Calculating Wind Variability Costs with Considering Ramping Costs of Conventional Power Plants

  • Xuemei Dai
  • Kaifeng ZhangEmail author
  • Jian Geng
  • Ying Wang
  • Kun Yuan
Original Article


Due to the variability of the wind power, conventional power plants are required to ramp more frequently to mitigate the imbalance of generation and supply, which increase the total cost of power systems. The increase of the cost is termed the “variability cost” of wind power. Generally, it includes the additional ramping cost, reserve cost and fuel cost of conventional plants. In this paper, we propose an alternative scenario construction method to calculate the “variability cost” of wind power from the viewpoint of the power system schedule. Firstly, in the alternative scenario, a new energy proxy with zero wind variability costs is constructed. Then, a unit commitment optimization model considering ramping costs is developed. The operation costs of power systems under two scenarios (alternative one and real one) are calculated and the difference between two costs is the variability cost. Furthermore, we apply the proposed method to calculate the variability costs of the wind farm cluster. The simulations show that the variability cost increases with higher penetration and higher variability of wind power. Meanwhile, it is found that the variability cost of the wind farm cluster as a whole is lower than the sum of variability costs of each wind farm.


Variability costs Alternative scenario construction method Optimal scheduling model Wind power 



This work is supported by the National Natural Science Foundation of China (No. 51477157) and State Key Laboratory of Smart Grid Protection and Control (Analysis of Response Characteristics of Load Resources and Frequency Control Technology of Source-Grid-Load Coordination).


  1. 1.
    Hans A, Reinhard H (2016) On integrating large shares of variable renewables into the electricity system. Energy 114:1592–1601Google Scholar
  2. 2.
    Zheng D, Eseye AT, Zhang J, Li H (2017) Short-term wind power forecasting using a double-stage hierarchical ANFIS approach for energy management in microgrids. Prot Control Mod Power Syst 2(2):136–145Google Scholar
  3. 3.
    Noureldeen O, Hamdan I (2018) Design of robust intelligent protection technique for large-scale grid-connected wind farm. Prot Control Mod Power Syst 3(3):169–182Google Scholar
  4. 4.
    Sara F, Ewa L (2017) Wind power volatility and its impact on production failures in the Nordic electricity market. Renew Energy 105:96–105Google Scholar
  5. 5.
    Ramteen S (2010) Evaluating the impacts of real-time pricing on the cost and value of wind generation. IEEE Trans Power Syst 25(2):741–748Google Scholar
  6. 6.
    Javad K, Golbon Z, Geoffrey P (2014) The effects of stochastic market clearing on the cost of wind integration: a case of New Zealand electricity market. Energy Syst 5(4):657–675Google Scholar
  7. 7.
    Simshauser P (2011) The hidden costs of wind generation in a thermal power system: what cost?. Aust Econ Rev 44(3):269–292Google Scholar
  8. 8.
    Warren K, Jay A (2012) The cost of wind power variability. Energy Policy 51(6):233–243Google Scholar
  9. 9.
    Andrew M, Wiser R (2013) Changes in the economic value of variable generation at high penetration levels: a pilot case study of california. In: 18th annual POWER conference on energy research and policy, Berkeley, CAGoogle Scholar
  10. 10.
    Hannele H, Peter M, Antje O (2011) Impacts of large amounts of wind power on design and operation of power systems results of IEA collaboration. Wind Energy 14(2):179–192Google Scholar
  11. 11.
    Acker TL, Robitaille A, Holttinen H (2015) Integration of wind and hydropower systems: results of IEA wind task 24. Wind Eng 36(1):1–18Google Scholar
  12. 12.
    Lion H, Falk U, Ottmar E (2015) Integration costs revisited—an economic framework for wind and solar variability. Renew Energy 74:925–939Google Scholar
  13. 13.
    Gil-Hugo A, Catalina GQ, Jesus R (2012) Large-scale wind power integration and wholesale electricity trading benefits: estimation via an ex post approach. Energy Policy 41(4):849–859Google Scholar
  14. 14.
    Lion H (2013) The market value of variable renewables. Energy Econ 38(2):218–236Google Scholar
  15. 15.
    Sensfuß F, Ragwitz M (2011) Weiterentwickeltes Fördersystem für die Vermarktung von erneuerbarer Stromerzeugung. In: Proceedings of the 7th Internationale Energiewirtschaftstagung an der TU Wien, Wien, Austria, 16–18 February 2011Google Scholar
  16. 16.
    Green R, Vasilakos N (2010) Storing wind for a rainy day: what kind of electricity does Denmark export? Discuss Pap 33:1–22Google Scholar
  17. 17.
    Mills AD, Wiser RH (2015) Changes in the economic value of wind energy and flexible resources at increasing penetration levels in the Rocky Mountain Power Area. Wind Energ 17(11):1711–1726Google Scholar
  18. 18.
    Joskow Paul L (2011) Comparing the Costs of intermittent and dispatchable electricity generating technologies. Am Econ Rev 101(3):238–241Google Scholar
  19. 19.
    Fripp M, Wiser RH (2008) Effects of temporal wind patterns on the value of wind-generated electricity in California and the Northwest. IEEE Trans Power Syst 23(2):477–485Google Scholar
  20. 20.
    Falko U, Lion H, Gunnar L, Ottmar E (2013) System LCOE: what are the costs of variable renewables? Energy 63:61–75Google Scholar
  21. 21.
    Michael M, Brendan K (2009) Calculating wind integration costs: separating wind energy value from integration cost impacts. J Physiol 564:775–790Google Scholar
  22. 22.
    Nicolosi M (2012) The economics of renewable electricity market integration. An empirical and model-based analysis of regulatory frameworks and their impacts on the power market. PhD thesis, University of CologneGoogle Scholar
  23. 23.
    Jennifer D, Kevin P, Michael M (2009) Wind energy and power system operations: a Review of wind integration studies to date. Electr J 22(10):34–43Google Scholar
  24. 24.
    Milligan M, Ela E, Hodge B, Kirby B (2011) Cost-causation and integration cost analysis for variable generation. Office of Scientific & Technical Information Report, NREL/TP-5500-51860Google Scholar
  25. 25.
    Hirth L (2012) Integration costs and the value of wind power thoughts on a valuation framework for variable renewable electricity sources. In: USAEE Working Paper, pp. 12–150Google Scholar
  26. 26.
    Chakraborty P, Baeyens E, Khargonekar P (2017) Cost causation based allocations of costs for market integration of renewable energy. IEEE Trans Power Syst 33(1):70–83Google Scholar
  27. 27.
    Niamh T, Damian F, Michael M (2012) Unit commitment with dynamic cycling costs. IEEE Trans Power Syst 27(4):2196–2205Google Scholar
  28. 28.
    Eleanor D, O’Malley M (2007) Quantifying the total net benefits of grid integrated wind. IEEE Trans Power Syst 22(2):605–615Google Scholar
  29. 29.
    Kumar N, Besuner P, Lefton S, Hilleman D (2012) Power plant cycling costs. Office of Scientific and Technical Information Technical Reports. Available at
  30. 30.
    German M-E, Latorre-Jesus M, Andres R (2013) Tight and compact MILP formulation for the thermal unit commitment problem. IEEE Trans Power Syst 28(4):4897–4908Google Scholar
  31. 31.
    Wang J, Shahidehpour M, Li Z (2008) Security-constrained unit commitment with volatile wind power generation. IEEE Trans Power Syst 23(3):1319–1327Google Scholar
  32. 32.
    ERCOT market load and wind generation data. Available at
  33. 33.
    Honghui X, Yong D (2017) dependent evidence combination based on Shearman coefficient and pearson coefficient. IEEE Access 99:1–1Google Scholar
  34. 34.
    Minxian Y, Ronald B (2011) Integration of variable generation, cost-causation, and integration costs. Electr J 24(9):51–63Google Scholar

Copyright information

© The Korean Institute of Electrical Engineers 2019

Authors and Affiliations

  • Xuemei Dai
    • 1
  • Kaifeng Zhang
    • 1
    Email author
  • Jian Geng
    • 2
  • Ying Wang
    • 1
  • Kun Yuan
    • 1
  1. 1.Key Laboratory of Measurement and Control of CSE, Department of AutomationSoutheast UniversityNanjingChina
  2. 2.Department of Electricity MarketChina Electric Power Research InstituteNanjingChina

Personalised recommendations