IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v256y2019ics0306261919316058.html
   My bibliography  Save this article

Worst-case conditional value-at-risk based bidding strategy for wind-hydro hybrid systems under probability distribution uncertainties

Author

Listed:
  • Liu, Yangyang
  • Shen, Zhongqi
  • Tang, Xiaowei
  • Lian, Hongbo
  • Li, Jiarui
  • Gong, Jinxia

Abstract

It is challenging for renewable power (such as wind power) to participate in electricity markets, because of various uncertainties in terms of prices and power generation fluctuations. Further, the exact probability distributions of random variables are difficult to specify, leading to problems and errors with respect to the bidding strategy and risk management conducted by power generation companies. To overcome these issues, a risk averse bidding strategy is proposed to allow a wind-hydro hybrid system to participate in an electricity market when only partial information is available about the underlying probability distributions of random variables. A mixture distribution structure is employed to model multiple distributional uncertainties for the hybrid system, and the worst-case conditional value-at-risk is used to measure the hybrid system’s risk considering the distributional uncertainties. This bidding strategy provides a solution that allows power generation companies to manage their distributional uncertainties in electricity markets, especially for renewable power with low accuracy forecasts. This method can estimate the benefits of forecast accuracy improvement and predictions’ probability information on generation companies. Compared with the stochastic bidding strategy, the proposed bidding strategy obtains robuster results for distributions to achieve better risk management, as illustrated by the study case.

Suggested Citation

  • Liu, Yangyang & Shen, Zhongqi & Tang, Xiaowei & Lian, Hongbo & Li, Jiarui & Gong, Jinxia, 2019. "Worst-case conditional value-at-risk based bidding strategy for wind-hydro hybrid systems under probability distribution uncertainties," Applied Energy, Elsevier, vol. 256(C).
    • Handle: RePEc:eee:appene:v:256:y:2019:i:c:s0306261919316058
    DOI: 10.1016/j.apenergy.2019.113918
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261919316058
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2019.113918?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. Tajeddini, Mohammad Amin & Rahimi-Kian, Ashkan & Soroudi, Alireza, 2014. "Risk averse optimal operation of a virtual power plant using two stage stochastic programming," Energy, Elsevier, vol. 73(C), pages 958-967.
    2. Di Somma, M. & Graditi, G. & Heydarian-Forushani, E. & Shafie-khah, M. & Siano, P., 2018. "Stochastic optimal scheduling of distributed energy resources with renewables considering economic and environmental aspects," Renewable Energy, Elsevier, vol. 116(PA), pages 272-287.
    3. Han, Shuang & Qiao, Yan-hui & Yan, Jie & Liu, Yong-qian & Li, Li & Wang, Zheng, 2019. "Mid-to-long term wind and photovoltaic power generation prediction based on copula function and long short term memory network," Applied Energy, Elsevier, vol. 239(C), pages 181-191.
    4. Shushang Zhu & Masao Fukushima, 2009. "Worst-Case Conditional Value-at-Risk with Application to Robust Portfolio Management," Operations Research, INFORMS, vol. 57(5), pages 1155-1168, October.
    5. 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.
    6. Fang, Xin & Cui, Hantao & Yuan, Haoyu & Tan, Jin & Jiang, Tao, 2019. "Distributionally-robust chance constrained and interval optimization for integrated electricity and natural gas systems optimal power flow with wind uncertainties," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    7. Al-Swaiti, Mustafa S. & Al-Awami, Ali T. & Khalid, Mohammad Waqas, 2017. "Co-optimized trading of wind-thermal-pumped storage system in energy and regulation markets," Energy, Elsevier, vol. 138(C), pages 991-1005.
    8. Ji, Ling & Huang, Guohe & Xie, Yulei & Zhou, Yong & Zhou, Jifang, 2018. "Robust cost-risk tradeoff for day-ahead schedule optimization in residential microgrid system under worst-case conditional value-at-risk consideration," Energy, Elsevier, vol. 153(C), pages 324-337.
    9. Han, Li & Jing, Huitian & Zhang, Rongchang & Gao, Zhiyu, 2019. "Wind power forecast based on improved Long Short Term Memory network," Energy, Elsevier, vol. 189(C).
    10. Jin, Yuhui & Chang, Chuei-Tin & Li, Shaojun & Jiang, Da, 2018. "On the use of risk-based Shapley values for cost sharing in interplant heat integration programs," Applied Energy, Elsevier, vol. 211(C), pages 904-920.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. He, Shuaijia & Gao, Hongjun & Chen, Zhe & Liu, Junyong, 2023. "Data-driven worst conditional value at risk energy management model of energy station," Energy, Elsevier, vol. 266(C).
    2. Wu, Shengyang & Ding, Zhaohao & Wang, Jingyu & Shi, Dongyuan, 2023. "Unveiling bidding uncertainties in electricity markets: A Bayesian deep learning framework based on accurate variational inference," Energy, Elsevier, vol. 276(C).
    3. Kempitiya, Thimal & Sierla, Seppo & De Silva, Daswin & Yli-Ojanperä, Matti & Alahakoon, Damminda & Vyatkin, Valeriy, 2020. "An Artificial Intelligence framework for bidding optimization with uncertainty in multiple frequency reserve markets," Applied Energy, Elsevier, vol. 280(C).
    4. Murphy, C.A. & Schleifer, A. & Eurek, K., 2021. "A taxonomy of systems that combine utility-scale renewable energy and energy storage technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 139(C).
    5. Qu, Kaiping & Yu, Tao & Pan, Zhenning & Zhang, Xiaoshun, 2020. "Point estimate-based stochastic robust dispatch for electricity-gas combined system under wind uncertainty using iterative convex optimization," Energy, Elsevier, vol. 211(C).
    6. Yetuo Tan & Yongming Zhi & Zhengbin Luo & Honggang Fan & Jun Wan & Tao Zhang, 2023. "Optimal Scheduling of Virtual Power Plant with Flexibility Margin Considering Demand Response and Uncertainties," Energies, MDPI, vol. 16(15), pages 1-14, August.
    7. Zhu, Ziqing & Wing Chan, Ka & Bu, Siqi & Zhou, Bin & Xia, Shiwei, 2021. "Real-Time interaction of active distribution network and virtual microgrids: Market paradigm and data-driven stakeholder behavior analysis," Applied Energy, Elsevier, vol. 297(C).
    8. Yongqi Zhao & Jiajia Chen, 2021. "A Quantitative Risk-Averse Model for Optimal Management of Multi-Source Standalone Microgrid with Demand Response and Pumped Hydro Storage," Energies, MDPI, vol. 14(9), pages 1-17, May.
    9. Li, Xiaozhu & Wang, Weiqing & Wang, Haiyun, 2021. "A novel bi-level robust game model to optimize a regionally integrated energy system with large-scale centralized renewable-energy sources in Western China," Energy, Elsevier, vol. 228(C).
    10. He, Shuaijia & Gao, Hongjun & Tang, Zao & Chen, Zhe & Jin, Xiaolong & Liu, Junyong, 2023. "Worst CVaR based energy management for generalized energy storage enabled building-integrated energy systems," Renewable Energy, Elsevier, vol. 203(C), pages 255-266.
    11. Kusi-Sarpong, Simonov & Orji, Ifeyinwa Juliet & Gupta, Himanshu & Kunc, Martin, 2021. "Risks associated with the implementation of big data analytics in sustainable supply chains," Omega, Elsevier, vol. 105(C).
    12. Cao, K.H. & Qi, H.S. & Tsai, C.H. & Woo, C.K. & Zarnikau, J., 2021. "Energy trading efficiency in the US Midcontinent electricity markets," Applied Energy, Elsevier, vol. 302(C).
    13. Li, Qirui & Yang, Zhifang & Yu, Juan & Li, Wenyuan, 2023. "Impacts of previous revenues on bidding strategies in electricity market: A quantitative analysis," Applied Energy, Elsevier, vol. 345(C).
    14. Street, Alexandre & Valladão, Davi & Lawson, André & Velloso, Alexandre, 2020. "Assessing the cost of the Hazard-Decision simplification in multistage stochastic hydrothermal scheduling," Applied Energy, Elsevier, vol. 280(C).
    15. Li, Xiaozhu & Wang, Weiqing & Wang, Haiyun, 2021. "Hybrid time-scale energy optimal scheduling strategy for integrated energy system with bilateral interaction with supply and demand," Applied Energy, Elsevier, vol. 285(C).
    16. Khaloie, Hooman & Abdollahi, Amir & Shafie-khah, Miadreza & Anvari-Moghaddam, Amjad & Nojavan, Sayyad & Siano, Pierluigi & Catalão, João P.S., 2020. "Coordinated wind-thermal-energy storage offering strategy in energy and spinning reserve markets using a multi-stage model," Applied Energy, Elsevier, vol. 259(C).

    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. Jianwei Gao & Yu Yang & Fangjie Gao & Pengcheng Liang, 2021. "Optimization of Electric Vehicles Based on Frank-Copula- GlueCVaR Combined Wind and Photovoltaic Output Scheduling Research," Energies, MDPI, vol. 14(19), pages 1-15, September.
    2. Zhang, Jiao & Li, Youping & Liu, Chunqiong & Wu, Bo & Shi, Kai, 2022. "A study of cross-correlations between PM2.5 and O3 based on Copula and Multifractal methods," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 589(C).
    3. Abdulelah Alkesaiberi & Fouzi Harrou & Ying Sun, 2022. "Efficient Wind Power Prediction Using Machine Learning Methods: A Comparative Study," Energies, MDPI, vol. 15(7), pages 1-24, March.
    4. Zhang, Fei & Li, Peng-Cheng & Gao, Lu & Liu, Yong-Qian & Ren, Xiao-Ying, 2021. "Application of autoregressive dynamic adaptive (ARDA) model in real-time wind power forecasting," Renewable Energy, Elsevier, vol. 169(C), pages 129-143.
    5. Seung Chan Jo & Young Gyu Jin & Yong Tae Yoon & Ho Chan Kim, 2021. "Methods for Integrating Extraterrestrial Radiation into Neural Network Models for Day-Ahead PV Generation Forecasting," Energies, MDPI, vol. 14(9), pages 1-18, May.
    6. 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.
    7. Xu, Fang Yuan & Tang, Rui Xin & Xu, Si Bin & Fan, Yi Liang & Zhou, Ya & Zhang, Hao Tian, 2021. "Neural network-based photovoltaic generation capacity prediction system with benefit-oriented modification," Energy, Elsevier, vol. 223(C).
    8. Wang, Jun & Cao, Junxing & Yuan, Shan & Cheng, Ming, 2021. "Short-term forecasting of natural gas prices by using a novel hybrid method based on a combination of the CEEMDAN-SE-and the PSO-ALS-optimized GRU network," Energy, Elsevier, vol. 233(C).
    9. Bingchun Liu & Shijie Zhao & Xiaogang Yu & Lei Zhang & Qingshan Wang, 2020. "A Novel Deep Learning Approach for Wind Power Forecasting Based on WD-LSTM Model," Energies, MDPI, vol. 13(18), pages 1-17, September.
    10. Khanmohammadi, Shoaib & Kizilkan, Onder & Ahmed, Faraedoon Waly, 2020. "Tri-objective optimization of a hybrid solar-assisted power-refrigeration system working with supercritical carbon dioxide," Renewable Energy, Elsevier, vol. 156(C), pages 1348-1360.
    11. Gu, Bo & Shen, Huiqiang & Lei, Xiaohui & Hu, Hao & Liu, Xinyu, 2021. "Forecasting and uncertainty analysis of day-ahead photovoltaic power using a novel forecasting method," Applied Energy, Elsevier, vol. 299(C).
    12. Zucatelli, P.J. & Nascimento, E.G.S. & Santos, A.Á.B. & Arce, A.M.G. & Moreira, D.M., 2021. "An investigation on deep learning and wavelet transform to nowcast wind power and wind power ramp: A case study in Brazil and Uruguay," Energy, Elsevier, vol. 230(C).
    13. Jafarzadeh Ghoushchi, Saeid & Manjili, Sobhan & Mardani, Abbas & Saraji, Mahyar Kamali, 2021. "An extended new approach for forecasting short-term wind power using modified fuzzy wavelet neural network: A case study in wind power plant," Energy, Elsevier, vol. 223(C).
    14. Li, Qing & Zhang, Xinyan & Ma, Tianjiao & Jiao, Chunlei & Wang, Heng & Hu, Wei, 2021. "A multi-step ahead photovoltaic power prediction model based on similar day, enhanced colliding bodies optimization, variational mode decomposition, and deep extreme learning machine," Energy, Elsevier, vol. 224(C).
    15. Lin, Yu & Yan, Yan & Xu, Jiali & Liao, Ying & Ma, Feng, 2021. "Forecasting stock index price using the CEEMDAN-LSTM model," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    16. Fekri, Mohammad Navid & Patel, Harsh & Grolinger, Katarina & Sharma, Vinay, 2021. "Deep learning for load forecasting with smart meter data: Online Adaptive Recurrent Neural Network," Applied Energy, Elsevier, vol. 282(PA).
    17. Crespo-Vazquez, Jose L. & Carrillo, C. & Diaz-Dorado, E. & Martinez-Lorenzo, Jose A. & Noor-E-Alam, Md., 2018. "A machine learning based stochastic optimization framework for a wind and storage power plant participating in energy pool market," Applied Energy, Elsevier, vol. 232(C), pages 341-357.
    18. Einolander, Johannes & Lahdelma, Risto, 2022. "Multivariate copula procedure for electric vehicle charging event simulation," Energy, Elsevier, vol. 238(PA).
    19. Yongqian Liu & Yanhui Qiao & Shuang Han & Yanping Xu & Tianxiang Geng & Tiandong Ma, 2021. "Quantitative Evaluation Methods of Cluster Wind Power Output Volatility and Source-Load Timing Matching in Regional Power Grid," Energies, MDPI, vol. 14(16), pages 1-14, August.
    20. Shahid, Farah & Zameer, Aneela & Mehmood, Ammara & Raja, Muhammad Asif Zahoor, 2020. "A novel wavenets long short term memory paradigm for wind power prediction," Applied Energy, Elsevier, vol. 269(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:256:y:2019:i:c:s0306261919316058. 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.