A. Talaat, S. F. Mekhamer, and W. Omran, “A Novel Approach for the Power Ramping Metrics,” Indones. J. Electr. Eng. Informatics, vol. 9, no. 2, pp. 313–333, 2021, doi: 10.52549/.v9i2.2612.

N. Pearre, K. Adye, and L. Swan, “Proportioning wind, solar, and in-stream tidal electricity generating capacity to co-optimize multiple grid integration metrics,” Appl. Energy, vol. 242, no. March, pp. 69–77, 2019, doi: 10.1016/j.apenergy.2019.03.073.

S. Han et al., “Quantitative evaluation method for the complementarity of wind-solar-hydro power and optimization of wind-solar ratio,” Appl. Energy, vol. 236, no. November 2018, pp. 973–984, 2019, doi: 10.1016/j.apenergy.2018.12.059.

J. P. Deane, G. Drayton, and B. P. Ó. Gallachóir, “The impact of sub-hourly modelling in power systems with significant levels of renewable generation q,” Appl. Energy, vol. 113, pp. 152–158, 2014, doi: 10.1016/j.apenergy.2013.07.027.

J. Kiviluoma, H. Holttinen, D. Weir, R. Scharff, and L. Söder, “Variability in large-scale wind power generation,” Wind Energy, vol. 18, no. 11, pp. 1649–1665, 2015, doi: 10.1002/we.

G. Ren, J. Wan, J. Liu, D. Yu, and L. Söder, “Analysis of wind power intermittency based on historical wind power data,” Energy, vol. 150, pp. 482–492, 2018, doi: 10.1016/

A. J. Deppe, W. A. Gallus, and E. S. Takle, “A WRF Ensemble for Improved Wind Speed Forecasts at Turbine Height,” Weather Forecast., vol. 28, pp. 212–228, 2013, doi: 10.1175/WAF-D-11-00112.1.

B. Huang, I. S. Member, V. Krishnan, I. Member, B. Hodge, and I. S. Member, “Analyzing the Impacts of Variable Renewable Resources on California Net-Load Ramp Events,” no. August, pp. 1–5, 2018.

F. Zhang, Y. Qiao, and Z. Lu, “Extreme wind power forecast error analysis considering its application in day-ahead reserve capacity planning,” IET Renew. Power Gener., vol. 12, no. 16, pp. 1923–1930, 2018, doi: 10.1049/iet-rpg.2018.5023.

W. Dong and S. Li, “Reliability sensitivity of wind power system considering correlation of forecast errors based on multivariate NSTPNT method,” Prot. Control Mod. Power Syst., vol. 6, no. 1, 2021, doi: 10.1186/s41601-021-00192-0.

A. A. Thatte and L. Xie, “A metric and market construct of inter-temporal flexibility in time-coupled economic dispatch,” IEEE Trans. Power Syst., vol. 31, no. 5, pp. 3437–3446, 2016, doi: 10.1109/TPWRS.2015.2495118.

A. Jonaitis et al., “Challenges of integrating wind power plants into the electric power system : Lithuanian case,” Renew. Sustain. Energy Rev., vol. 94, no. June, pp. 468–475, 2018, doi: 10.1016/j.rser.2018.06.032.

S. Goodarzi, H. N. Perera, and D. Bunn, “The impact of renewable energy forecast errors on imbalance volumes and electricity spot prices,” Energy Policy, vol. 134, no. October 2018, p. 110827, 2019, doi: 10.1016/j.enpol.2019.06.035.

L. Han, H. Jing, R. Zhang, and Z. Gao, “Wind power forecast based on improved Long Short Term Memory network,” Energy, vol. 189, no. xxxx, p. 116300, 2019, doi: 10.1016/

C. Otero-Casal, P. Patlakas, M. A. Prosper, G. Galanis, and G. Miguez-Macho, “Development of a High-Resolution Wind Forecast System Based on the WRF Model and a Hybrid,” Energies, vol. 12, no. 3050, pp. 1–19, 2019, doi: 10.3390/en12163050.

I. Würth et al., “Minute-scale forecasting of wind power—results from the collaborative workshop of IEA Wind task 32 and 36,” Energies, vol. 12, no. 4, 2019, doi: 10.3390/en12040712.

S. J. Wellby and N. A. Engerer, “Categorizing the meteorological origins of critical ramp events in collective photovoltaic array output,” J. Appl. Meteorol. Climatol., vol. 55, no. 6, pp. 1323–1344, 2016, doi: 10.1175/JAMC-D-15-0107.1.

L. Y. Musilek P, “Forecasting of wind ramp events—analysis of cold front detection.”

C. Gallego-Castillo, A. Cuerva-Tejero, and O. Lopez-Garcia, “A review on the recent history of wind power ramp forecasting,” Renew. Sustain. Energy Rev., vol. 52, pp. 1148–1157, 2015, doi: 10.1016/j.rser.2015.07.154.

M. Sherry and D. Rival, “Meteorological phenomena associated with wind-power ramps downwind of mountainous terrain,” J. Renew. Sustain. Energy, vol. 033101, no. 7, pp. 1–13, 2015, doi: 10.1063/1.4919021.

S. Watson, “Quantifying the variability of wind energy,” WIREs Energy Environ., vol. 3, pp. 330–342, 2014, doi: 10.1002/wene.95.

H. Huang, M. Zhou, and G. Li, “An Endogenous Approach to Quantifying the Wind Power Reserve,” IEEE Trans. Power Syst., vol. 35, no. 3, pp. 2431–2442, 2020, doi: 10.1109/TPWRS.2019.2954844.

C. Rahmann, A. Heinemann, and R. Torres, “Quantifying operating reserves with wind power : towards probabilistic – dynamic approaches,” vol. 10, pp. 366–373, 2016, doi: 10.1049/iet-gtd.2015.0538.

D.-C. Radu, “Strategies for Provision of Secondary Reserve Capacity to Balance Short-Term Fluctuations of Variable Renewable Energy,” KTH School of Industrial Engineering and Management, 2017.

S. M. M. Agah and D. Flynn, “Impact of modelling non-normality and stochastic dependence of variables on operating reserve determination of power systems with high penetration of wind power,” Electr. Power Energy Syst., vol. 97, no. November 2016, pp. 146–154, 2018, doi: 10.1016/j.ijepes.2017.11.002.

S. Collins et al., “Integrating short term variations of the power system into integrated energy system models : A methodological review,” Renew. Sustain. Energy Rev., vol. 76, no. July 2016, pp. 839–856, 2017, doi: 10.1016/j.rser.2017.03.090.

Elia group, “Methodology for the dimensioning of the aFRR needs,” 2020.

M. S. Eltohamy, H. E. A. Talaat, M. S. A. Moteleb, S. F. Mekhamer, and W. A. Omran, “A Probabilistic Methodology for Estimating Reserve Requirement and Optimizing its Components in Systems with High Wind Penetration,” IEEE Access, pp. 1–21, 2022, doi: 10.1109/ACCESS.2022.3211305.

S. Asiaban et al., “Wind and Solar Intermittency and the Associated Integration Challenges : A Comprehensive Review Including the Status in the Belgian Power System,” Energies, vol. 14, no. 9, pp. 1–41, 2021, doi: 10.3390/en14092630.

K. De Vos, J. Morbee, J. Driesen, and R. Belmans, “Impact of wind power on sizing and allocation of reserve requirements,” IET Renew. Power Gener., no. October 2012, pp. 1–9, 2013, doi: 10.1049/iet-rpg.2012.0085.

H. Holttinen et al., “Methodologies to Determine Operating Reserves due to Increased Wind Power,” IEEE Trans. Sustain. Energy, vol. 3, no. 4, 2013.

T. Ikegami, C. T. Urabe, T. Saitou, and K. Ogimoto, “Numerical definitions of wind power output fluctuations for power system operations,” Renew. Energy, vol. 115, pp. 6–15, 2018, doi: 10.1016/j.renene.2017.08.009.

I. Side et al., “Reserve requirements of wind power IEA WIND Task 25 Operating Reserves different in all systems – General definition,” 2011.

M. A. Matos and R. J. Bessa, “Setting the Operating Reserve Using Probabilistic Wind Power Forecasts,” IEEE Trans. POWER Syst., vol. 26, no. 2, pp. 594–603, 2011.

M. Black and G. Strbac, “Value of bulk energy storage for managing wind power fluctuations,” IEEE Trans. Energy Convers., vol. 22, no. 1, pp. 197–205, 2007, doi: 10.1109/TEC.2006.889619.

T. Boutsika and S. Santoso, “Quantifying Short-term Wind Power Variability,” pp. 1–7, 2011.

H. Holttinen, M. Milligan, B. Kirby, T. Acker, V. Neimane, and T. Molinski, “Using Standard Deviation as a Measure of Increased Operational Reserve Requirement for Wind Power,” Wind Eng., vol. 32, no. 4, pp. 355–377, 2008.

M. Milligan et al., “Operating Reserves and Wind Power Integration : An International Comparison,” in The 9th Annual International Workshop on Large-Scale Integration of Wind Power into Power Systems as well as on Transmission Networks for Offshore Wind Power Plants Conference Québec, Canada; October 18-19, 2010, 2010, no. October.

A. S. Brouwer, M. Van Den Broek, A. Seebregts, and A. Faaij, “Impacts of large-scale Intermittent Renewable Energy Sources on electricity systems, and how these can be modeled,” Renew. Sustain. Energy Rev., vol. 33, pp. 443–466, 2014, doi: 10.1016/j.rser.2014.01.076.

H. K. Ringkjøb, P. M. Haugan, P. Seljom, A. Lind, F. Wagner, and S. Mesfun, “Short-term solar and wind variability in long-term energy system models - A European case study,” Energy, vol. 209, p. 118377, 2020, doi: 10.1016/

E. Ibanez, I. Krad, and E. Ela, “A Systematic Comparison of Operating Reserve Methodologies,” 2014.

G. Bel, C. P. Connaughton, M. Toots, and M. M. Bandi, “Grid-scale fluctuations and forecast error in wind power,” New J. Phys., vol. 18, no. 2, 2016, doi: 10.1088/1367-2630/18/2/023015.

J. Shi, L. Wang, W. Lee, X. Cheng, and X. Zong, “Hybrid Energy Storage System ( HESS ) optimization enabling very short- term wind power generation scheduling based on output feature extraction,” Appl. Energy, vol. 256, no. September, p. 113915, 2019, doi: 10.1016/j.apenergy.2019.113915.

M. S. Silva Pinto, M. S. S. Pinto, O. R. Saavedra, and O. R. Saavedra, “Power reserve dispatch to mitigate variability of generation output due to wind ramps,” 2020 IEEE PES Transm. Distrib. Conf. Exhib. - Lat. Am. T D LA 2020, 2020, doi: 10.1109/TDLA47668.2020.9326195.

K. T. Bradford, D. R. L. Carpenter, Jr., and B. L. Shaw, “Forecasting southern plains wind ramp events using the WRF model at 3-km,” in AMS Student Conference, Atlanta, Georgia, 2010, vol. 128, no. 3, pp. 247–253, doi: 10.1016/0378-1097(95)00100-J.

A. Florita, B. M. Hodge, and K. Orwig, “Identifying wind and solar ramping events,” IEEE Green Technol. Conf., no. January, pp. 147–152, 2013, doi: 10.1109/GreenTech.2013.30.

M. S. Eltohamy, M. S. A. Moteleb, H. Talaat, S. F. Mekhamer, and W. Omran, “Power System Flexibility Metrics Review with High Penetration of Variable Renewable Generation,” Int. J. Inf. Technol. Appl., vol. 8, no. 1, pp. 21–46, 2019, [Online]. Available:

M. S. Eltohamy, M. S. A. Moteleb, H. Talaat, S. F. Mekhamer, and W. Omran, “Technical Investigation for Power System Flexibility,” in 6th International Conference on Advanced Control Circuits and Systems (ACCS) & 2019 5th International Conference on New Paradigms in Electronics & information Technology (PEIT), 2019, pp. 299–309, doi: 10.1109/ACCS-PEIT48329.2019.9062862.

M. Cui, J. Zhang, C. Feng, A. R. Florita, Y. Sun, and B. M. Hodge, “Characterizing and analyzing ramping events in wind power, solar power, load, and netload,” Renew. Energy, vol. 111, pp. 227–244, 2017, doi: 10.1016/j.renene.2017.04.005.

G. Ye, Z. Wang, Y. Yan, and Z. Li, “Data-Driven Stochastic Unit Commitment Optimization Considering Spatial Correlation of Wind Farms,” 2020 5th Int. Conf. Power Renew. Energy, ICPRE 2020, pp. 582–587, 2020, doi: 10.1109/ICPRE51194.2020.9233279.

B. S.Everitt and A. Skrondal, The Cambridge Dictionary of Statistics, Fourth edi. Cambridge University Press, 2010.

C. Mihai, I. Lepadat, E. Helerea, and D. Călin, “Load curve analysis for an industrial consumer,” Proc. 12th Int. Conf. Optim. Electr. Electron. Equipment, OPTIM, no. 3, pp. 1275–1280, 2010, doi: 10.1109/OPTIM.2010.5510494.

B. Ela, E., Milligan, M., and Kirby, “Operating Reserves and Variable Generation,” Natl. Renew. Energy Lab., no. NREL/TP-5500-51978, pp. 1–103, 2011, doi: 10.2172/1023095.

G. E. Corridors, “Dimensioning of Control Reserves in Southern Region Grid States,” New Delhi, 2020.

A. Muzhikyan, A. M. Farid, and K. Youcef-Toumi, “An a priori analytical method for the determination of operating reserve requirements,” Int. J. Electr. Power Energy Syst., vol. 86, pp. 1–17, 2017, doi: 10.1016/j.ijepes.2016.09.005.

M. H. T. T. Thilekha et al., “Impact of large-scale wind and solar power integration on operating reserve requirements of an Islanded power system,” in MERCon 2018 - 4th International Multidisciplinary Moratuwa Engineering Research Conference, 2018, pp. 589–594, doi: 10.1109/MERCon.2018.8421919.

A. Abedi and M. Rahimiyan, “Day-ahead energy and reserve scheduling under correlated wind power production,” Electr. Power Energy Syst., vol. 120, no. March, p. 105931, 2020, doi: 10.1016/j.ijepes.2020.105931.

Y. Bapin and V. Zarikas, “Probabilistic Method for Estimation of Spinning Reserves in Multi-connected Power Systems with Bayesian Network-based Rescheduling Algorithm,” in the 11th International Conference on Agents and Artificial Intelligence (ICAART 2019), 2019, pp. 840–849, doi: 10.5220/0007577308400849.

Elia group, “Adequacy and flexibility study for Belgium 2020 - 2030,” 2019. [Online]. Available:

W. Harvest, “Wind Farms in Belgium and Wind Speeds at known locations,” 2020. (accessed Sep. 29, 2021).

R. Brabant and R. Brabant, Environmental Impacts of Offshore Wind Farms in the Belgian Part of the North Sea: Empirical Evidence Inspiring Priority Monitoring, Research and Management., no. January 2021. 2020.

R. Brabant, S. Degraer, and B. Rumes, “Offshore wind energy development in the Belgian part of the North Sea & anticipated impacts : an update,” no. January, 2012.

WindEurope, “Wind energy in Europe 2020 Statistics and the outlook for 2021-2025.” pp. 1–37, 2021, [Online]. Available:übra/Desktop/tezler/210224_windeurope_combined_2020_stats.pdf.

ELIA Group, “Belgium ’ s electricity mix in 2020 : Renewable generation up 31 % in a year marked by the COVID-19 crisis,” Press Release, no. January, pp. 1–7, 2021.

S. Le Bot et al., “Optimal Offshore Wind Energy Developments in Belgium,” 2004. [Online]. Available:

N. Akbari, D. Jones, and R. Treloar, “A cross-European efficiency assessment of offshore wind farms: A DEA approach,” Renew. Energy, vol. 151, pp. 1186–1195, 2020, doi: 10.1016/j.renene.2019.11.130.

A. Malvaldi, S. Weiss, D. Infield, J. Browell, and A. M. Foley, “A spatial and temporal correlation analysis of aggregate wind power in an ideally interconnected Europe,” Wind Energy, vol. 20, no. March, pp. 1315–1329, 2017, doi: 10.1002/we.2095.

A. Abedi and M. Rahimiyan, “Day-ahead energy and reserve scheduling under correlated wind power production,” Int. J. Electr. Power Energy Syst., vol. 120, no. February, p. 105931, 2020, doi: 10.1016/j.ijepes.2020.105931.

L. Söder et al., “Review of wind generation within adequacy calculations and capacity markets for different power systems,” Renew. Sustain. Energy Rev., vol. 119, no. October 2019, 2020, doi: 10.1016/j.rser.2019.109540.

S. Van Ackere, G. Van Eetvelde, D. Schillebeeckx, E. Papa, K. Van Wyngene, and L. Vandevelde, “Wind Resource Mapping Using Landscape Roughness and Spatial Interpolation Methods,” Energies, vol. 8, no. 8, pp. 8682–8703, 2015, doi: 10.3390/en8088682.

“Elia, Belgium’s electricity transmission system operator,” 2021. (accessed Jan. 31, 2021).

Full Text: PDF


  • There are currently no refbacks.


Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
ISSN 2089-3272

Creative Commons Licence

This work is licensed under a Creative Commons Attribution 4.0 International License.

web analytics
View IJEEI Stats