IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v228y2018icp426-436.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261918309358
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2018.06.066?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mohseni, Soheil & Brent, Alan C. & Kelly, Scott & Browne, Will N., 2022. "Demand response-integrated investment and operational planning of renewable and sustainable energy systems considering forecast uncertainties: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    2. Lewis, Matt & McNaughton, James & Márquez-Dominguez, Concha & Todeschini, Grazia & Togneri, Michael & Masters, Ian & Allmark, Matthew & Stallard, Tim & Neill, Simon & Goward-Brown, Alice & Robins, Pet, 2019. "Power variability of tidal-stream energy and implications for electricity supply," Energy, Elsevier, vol. 183(C), pages 1061-1074.
    3. Wang, Yun & Zou, Runmin & Liu, Fang & Zhang, Lingjun & Liu, Qianyi, 2021. "A review of wind speed and wind power forecasting with deep neural networks," Applied Energy, Elsevier, vol. 304(C).
    4. Biancardi, Andrea & Mendes, Carla & Staffell, Iain, 2024. "Battery electricity storage as both a complement and substitute for cross-border interconnection," Energy Policy, Elsevier, vol. 189(C).
    5. Kong, Xiangyu & Sun, Fangyuan & Huo, Xianxu & Li, Xue & Shen, Yu, 2020. "Hierarchical optimal scheduling method of heat-electricity integrated energy system based on Power Internet of Things," Energy, Elsevier, vol. 210(C).
    6. Yao, Xing & Yi, Bowen & Yu, Yang & Fan, Ying & Zhu, Lei, 2020. "Economic analysis of grid integration of variable solar and wind power with conventional power system," Applied Energy, Elsevier, vol. 264(C).
    7. Wei, Congying & Xu, Jian & Liao, Siyang & Sun, Yuanzhang & Jiang, Yibo & Ke, Deping & Zhang, Zhen & Wang, Jing, 2018. "A bi-level scheduling model for virtual power plants with aggregated thermostatically controlled loads and renewable energy," Applied Energy, Elsevier, vol. 224(C), pages 659-670.
    8. Sun, Mucun & Feng, Cong & Zhang, Jie, 2020. "Multi-distribution ensemble probabilistic wind power forecasting," Renewable Energy, Elsevier, vol. 148(C), pages 135-149.
    9. López Prol, Javier & Zilberman, David, 2023. "No alarms and no surprises: Dynamics of renewable energy curtailment in California," Energy Economics, Elsevier, vol. 126(C).
    10. Boza, Pal & Evgeniou, Theodoros, 2021. "Artificial intelligence to support the integration of variable renewable energy sources to the power system," Applied Energy, Elsevier, vol. 290(C).
    11. Kocaman, Ayse Selin & Ozyoruk, Emin & Taneja, Shantanu & Modi, Vijay, 2020. "A stochastic framework to evaluate the impact of agricultural load flexibility on the sizing of renewable energy systems," Renewable Energy, Elsevier, vol. 152(C), pages 1067-1078.
    12. Jiang, Huaiguang & Zhang, Yingchen & Chen, Yuche & Zhao, Changhong & Tan, Jin, 2018. "Power-traffic coordinated operation for bi-peak shaving and bi-ramp smoothing – A hierarchical data-driven approach," Applied Energy, Elsevier, vol. 229(C), pages 756-766.
    13. Rabiee, Abdorreza & Sadeghi, Mohammad & Aghaeic, Jamshid & Heidari, Alireza, 2016. "Optimal operation of microgrids through simultaneous scheduling of electrical vehicles and responsive loads considering wind and PV units uncertainties," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 721-739.
    14. Jia, Ke & Li, Yanbin & Fang, Yu & Zheng, Liming & Bi, Tianshu & Yang, Qixun, 2018. "Transient current similarity based protection for wind farm transmission lines," Applied Energy, Elsevier, vol. 225(C), pages 42-51.
    15. Shanmugarajah Vinothine & Lidula N. Widanagama Arachchige & Athula D. Rajapakse & Roshani Kaluthanthrige, 2022. "Microgrid Energy Management and Methods for Managing Forecast Uncertainties," Energies, MDPI, vol. 15(22), pages 1-22, November.
    16. Suryanarayana, Gowri & Lago, Jesus & Geysen, Davy & Aleksiejuk, Piotr & Johansson, Christian, 2018. "Thermal load forecasting in district heating networks using deep learning and advanced feature selection methods," Energy, Elsevier, vol. 157(C), pages 141-149.
    17. Qian, Zheng & Pei, Yan & Zareipour, Hamidreza & Chen, Niya, 2019. "A review and discussion of decomposition-based hybrid models for wind energy forecasting applications," Applied Energy, Elsevier, vol. 235(C), pages 939-953.
    18. Qiao, Qiao & Zeng, Xianhai & Lin, Boqiang, 2024. "Mitigating wind curtailment risk in China: The impact of subsidy reduction policy," Applied Energy, Elsevier, vol. 368(C).
    19. Romeiro, Diogo Lisbona & Almeida, Edmar Luiz Fagundes de & Losekann, Luciano, 2020. "Systemic value of electricity sources – What we can learn from the Brazilian experience?," Energy Policy, Elsevier, vol. 138(C).
    20. Boßmann, Tobias & Eser, Eike Johannes, 2016. "Model-based assessment of demand-response measures—A comprehensive literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 1637-1656.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:228:y:2018:i:c:p:426-436. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.