Economical Evaluation of the Flexible Resources for Providing the Operational Flexibility in the Power System

  • Yi DingEmail author
  • Yonghua Song
  • Hongxun Hui
  • Changzheng Shao


The inherent stochastic nature of wind power requires additional flexibility during power system operation. Traditionally, conventional generation is the only option to provide the required flexibility. However, the provision of the flexibility from the conventional generation such as coal-fired generating units comes at the cost of significantly additional fuel consumption and carbon emissions. Fortunately, with the development of the technologies, energy storage and customer demand response would be able to compete with the conventional generation in providing the flexibility. Give that power systems should deploy the most economic resources for provision of the required operational flexibility, this chapter presents a detailed analysis of the economic characteristics of these key flexibility options. The concept of balancing cost is proposed to represent the cost of utilizing the flexible resources to integrate the variable wind power. The key indicators are proposed respectively for the different flexible resources to measure the balancing cost. Moreover, the optimization models are developed to evaluate the indicators to find out the balancing costs when utilizing different flexible resources. The results illustrate that exploiting the potential of flexibility from demand side management is the preferred option for integrating variable wind power when the penetration level is below 10%, preventing additional fuel consumption and carbon emissions. However, it may require 8% of the customer demand to be flexible and available. Moreover, although energy storage is currently relatively expensive, it is likely to prevail over conventional generation by 2025 to 2030, when the capital cost of energy storage is projected to drop to approximately $400/kWh or lower.


  1. 1.
    C. Feng, M. Cui, B.M. Hodge, J. Zhang, A data-driven multi-model methodology with deep feature selection for short-term wind forecasting. Appl. Energy 190, 1245–1257 (2017)CrossRefGoogle Scholar
  2. 2.
    H.Z. Wang, G.Q. Li, G.B. Wang, J.C. Peng, H. Jiang, Y.T. Liu, Deep learning based ensemble approach for probabilistic wind power forecasting. Appl. Energy 188, 56–70 (2017)CrossRefGoogle Scholar
  3. 3.
    L. Ju, Z. Tan, J. Yuan et al., A bi-level stochastic scheduling optimization model for a virtual power plant connected to a wind–photovoltaic–energy storage system considering the uncertainty and demand response. Appl. Energy 171, 184–199 (2016)CrossRefGoogle Scholar
  4. 4.
    M.H. Amrollahi, S.M.T. Bathaee, Techno-economic optimization of hybrid photovoltaic/wind generation together with energy storage system in a stand-alone micro-grid subjected to demand response. Appl. Energy 202, 66–77 (2017)CrossRefGoogle Scholar
  5. 5.
    Y. Jiang, J. Xu, Y. Sun, C. Wei, J. Wang, D. Ke et al., Day-ahead stochastic economic dispatch of wind integrated power system considering demand response of residential hybrid energy system. Appl. Energy 190, 1126–1137 (2017)CrossRefGoogle Scholar
  6. 6.
    G. Ren, J. Liu, J. Wan, Y. Guo, D. Yu, J. Yan, Overview of wind power intermittency: impacts, measurements, and mitigation solutions. Appl. Energy 204, 47–65 (2017)CrossRefGoogle Scholar
  7. 7.
    M. Kubik, P. Coker, C. Hunt, The role of conventional generation in managing variability. Energy Policy 50, 253–261 (2012)CrossRefGoogle Scholar
  8. 8.
    L. Hirth, F. Ueckerdt, O. Edenhofer, Integration costs revisited–An economic framework for wind and solar variability. Renew. Energy 74, 925–939 (2015)CrossRefGoogle Scholar
  9. 9.
    P. Simshauser, The hidden costs of wind generation in a thermal power system: what cost? Aust. Econ. Rev. 44, 269–292 (2011)CrossRefGoogle Scholar
  10. 10.
    H. Bludszuweit, J.A. Dominguez-Navarro, A probabilistic method for energy storage sizing based on wind power forecast uncertainty. IEEE Trans. Power Syst. 26, 1651–1658 (2011)CrossRefGoogle Scholar
  11. 11.
    H. Holttinen, P. Meibom, A. Orths et al., Impacts of large amounts of wind power on design and operation of power systems, results of IEA collaboration. Wind Energy 14(2), 179–192 (2011)CrossRefGoogle Scholar
  12. 12.
    M. Khalid, R.P. Aguilera, A.V. Savkin, V.G. Agelidis, On maximizing profit of wind-battery supported power station based on wind power and energy price forecasting. Appl. Energy 211, 764–773 (2017)CrossRefGoogle Scholar
  13. 13.
    C.D. Jonghe, B.F. Hobbs, R. Belmans, Optimal generation mix with short-term demand response and wind penetration. IEEE Trans. Power Syst. 27, 830–839 (2012)CrossRefGoogle Scholar
  14. 14.
    T. Broeer, J. Fuller, F. Tuffner, D. Chassin, N. Djilali, Modeling framework and validation of a smart grid and demand response system for wind power integration. Appl. Energy 113, 199–207 (2014)CrossRefGoogle Scholar
  15. 15.
    H. Falsafi, A. Zakariazadeh, S. Jadid, The role of demand response in single and multi-objective wind-thermal generation scheduling: a stochastic programming. Energy 64, 853–867 (2014)CrossRefGoogle Scholar
  16. 16.
    G.P. Swin, M. Godel, Estimating the impact of wind generation on balancing costs in the GB electricity markets, in European Energy Market (2012), pp. 1–8Google Scholar
  17. 17.
    M. Yang, R. Bewley, Integration of variable generation, cost-causation, and integration costs. Electr. J. 24, 51–63 (2011)Google Scholar
  18. 18.
    J. Yan, F. Li, Y. Liu, C. Gu, Novel cost model for balancing wind power forecasting uncertainty. IEEE Trans. Energy Convers. 32, 318–329 (2017)CrossRefGoogle Scholar
  19. 19.
    M. Joos, I. Staffell, Short-term integration costs of variable renewable energy: wind curtailment and balancing in Britain and Germany. Renew. Sustain. Energy Rev. 86, 45–65 (2018)CrossRefGoogle Scholar
  20. 20.
    N. Mahmoudi, T.K. Saha, M. Eghbal, Demand response application by strategic wind power producers. IEEE Trans. Power Syst. 31, 1227–1237 (2015)CrossRefGoogle Scholar
  21. 21.
    Y. Ding, C. Shao, J. Yan, Y. Song, C. Zhang, C. Guo, Economical flexibility options for integrating fluctuating wind energy in power systems: The case of China. Appl. Energy 228, 426–436 (2018).CrossRefGoogle Scholar
  22. 22.
    J. Ma, V. Silva, R. Belhomme, D.S. Kirschen, Evaluating and planning flexibility in sustainable power systems. IEEE Trans. Sustain. Energy 4, 1–11 (2013)CrossRefGoogle Scholar
  23. 23.
    M.S. Lu, C.L. Chang, W.J. Lee, L. Wang, Combining the wind power generation system with energy storage equipment. IEEE Trans. Ind. Appl. 45, 2109–2115 (2009)CrossRefGoogle Scholar
  24. 24.
    P. Giorsetto, K.F. Utsurogi, Development of a new procedure for reliability modeling of wind turbine generators. IEEE Trans. Power Appar. Syst. PAS-102, 134–143 (1983)CrossRefGoogle Scholar
  25. 25.
    L. Cheng, M. Liu, Y. Sun, Y. Ding, A multi-state model for wind farms considering operational outage probability. J. Mod. Power Syst. Clean Energy 1, 177–185 (2013)CrossRefGoogle Scholar
  26. 26.
    R. Karki, P. Hu, R. Billinton, A simplified wind power generation model for reliability evaluation. IEEE Trans. Energy Convers. 21, 533–540 (2006)CrossRefGoogle Scholar
  27. 27.
    H.-I. Su, A. El Gamal, Modeling and analysis of the role of energy storage for renewable integration: power balancing. IEEE Trans. Power Syst. 28, 4109–4117 (2013)CrossRefGoogle Scholar
  28. 28.
    N. Zhang, C. Kang, D.S. Kirschen, Q. Xia, W. Xi, J. Huang et al., Planning pumped storage capacity for wind power integration. IEEE Trans. Sustain. Energy 4, 393–401 (2013)CrossRefGoogle Scholar
  29. 29.
    K. Wang, K. Jiang, B. Chung, T. Ouchi, P.J. Burke, D.A. Boysen et al., Lithium-antimony-lead liquid metal battery for grid-level energy storage. Nature 514, 348–350 (2014)CrossRefGoogle Scholar
  30. 30.
    C. Zhao, Q. Wang, J. Wang, Y. Guan, Expected value and chance constrained stochastic unit commitment ensuring wind power utilization. IEEE Trans. Power Syst. 29, 2696–2705 (2014)CrossRefGoogle Scholar
  31. 31.
    T.K.A. Brekken, A. Yokochi, A.V. Jouanne, Z.Z. Yen, H.M. Hapke, D.A. Halamay, Optimal energy storage sizing and control for wind power applications. IEEE Trans. Sustain. Energy 2, 69–77 (2011)Google Scholar
  32. 32.
    D. Elliott, Renewable energy and sustainable futures. Futures 32, 261–274 (2000)CrossRefGoogle Scholar
  33. 33.
    F. Liu, X. Jiang, Z. Li, Investigation on affects of generator load on coal consumption rate in fossil power plant. Power Syst. Eng. (2008)Google Scholar
  34. 34.
    C. Zhou, K. Qian, M. Allan, W. Zhou, Modeling of the cost of EV battery wear due to V2G application in power systems. IEEE Trans. Energy Convers. 26, 1041–1050 (2011)CrossRefGoogle Scholar
  35. 35.
    S. Han, H. Aki, S. Han, A practical battery wear model for electric vehicle charging applications, in Power and Energy Society General Meeting (2013), pp. 1100–1108Google Scholar
  36. 36.
    Y. Choi, H. Kim, E. Sciubba, Optimal scheduling of energy storage system for self-sustainable base station operation considering battery wear-out cost. Energies 9, 462 (2016)CrossRefGoogle Scholar
  37. 37.
    H. Zhong, L. Xie, Q. Xia, Coupon incentive-based demand response: theory and case study. IEEE Trans. Power Syst. 28, 1266–1276 (2013)CrossRefGoogle Scholar
  38. 38.
    C. Cecati, F. Ciancetta, P. Siano, A multilevel inverter for photovoltaic systems with fuzzy logic control. IEEE Trans. Ind. Electron. 57, 4115–4125 (2010)CrossRefGoogle Scholar
  39. 39.
    R. Doherty, M.O. Malley, A new approach to quantify reserve demand in systems with significant installed wind capacity. IEEE Trans. Power Syst. 20, 587–595 (2005)CrossRefGoogle Scholar
  40. 40.
    The Report on National Electricity Reliability Index in 2015 (China Electricity Councile)Google Scholar
  41. 41.
    M. Klobasa, Analysis of demand response and wind integration in Germany’s electricity market. IET Renew. Power Gener. 4, 55–63 (2010)CrossRefGoogle Scholar
  42. 42.
    Y. Ding, S. Pineda, P. Nyeng, J. Østergaard, E.M. Larsen, Q. Wu, Real-time market concept architecture for EcoGrid EU—A prototype for european smart grids. IEEE Trans. Smart Grid 4, 2006–2016 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Yi Ding
    • 1
    Email author
  • Yonghua Song
    • 1
    • 2
  • Hongxun Hui
    • 1
  • Changzheng Shao
    • 1
  1. 1.Zhejiang UniversityHangzhouChina
  2. 2.University of MacauMacauChina

Personalised recommendations