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Economical flexibility options for integrating fluctuating wind energy in power systems: The case of China

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  • Ding, Yi
  • Shao, Changzheng
  • Yan, Jinyue
  • Song, Yonghua
  • Zhang, Chi
  • Guo, Chuangxin

Abstract

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 paper 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.

Suggested Citation

  • Ding, Yi & Shao, Changzheng & Yan, Jinyue & Song, Yonghua & Zhang, Chi & Guo, Chuangxin, 2018. "Economical flexibility options for integrating fluctuating wind energy in power systems: The case of China," Applied Energy, Elsevier, vol. 228(C), pages 426-436.
  • Handle: RePEc:eee:appene:v:228:y:2018:i:c:p:426-436
    DOI: 10.1016/j.apenergy.2018.06.066
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    1. Kubik, M.L. & Coker, P.J. & Hunt, C., 2012. "The role of conventional generation in managing variability," Energy Policy, Elsevier, vol. 50(C), pages 253-261.
    2. Ju, Liwei & Tan, Zhongfu & Yuan, Jinyun & Tan, Qingkun & Li, Huanhuan & Dong, Fugui, 2016. "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," Applied Energy, Elsevier, vol. 171(C), pages 184-199.
    3. Yohwan Choi & Hongseok Kim, 2016. "Optimal Scheduling of Energy Storage System for Self-Sustainable Base Station Operation Considering Battery Wear-Out Cost," Energies, MDPI, vol. 9(6), pages 1-19, June.
    4. Paul Simshauser, 2011. "The Hidden Costs of Wind Generation in a Thermal Power System: What Cost?," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 44(3), pages 269-292, September.
    5. Feng, Yi & Lin, Heyun & Ho, S.L. & Yan, Jianhu & Dong, Jianning & Fang, Shuhua & Huang, Yunkai, 2015. "Overview of wind power generation in China: Status and development," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 847-858.
    6. Amrollahi, Mohammad Hossein & Bathaee, Seyyed Mohammad Taghi, 2017. "Techno-economic optimization of hybrid photovoltaic/wind generation together with energy storage system in a stand-alone micro-grid subjected to demand response," Applied Energy, Elsevier, vol. 202(C), pages 66-77.
    7. Broeer, Torsten & Fuller, Jason & Tuffner, Francis & Chassin, David & Djilali, Ned, 2014. "Modeling framework and validation of a smart grid and demand response system for wind power integration," Applied Energy, Elsevier, vol. 113(C), pages 199-207.
    8. Hirth, Lion & Ueckerdt, Falko & Edenhofer, Ottmar, 2015. "Integration costs revisited – An economic framework for wind and solar variability," Renewable Energy, Elsevier, vol. 74(C), pages 925-939.
    9. Dong, Changgui & Qi, Ye & Dong, Wenjuan & Lu, Xi & Liu, Tianle & Qian, Shuai, 2018. "Decomposing driving factors for wind curtailment under economic new normal in China," Applied Energy, Elsevier, vol. 217(C), pages 178-188.
    10. William Nordhaus, 2014. "Estimates of the Social Cost of Carbon: Concepts and Results from the DICE-2013R Model and Alternative Approaches," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 1(1), pages 000.
    11. Wang, Huai-zhi & Li, Gang-qiang & Wang, Gui-bin & Peng, Jian-chun & Jiang, Hui & Liu, Yi-tao, 2017. "Deep learning based ensemble approach for probabilistic wind power forecasting," Applied Energy, Elsevier, vol. 188(C), pages 56-70.
    12. Sheikhi, Aras & Bahrami, Shahab & Ranjbar, Ali Mohammad, 2015. "An autonomous demand response program for electricity and natural gas networks in smart energy hubs," Energy, Elsevier, vol. 89(C), pages 490-499.
    13. Jiang, Yibo & Xu, Jian & Sun, Yuanzhang & Wei, Congying & Wang, Jing & Ke, Deping & Li, Xiong & Yang, Jun & Peng, Xiaotao & Tang, Bowen, 2017. "Day-ahead stochastic economic dispatch of wind integrated power system considering demand response of residential hybrid energy system," Applied Energy, Elsevier, vol. 190(C), pages 1126-1137.
    14. Feng, Cong & Cui, Mingjian & Hodge, Bri-Mathias & Zhang, Jie, 2017. "A data-driven multi-model methodology with deep feature selection for short-term wind forecasting," Applied Energy, Elsevier, vol. 190(C), pages 1245-1257.
    15. Ren, Guorui & Liu, Jinfu & Wan, Jie & Guo, Yufeng & Yu, Daren, 2017. "Overview of wind power intermittency: Impacts, measurements, and mitigation solutions," Applied Energy, Elsevier, vol. 204(C), pages 47-65.
    16. Kangli Wang & Kai Jiang & Brice Chung & Takanari Ouchi & Paul J. Burke & Dane A. Boysen & David J. Bradwell & Hojong Kim & Ulrich Muecke & Donald R. Sadoway, 2014. "Lithium–antimony–lead liquid metal battery for grid-level energy storage," Nature, Nature, vol. 514(7522), pages 348-350, October.
    17. Joos, Michael & Staffell, Iain, 2018. "Short-term integration costs of variable renewable energy: Wind curtailment and balancing in Britain and Germany," Renewable and Sustainable Energy Reviews, Elsevier, vol. 86(C), pages 45-65.
    18. Falsafi, Hananeh & Zakariazadeh, Alireza & Jadid, Shahram, 2014. "The role of demand response in single and multi-objective wind-thermal generation scheduling: A stochastic programming," Energy, Elsevier, vol. 64(C), pages 853-867.
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