Home Unleashing the economic potential of wind power for ancillary services
Article
Licensed
Unlicensed Requires Authentication

Unleashing the economic potential of wind power for ancillary services

  • Sanchita Baral and George Xydis ORCID logo EMAIL logo
Published/Copyright: February 9, 2024
Become an author with De Gruyter Brill
Submit Manuscript Author Information
International Journal of Emerging Electric Power Systems
From the journal International Journal of Emerging Electric Power Systems Volume 26 Issue 1

Abstract

Ancillary services play a significant role in ensuring stable operation of power systems in the growing penetration of renewable energy. Some of the important functions for ancillary support include voltage support, frequency support, and system restoration. This work highlights the possibility of relevant ancillary support from wind power and investigates how such services will impact from an economic point of view. A crucial aspect of ancillary services from wind power relates to tailored and dynamic information about the conditional power production from the turbines and wind parks which could be obtained through accurate wind power forecasting. The study of the impact of forecast accuracy and correlation in errors on the possibility for wind power to provide ancillary services is analyzed. Furthermore, the limitations and challenges associated with the technical capabilities of wind turbines to support such services are reviewed and presented. An initial qualitative assessment of the value of providing such services is carried out and the capabilities combined with electricity market data from Nord Pool taking an example of the western region of Denmark is discussed. The result of this work shows that enhancing ancillary services from wind power combined with more accurate wind forecasting has a positive financial impact on wind power market.

Keywords: forecasting; ancillary services; wind energy
Corresponding author: George Xydis, Department of Business Development and Technology, Arhus University, Birk Centerpark 15, 7400, Herning, Denmark; and Department of Mechanical Engineering, University of the Peloponnese, 1 Megalou Alexandrou str., Koukouli, 26334, Patras, Achaia, Greece, E-mail:

  1. Research ethics: Not applicable.

  2. Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Competing interests: The authors state no conflict of interest.

  4. Research funding: None declared.

  5. Data availability: The raw data can be obtained on request from the corresponding author.

References

1. Ghadiali, F, Xydis, G. Grid integration of wind farms and the interconnection requirements for the US grid. Int J Emerg Elec Power Syst 2024;25:421–8. https://doi.org/10.1515/ijeeps-2023-0037.Search in Google Scholar

2. Rasmussen, NB, Enevoldsen, P, Xydis, G. Transformative multivalue business models: a bottom‐up perspective on the hydrogen‐based green transition for modern wind power cooperatives. Int J Energy Res 2020;44:3990–4007. https://doi.org/10.1002/er.5215.Search in Google Scholar

3. Ma, O, Alkadi, N, Cappers, P, Denholm, P, Dudley, J, Goli, S, et al.. Demand response for ancillary services. IEEE Trans Smart Grid 2013;4:1988–95. https://doi.org/10.1109/tsg.2013.2258049.Search in Google Scholar

4. Skytte, K, Grohnheit, PE. Market prices in a power market with more than 50% wind power. In: Electricity markets with increasing levels of renewable generation: structure, operation, agent-based simulation, and emerging designs; 2018:81–94 pp.10.1007/978-3-319-74263-2_4Search in Google Scholar

5. Kumar, Y, Ringenberg, J, Depuru, SS, Devabhaktuni, VK, Lee, JW, Nikolaidis, E, et al.. Wind energy: trends and enabling technologies. Renew Sustain Energy Rev 2016;53:209–24. https://doi.org/10.1016/j.rser.2015.07.200.Search in Google Scholar

6. Wang, Q, Zhang, C, Ding, Y, Xydis, G, Wang, J, Østergaard, J. Review of real-time electricity markets for integrating distributed energy resources and demand response. Appl Energy 2015;138:695–706. https://doi.org/10.1016/j.apenergy.2014.10.048.Search in Google Scholar

7. Hesser, T, Succar, S. Renewables integration through direct load control and demand response. In: Smart grid. Cambridge, MA: Academic Press; 2012:209–33 pp.10.1016/B978-0-12-386452-9.00009-7Search in Google Scholar

8. Rebello, E, Watson, D, Rodgers, M. Ancillary services from wind turbines: automatic generation control (AGC) from a single Type 4 turbine. Wind Energy Sci 2020;5:225–36. https://doi.org/10.5194/wes-5-225-2020.Search in Google Scholar

9. Holttinen, H, Ivanova, A, Dominguez, JL. Wind power in markets for frequency support services. In: 2016 13th international conference on the European Energy Market (EEM). Porto, Portugal: IEEE; 2016:1–5 pp.10.1109/EEM.2016.7521310Search in Google Scholar

10. ENTSO-E. ENTSO-E network code for requirements for grid connection appicable to all generators. Brussels: ENTSO-E; 2013.Search in Google Scholar

11. Bucher, MA, Delikaraoglou, S, Heussen, K, Pinson, P, Andersson, G. On quantification of flexibility in power systems. In: 2015 IEEE Eindhoven PowerTech. Eindhoven, The Netherlands: IEEE; 2015:1–6 pp.10.1109/PTC.2015.7232514Search in Google Scholar

12. Holttinen, H, Kiviluoma, J, Cutululis, N, Gubina, A, Keane, A, Van Julle, F. Ancillary services: technical specifications, system needs and costs: REserviceS Deliverable D2. 2. Lyngby, Denmark: DTU; 2012.Search in Google Scholar

13. EirGrid and SONI. DS3 system services: portfolio capability analysis. Dublin, Ireland: EirGrid and SONI; 2014.Search in Google Scholar

14. Faiella, M, Hennig, T, Cutululis, NA, & Van Hulle, F. Capabilities and costs for ancillary services provision by wind power plants. Deliverable d 3.1.Search in Google Scholar

15. Energinet. Energinet; 2019. [Online]. https://en.energinet.dk/Electricity/Rules-and-Regulations/Regulations-for-grid-connection [Accessed March 2020].Search in Google Scholar

16. Energinet. Technical regulation 3.2.5 for wind power plants above 11kW. Fredericia, Denmark: Energinet DK; 2017.Search in Google Scholar

17. Bousseau, P, Belhomme, R, Monnot, E, Laverdure, N, Boëda, D, Roye, D, et al.. Contribution of wind farms to ancillary services. Cigre 2006;21:1–11.Search in Google Scholar

18. NERC, R. Committee and others. Balancing and frequency control: a technical document prepared by the nerc resources subcommittee. Atlanta, GA: NERC Technical Report, Tech. Rep; 2011.Search in Google Scholar

19. Ancillary Services; n.d. www.next-kraftwerke.com. https://www.next-kraftwerke.com/knowledge/ancillary-services [Accessed 18 Feb 2023].Search in Google Scholar

20. Papalexopoulos, A, Singh, H. On the various design options for ancillary services markets. In: Proceedings of the 34th annual Hawaii international conference on system sciences. Hawaii, USA: IEEE; 2001:8 pp.10.1109/HICSS.2001.926288Search in Google Scholar

21. Albeta Electric System Operator. Market and operational framework for wind integration in Alberta. Alberta, Canada: Albeta Electric System Operator; 2007.Search in Google Scholar

22. Sproul, S. Do-you-know-the-difference-between-virtual-inertia-and-fast-frequency-response?; 2020. [Online]. http://www.wattclarity.com.au/articles/2020/04/do-you-know-the-difference-between-virtual-inertia-and-fast-frequency-response/ [Accessed 07 April 2020].Search in Google Scholar

23. Roscoe, A, Brogan, P, Elliott, D, Knueppel, T, Gutierrez, I, Campion, JP, et al.. Practical experience of operating a grid forming wind park and its response to system events. In: Proceedings of the 18th wind integration workshop, Dublin, Ireland; 2019:16–18 pp.Search in Google Scholar

24. EWEA. Wind energy and electricity prices. Brussels, Belgium: EWEA; 2010. www.ewea.or.Search in Google Scholar

25. Kumar, A, Meena, NK, Singh, AR, Deng, Y, He, X, Bansal, RC, et al.. Strategic integration of battery energy storage systems with the provision of distributed ancillary services in active distribution systems. Appl Energy 2019;253:113503. https://doi.org/10.1016/j.apenergy.2019.113503.Search in Google Scholar

26. Ela, E, Kirby, B, Navid, N, Smith, JC. Effective ancillary services market designs on high wind power penetration systems. In: 2012 IEEE power and energy society general meeting. San Diego, California: IEEE; 2012:1–8 pp.10.1109/PESGM.2012.6345361Search in Google Scholar

27. IRENA. Innovative ancillary services. Masdar City, UAE: IRENA; 2019.Search in Google Scholar

28. Singh, H, Papalexopoulos, A. Competitive procurement of ancillary services by an independent system operator. IEEE Trans Power Syst 1999;14:498–504. https://doi.org/10.1109/59.761872.Search in Google Scholar

29. Dobschinski, J. How good is my forecast? Comparability of wind power forecast errors. In: Proceedings of the 13th international workshop on large scale integration of wind power into power systems as well as on transmission networks for offshore wind farms, Berlin, Germany; 2014, vol 1113.Search in Google Scholar

30. Monteiro, C, Bessa, R, Miranda, V, Botterud, A, Wang, J, Conzelmann, G. Wind power forecasting: state-of-the-art 2009 (No. ANL/DIS-10-1). Argonne, IL (United States): Argonne National Lab.(ANL); 2009.10.2172/968212Search in Google Scholar

31. Giebel, G, Kariniotakis, G. Wind power forecasting—a review of the state of the art. Renew Energy Forecast 2017:59–109. https://doi.org/10.1016/b978-0-08-100504-0.00003-2.Search in Google Scholar

32. Madsen, H, Pinson, P, Kariniotakis, G, Nielsen, HA, Nielsen, TS. Standardizing the performance evaluation of short-term wind power prediction models. Wind Eng 2005;29:475–89. https://doi.org/10.1260/030952405776234599.Search in Google Scholar

33. Kariniotakis, G, Martí, I, Casas, D, Pinson, P, Nielsen, TS, Madsen, H, et al.. What performance can be expected by short-term wind power prediction models depending on site characteristics? In: European wind energy conference EWEC 2004; 2004.Search in Google Scholar

34. Lang, S, Möhrlen, C, Jørgensen, J, Gallachóir, BO, McKeogh, E. Forecasting total wind power generation on the republic of Ireland grid with a multi-scheme ensemble prediction system. Vectors 2006;10:11.Search in Google Scholar

35. Tastu, J, Pinson, P, Kotwa, E, Madsen, H, Nielsen, HA. Spatio‐temporal analysis and modeling of short‐term wind power forecast errors. Wind Energy 2011;14:43–60. https://doi.org/10.1002/we.401.Search in Google Scholar

36. eSett Oy Settlement, Service. Nordic imbalance settlement. Helsinki, FL: eSett Oy Settlement Service; 2018. [Online]. Available from: https://www.esett.com.Search in Google Scholar

37. Holttinen, H, Miettinen, JJ, Sillanpää, S. Wind power forecasting accuracy and uncertainty in Finland. Espoo, FL: VTT Technical Research Centre of Finland; 2013.Search in Google Scholar

38. Foley, AM, McIlwaine, N, Morrow, DJ, Hayes, BP, Zehir, MA, Mehigan, L, et al.. A critical evaluation of grid stability and codes, energy storage and smart loads in power systems with wind generation. Energy 2020;205:117671. https://doi.org/10.1016/j.energy.2020.117671.Search in Google Scholar

39. Aziz, A, Oo, AT, Stojcevski, A. Frequency regulation capabilities in wind power plant. Sustain Energy Technol Assessments 2018;26:47–76. https://doi.org/10.1016/j.seta.2017.10.002.Search in Google Scholar

40. Wang, S, Tomsovic, K. Fast frequency support from wind turbine generators with auxiliary dynamic demand control. IEEE Trans Power Syst 2019;34:3340–8. https://doi.org/10.1109/tpwrs.2019.2911232.Search in Google Scholar

41. Nikolopoulou, A. Wind turbine contribution to ancillary services under increased renewable penetration levels. Delft, The Netherlands: TU Delft; 2017.Search in Google Scholar

42. Yaramasu, V, Wu, B, Sen, PC, Kouro, S, Narimani, M. High-power wind energy conversion systems: state-of-the-art and emerging technologies. Proc IEEE 2015;103:740–88. https://doi.org/10.1109/jproc.2014.2378692.Search in Google Scholar

43. Du, P. Ancillary services (AS) at ERCOT. In: Renewable energy integration for bulk power systems: ERCOT and the Texas interconnection. Cham: Springer International Publishing; 2023:103–36 pp.10.1007/978-3-031-28639-1_4Search in Google Scholar

44. Gau, DY, Wu, YK. Overview of reactive power and voltage control of offshore wind farms. In: 2020 international symposium on computer, consumer and control (IS3C). IEEE; 2020:276–9 pp.10.1109/IS3C50286.2020.00078Search in Google Scholar

45. Enevoldsen, P, Xydis, G. Examining the trends of 35 years growth of key wind turbine components. Energy Sustain Develop 2019;50:18–26. https://doi.org/10.1016/j.esd.2019.02.003.Search in Google Scholar

46. Ørsted. Making-green-energy-affordable. Gentofte, Denmark: Ørsted; 2019.Search in Google Scholar

47. Foley, AM, Leahy, PG, Marvuglia, A, McKeogh, EJ. Current methods and advances in forecasting of wind power generation. Renew Energy 2012;37:1–8. https://doi.org/10.1016/j.renene.2011.05.033.Search in Google Scholar

48. Ullah, NR, Bhattacharya, K, Thiringer, T. Wind farms as reactive power ancillary service providers—technical and economic issues. IEEE Trans Energy Convers 2009;24:661–72. https://doi.org/10.1109/tec.2008.2008957.Search in Google Scholar

49. Sideratos, G, Hatziargyriou, ND. An advanced statistical method for wind power forecasting. IEEE Trans Power Syst 2007;22:258–65. https://doi.org/10.1109/tpwrs.2006.889078.Search in Google Scholar

50. Barbosa de Alencar, D, de Mattos Affonso, C, Limão de Oliveira, RC, Moya Rodriguez, JL, Leite, JC, Reston Filho, JC. Different models for forecasting wind power generation: case study. Energies 2017;10:1976. https://doi.org/10.3390/en10121976.Search in Google Scholar

51. Soman, SS, Zareipour, H, Malik, O, Mandal, P. A review of wind power and wind speed forecasting methods with different time horizons. In: North American power symposium 2010. IEEE; 2010:1–8 pp.10.1109/NAPS.2010.5619586Search in Google Scholar

52. Wu, YK, Hong, JS. A literature review of wind forecasting technology in the world. In: 2007 IEEE Lausanne Power Tech; 2007:504–9 pp.10.1109/PCT.2007.4538368Search in Google Scholar

53. Lange, M, Focken, U. New developments in wind energy forecasting. In: 2008 IEEE power and energy society general meeting-conversion and delivery of electrical energy in the 21st century. IEEE; 2008:1–8 pp.10.1109/PES.2008.4596135Search in Google Scholar

54. Potter, CW, Negnevitsky, M. Very short-term wind forecasting for Tasmanian power generation. IEEE Trans Power Syst 2006;21:965–72. https://doi.org/10.1109/tpwrs.2006.873421.Search in Google Scholar

55. Rajagopalan, S, Santoso, S. Wind power forecasting and error analysis using the autoregressive moving average modeling. In: 2009 IEEE power & energy society general meeting. IEEE; 2009:1–6 pp.10.1109/PES.2009.5276019Search in Google Scholar

56. Chandra, DR, Kumari, MS, Sydulu, M. A detailed literature review on wind forecasting. In: 2013 international conference on power, energy and control (ICPEC). IEEE; 2013:630–4 pp.10.1109/ICPEC.2013.6527734Search in Google Scholar

57. Ammar, E, Xydis, G. Wind speed forecasting using deep learning and preprocessing techniques. Int J Green Energy 2023:1–29. https://doi.org/10.1080/15435075.2023.2228878.Search in Google Scholar

58. Hodge, BM, Lew, D, Milligan, M, Holttinen, H, Sillanpaa, S, Gómez-Lázaro, E, et al.. Wind power forecasting error distributions: an international comparison (No. NREL/CP-5500-56130). Golden, CO (United States): National Renewable Energy Lab.(NREL); 2012.Search in Google Scholar

59. Pappala, VS, Erlich, I, Rohrig, K, Dobschinski, J. A stochastic model for the optimal operation of a wind-thermal power system. IEEE Trans Power Syst 2009;24:940–50. https://doi.org/10.1109/tpwrs.2009.2016504.Search in Google Scholar

60. Ortega-Vazquez, MA, Kirschen, DS. Estimating the spinning reserve requirements in systems with significant wind power generation penetration. IEEE Trans Power Syst 2008;24:114–24. https://doi.org/10.1109/tpwrs.2008.2004745.Search in Google Scholar

61. Soder, L. Reserve margin planning in a wind-hydro-thermal power system. IEEE Trans Power Syst 1993;8:564–71. https://doi.org/10.1109/59.260826.Search in Google Scholar

62. Doherty, R, O’malley, M. A new approach to quantify reserve demand in systems with significant installed wind capacity. IEEE Trans Power Syst 2005;20:587–95. https://doi.org/10.1109/tpwrs.2005.846206.Search in Google Scholar

63. Pinson, P. Ancillary services and regulation markets. Lecture at DTU. Lyngby, Denmark: Technical University of Denmark-Center for Electric Power and Energy; 2018.Search in Google Scholar

64. Rose, S, Apt, J. The cost of curtailing wind turbines for frequency regulation and ramp-rate limitation. In: Proc. 29th USAEE/IAEE North American conference on energy and the environment: conventional and unconventional solutions; 2010:1–18 pp.Search in Google Scholar

65. Neamtu, I. Wind power effects and price elasticity of demand for the Nordic electricity markets [Doctoral dissertation Ph.D. thesis]. Department of Economics and Business Economics, Aarhus University, Aarhus, Denmark; 2016. https://pure.au.dk/portal/files/104558637/PhD_dissertation_Ioana_Neamtu.pdf.Search in Google Scholar

66. Jónsson, T, Pinson, P, Madsen, H. On the market impact of wind energy forecasts. Energy Econ 2010;32:313–20. https://doi.org/10.1016/j.eneco.2009.10.018.Search in Google Scholar

67. Ketterer, JC. The impact of wind power generation on the electricity price in Germany. Energy Econ 2014;44:270–80. https://doi.org/10.1016/j.eneco.2014.04.003.Search in Google Scholar
Received: 2023-07-25
Accepted: 2024-01-11
Published Online: 2024-02-09

© 2024 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Research Articles
  3. A seven level fault tolerant hybrid cascaded inverter for renewable energy applications
  4. Optimal layout scheme design of distribution network micro PMU based on information entropy theory
  5. Current sensorless model predictive control for LC-filtered voltage source inverters based on sliding mode observer
  6. BCLM: a novel chaotic map for designing cryptography-based security mechanism for IEEE C37.118.2 PMU communication in smart grid
  7. Design and control of utility grid-tied bipolar DC microgrid
  8. Network dynamics in hybrid microgrid and its implications on stability analysis
  9. Electrical modelling, design, and implementation of a hardware PEM electrolyzer emulator for smart grid testing
  10. A hybrid search space reduction algorithm and Newton–Raphson based selective harmonic elimination for an asymmetric cascade H-bridge multi-level inverter
  11. Dynamic load prediction of charging piles for energy storage electric vehicles based on Space-time constraints in the internet of things environment
  12. Power coordination control method for AC/DC hybrid microgrid considering demand response
  13. Performance analysis and effective modeling of a solar photovoltaic module based on field tests
  14. Unleashing the economic potential of wind power for ancillary services
https://doi.org/10.1515/ijeeps-2023-0267
Keywords for this article
forecasting; ancillary services; wind energy

Articles in the same Issue

  1. Frontmatter
  2. Research Articles
  3. A seven level fault tolerant hybrid cascaded inverter for renewable energy applications
  4. Optimal layout scheme design of distribution network micro PMU based on information entropy theory
  5. Current sensorless model predictive control for LC-filtered voltage source inverters based on sliding mode observer
  6. BCLM: a novel chaotic map for designing cryptography-based security mechanism for IEEE C37.118.2 PMU communication in smart grid
  7. Design and control of utility grid-tied bipolar DC microgrid
  8. Network dynamics in hybrid microgrid and its implications on stability analysis
  9. Electrical modelling, design, and implementation of a hardware PEM electrolyzer emulator for smart grid testing
  10. A hybrid search space reduction algorithm and Newton–Raphson based selective harmonic elimination for an asymmetric cascade H-bridge multi-level inverter
  11. Dynamic load prediction of charging piles for energy storage electric vehicles based on Space-time constraints in the internet of things environment
  12. Power coordination control method for AC/DC hybrid microgrid considering demand response
  13. Performance analysis and effective modeling of a solar photovoltaic module based on field tests
  14. Unleashing the economic potential of wind power for ancillary services
Sign up now to receive a 20% welcome discount
Institutional Access
How does access work?
Have an idea on how to improve our website?
Please write us.
© 2025 De Gruyter Brill
Downloaded on 27.10.2025 from https://www.degruyterbrill.com/document/doi/10.1515/ijeeps-2023-0267/html
Scroll to top button