IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v260y2020ics0306261919319269.html
   My bibliography  Save this article

Multi-plan formulation of hydropower generation considering uncertainty of wind power

Author

Listed:
  • Yang, Yuqi
  • Zhou, Jianzhong
  • Liu, Guangbiao
  • Mo, Li
  • Wang, Yongqiang
  • Jia, Benjun
  • He, Feifei

Abstract

Being driven by wind, which has the characteristics of intermittency, the output power of a wind farm is fluctuating and difficult to forecast accurately, which imposes higher requirements on the schedule of the power system. In this paper, the fluctuation characteristics of wind power during the adjacent time interval is accurately captured through statistical analysis of the historical data of the wind farm. Then, a two-layer nested model for a hydro-wind complementary system is formulated to improve the guidance for the daily generation scheduling. In the outer layer, load distribution schemes for each station are determined using the heuristic successive cutting load method. In the inner layer, the water consumption tolerance is introduced so that the original inner model is modified into a multi-plan model of in-station economic operation, which is solved with the multi-plan solution of the dynamic programming method. A cascade hydropower station located in Qing River of China is chosen for a detailed case study. The operational results indicate the following: (1) In hydro-wind complementary system, the water consumption tolerance of a hydropower station based on the fluctuation distribution of wind power is adaptable to wind power uncertainty; (2) Using the multi-plan solution of the dynamic programming method, the optimal scheduling of the load distribution of a hydropower station based on water consumption tolerance provides a larger decision-making space for economic operation of the hydropower station; (3) The adjustable range of unit is related to the range of water head, output, and vibration zone of the hydropower station. A small change in water consumption can bring about a large change in the adjustable range; (4) The daily generation scheduling considering the adjustable range of unit can reserve the adjustment margin of the unit before the implementation of the plan, reduce the number of times the unit passes through the vibration zone during actual operation, and enhance the stability of the unit and the applicability of the daily generation plan, so as to be more adaptable to the uncertainty of wind power and decrease the impact of the uncertainty of wind power on the grid.

Suggested Citation

  • Yang, Yuqi & Zhou, Jianzhong & Liu, Guangbiao & Mo, Li & Wang, Yongqiang & Jia, Benjun & He, Feifei, 2020. "Multi-plan formulation of hydropower generation considering uncertainty of wind power," Applied Energy, Elsevier, vol. 260(C).
    • Handle: RePEc:eee:appene:v:260:y:2020:i:c:s0306261919319269
    DOI: 10.1016/j.apenergy.2019.114239
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261919319269
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2019.114239?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. Paska, Józef & Biczel, Piotr & Kłos, Mariusz, 2009. "Hybrid power systems – An effective way of utilising primary energy sources," Renewable Energy, Elsevier, vol. 34(11), pages 2414-2421.
    2. Zhang, Hongxuan & Lu, Zongxiang & Hu, Wei & Wang, Yiting & Dong, Ling & Zhang, Jietan, 2019. "Coordinated optimal operation of hydro–wind–solar integrated systems," Applied Energy, Elsevier, vol. 242(C), pages 883-896.
    3. Athraa Ali Kadhem & Noor Izzri Abdul Wahab & Ishak Aris & Jasronita Jasni & Ahmed N. Abdalla, 2017. "Advanced Wind Speed Prediction Model Based on a Combination of Weibull Distribution and an Artificial Neural Network," Energies, MDPI, vol. 10(11), pages 1-17, October.
    4. Li, He & Liu, Pan & Guo, Shenglian & Ming, Bo & Cheng, Lei & Yang, Zhikai, 2019. "Long-term complementary operation of a large-scale hydro-photovoltaic hybrid power plant using explicit stochastic optimization," Applied Energy, Elsevier, vol. 238(C), pages 863-875.
    5. Telesca, Luciano & Lovallo, Michele & Kanevski, Mikhail, 2016. "Power spectrum and multifractal detrended fluctuation analysis of high-frequency wind measurements in mountainous regions," Applied Energy, Elsevier, vol. 162(C), pages 1052-1061.
    6. Melamed, Michal & Ben-Tal, Aharon & Golany, Boaz, 2018. "A multi-period unit commitment problem under a new hybrid uncertainty set for a renewable energy source," Renewable Energy, Elsevier, vol. 118(C), pages 909-917.
    7. Bayón, L. & Grau, J.M. & Ruiz, M.M. & Suárez, P.M., 2016. "A comparative economic study of two configurations of hydro-wind power plants," Energy, Elsevier, vol. 112(C), pages 8-16.
    8. Liu, Guangbiao & Zhou, Jianzhong & Jia, Benjun & He, Feifei & Yang, Yuqi & Sun, Na, 2019. "Advance short-term wind energy quality assessment based on instantaneous standard deviation and variogram of wind speed by a hybrid method," Applied Energy, Elsevier, vol. 238(C), pages 643-667.
    9. Wang, Xianxun & Mei, Yadong & Kong, Yanjun & Lin, Yuru & Wang, Hao, 2017. "Improved multi-objective model and analysis of the coordinated operation of a hydro-wind-photovoltaic system," Energy, Elsevier, vol. 134(C), pages 813-839.
    10. Biswas, Partha P. & Suganthan, P.N. & Qu, B.Y. & Amaratunga, Gehan A.J., 2018. "Multiobjective economic-environmental power dispatch with stochastic wind-solar-small hydro power," Energy, Elsevier, vol. 150(C), pages 1039-1057.
    11. Denault, Michel & Dupuis, Debbie & Couture-Cardinal, Sébastien, 2009. "Complementarity of hydro and wind power: Improving the risk profile of energy inflows," Energy Policy, Elsevier, vol. 37(12), pages 5376-5384, December.
    12. Deshmukh, M.K. & Deshmukh, S.S., 2008. "Modeling of hybrid renewable energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(1), pages 235-249, January.
    13. Xinshuo Zhang & Guangwen Ma & Weibin Huang & Shijun Chen & Shuai Zhang, 2018. "Short-Term Optimal Operation of a Wind-PV-Hydro Complementary Installation: Yalong River, Sichuan Province, China," Energies, MDPI, vol. 11(4), pages 1-19, April.
    14. Calif, Rudy, 2012. "PDF models and synthetic model for the wind speed fluctuations based on the resolution of Langevin equation," Applied Energy, Elsevier, vol. 99(C), pages 173-182.
    15. Chen, J.J. & Zhuang, Y.B. & Li, Y.Z. & Wang, P. & Zhao, Y.L. & Zhang, C.S., 2017. "Risk-aware short term hydro-wind-thermal scheduling using a probability interval optimization model," Applied Energy, Elsevier, vol. 189(C), pages 534-554.
    16. Chen, Yue & Wei, Wei & Liu, Feng & Mei, Shengwei, 2016. "Distributionally robust hydro-thermal-wind economic dispatch," Applied Energy, Elsevier, vol. 173(C), pages 511-519.
    17. Ming, Bo & Liu, Pan & Guo, Shenglian & Zhang, Xiaoqi & Feng, Maoyuan & Wang, Xianxun, 2017. "Optimizing utility-scale photovoltaic power generation for integration into a hydropower reservoir by incorporating long- and short-term operational decisions," Applied Energy, Elsevier, vol. 204(C), pages 432-445.
    18. Li, Fang-Fang & Qiu, Jun, 2016. "Multi-objective optimization for integrated hydro–photovoltaic power system," Applied Energy, Elsevier, vol. 167(C), pages 377-384.
    19. Jónsdóttir, Guðrún Margrét & Milano, Federico, 2019. "Data-based continuous wind speed models with arbitrary probability distribution and autocorrelation," Renewable Energy, Elsevier, vol. 143(C), pages 368-376.
    20. Xu, Bin & Zhu, Feilin & Zhong, Ping-an & Chen, Juan & Liu, Weifeng & Ma, Yufei & Guo, Le & Deng, Xiaoliang, 2019. "Identifying long-term effects of using hydropower to complement wind power uncertainty through stochastic programming," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    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. Ming, Bo & Liu, Pan & Guo, Shenglian & Cheng, Lei & Zhou, Yanlai & Gao, Shida & Li, He, 2018. "Robust hydroelectric unit commitment considering integration of large-scale photovoltaic power: A case study in China," Applied Energy, Elsevier, vol. 228(C), pages 1341-1352.
    2. Li, He & Liu, Pan & Guo, Shenglian & Ming, Bo & Cheng, Lei & Yang, Zhikai, 2019. "Long-term complementary operation of a large-scale hydro-photovoltaic hybrid power plant using explicit stochastic optimization," Applied Energy, Elsevier, vol. 238(C), pages 863-875.
    3. Guo, Yi & Ming, Bo & Huang, Qiang & Wang, Yimin & Zheng, Xudong & Zhang, Wei, 2022. "Risk-averse day-ahead generation scheduling of hydro–wind–photovoltaic complementary systems considering the steady requirement of power delivery," Applied Energy, Elsevier, vol. 309(C).
    4. Ming, Bo & Liu, Pan & Guo, Shenglian & Cheng, Lei & Zhang, Jingwen, 2019. "Hydropower reservoir reoperation to adapt to large-scale photovoltaic power generation," Energy, Elsevier, vol. 179(C), pages 268-279.
    5. Kumbuso Joshua Nyoni & Anesu Maronga & Paul Gerard Tuohy & Agabu Shane, 2021. "Hydro–Connected Floating PV Renewable Energy System and Onshore Wind Potential in Zambia," Energies, MDPI, vol. 14(17), pages 1-42, August.
    6. Li, He & Liu, Pan & Guo, Shenglian & Cheng, Lei & Huang, Kangdi & Feng, Maoyuan & He, Shaokun & Ming, Bo, 2021. "Deriving adaptive long-term complementary operating rules for a large-scale hydro-photovoltaic hybrid power plant using ensemble Kalman filter," Applied Energy, Elsevier, vol. 301(C).
    7. Zhang, Yusheng & Ma, Chao & Yang, Yang & Pang, Xiulan & Liu, Lu & Lian, Jijian, 2021. "Study on short-term optimal operation of cascade hydro-photovoltaic hybrid systems," Applied Energy, Elsevier, vol. 291(C).
    8. Gong, Yu & Liu, Pan & Liu, Yini & Huang, Kangdi, 2021. "Robust operation interval of a large-scale hydro-photovoltaic power system to cope with emergencies," Applied Energy, Elsevier, vol. 290(C).
    9. Xu, Bin & Zhu, Feilin & Zhong, Ping-an & Chen, Juan & Liu, Weifeng & Ma, Yufei & Guo, Le & Deng, Xiaoliang, 2019. "Identifying long-term effects of using hydropower to complement wind power uncertainty through stochastic programming," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    10. Wang, Fengjuan & Xie, Yachen & Xu, Jiuping, 2019. "Reliable-economical equilibrium based short-term scheduling towards hybrid hydro-photovoltaic generation systems: Case study from China," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    11. Gong, Yu & Liu, Pan & Ming, Bo & Li, Dingfang, 2021. "Identifying the effect of forecast uncertainties on hybrid power system operation: A case study of Longyangxia hydro–photovoltaic plant in China," Renewable Energy, Elsevier, vol. 178(C), pages 1303-1321.
    12. Sterl, Sebastian & Donk, Peter & Willems, Patrick & Thiery, Wim, 2020. "Turbines of the Caribbean: Decarbonising Suriname's electricity mix through hydro-supported integration of wind power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).
    13. Gong, Yu & Liu, Pan & Ming, Bo & Feng, Maoyuan & Huang, Kangdi & Wang, Yibo, 2022. "Identifying the functional form of operating rules for hydro–photovoltaic hybrid power systems," Energy, Elsevier, vol. 243(C).
    14. Yuan, Wenlin & Wang, Xinqi & Su, Chengguo & Cheng, Chuntian & Liu, Zhe & Wu, Zening, 2021. "Stochastic optimization model for the short-term joint operation of photovoltaic power and hydropower plants based on chance-constrained programming," Energy, Elsevier, vol. 222(C).
    15. Jiang, Jianhua & Ming, Bo & Liu, Pan & Huang, Qiang & Guo, Yi & Chang, Jianxia & Zhang, Wei, 2023. "Refining long-term operation of large hydro–photovoltaic–wind hybrid systems by nesting response functions," Renewable Energy, Elsevier, vol. 204(C), pages 359-371.
    16. Feng, Zhong-kai & Huang, Qing-qing & Niu, Wen-jing & Su, Hua-ying & Li, Shu-shan & Wu, Hui-jun & Wang, Jia-yang, 2024. "Peak operation optimization of cascade hydropower reservoirs and solar power plants considering output forecasting uncertainty," Applied Energy, Elsevier, vol. 358(C).
    17. Chaoyang Chen & Hualing Liu & Yong Xiao & Fagen Zhu & Li Ding & Fuwen Yang, 2022. "Power Generation Scheduling for a Hydro-Wind-Solar Hybrid System: A Systematic Survey and Prospect," Energies, MDPI, vol. 15(22), pages 1-31, November.
    18. Cheng, Qian & Liu, Pan & Ming, Bo & Yang, Zhikai & Cheng, Lei & Liu, Zheyuan & Huang, Kangdi & Xu, Weifeng & Gong, Lanqiang, 2024. "Synchronizing short-, mid-, and long-term operations of hydro-wind-photovoltaic complementary systems," Energy, Elsevier, vol. 305(C).
    19. Zhang, Xinshuo & Huang, Weibin & Chen, Shijun & Xie, Diya & Liu, Dexu & Ma, Guangwen, 2020. "Grid–source coordinated dispatching based on heterogeneous energy hybrid power generation," Energy, Elsevier, vol. 205(C).
    20. Xiong, Hualin & Egusquiza, Mònica & Alberg Østergaard, Poul & Pérez-Díaz, Juan I. & Sun, Guoxiu & Egusquiza, Eduard & Patelli, Edoardo & Xu, Beibei & Duan, Hongjiang & Chen, Diyi & Luo, Xingqi, 2021. "Multi-objective optimization of a hydro-wind-photovoltaic power complementary plant with a vibration avoidance strategy," Applied Energy, Elsevier, vol. 301(C).

    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:260:y:2020:i:c:s0306261919319269. 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.