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Co-optimized trading of wind-thermal-pumped storage system in energy and regulation markets

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  • Al-Swaiti, Mustafa S.
  • Al-Awami, Ali T.
  • Khalid, Mohammad Waqas

Abstract

Trading of wind energy in day-ahead electricity markets entails significant risks. Wind traders are, therefore, interested in mitigating their trading risks in a number of ways. This work suggests addressing this issue in a comprehensive manner by coordinating the trading of wind resources with thermal resources and a pumped-hydro storage system (PHS) and co-optimizing the trading in energy and regulation markets. The trading strategy is modeled as a mixed integer linear stochastic program that takes into account several uncertain parameters, including wind power outputs, prices for energy, balancing, and regulation, and regulation deployments. In this optimization, the trader’s attitude toward risks is controlled by using the metric of conditional value at risk (CVaR). Simulation results show that coordinated and co-optimized trading of the generation resources and PHS significantly improve the total expected profits and mitigates trader’s risks.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:138:y:2017:i:c:p:991-1005
    DOI: 10.1016/j.energy.2017.07.101
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    References listed on IDEAS

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    1. Ahmadi, Abdollah & Charwand, Mansour & Siano, Pierluigi & Nezhad, Ali Esmaeel & Sarno, Debora & Gitizadeh, Mohsen & Raeisi, Fatima, 2016. "A novel two-stage stochastic programming model for uncertainty characterization in short-term optimal strategy for a distribution company," Energy, Elsevier, vol. 117(P1), pages 1-9.
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    Cited by:

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    2. Chen, Sheng & Wang, Jing & Zhang, Jian & Yu, Xiaodong & He, Wei, 2020. "Transient behavior of two-stage load rejection for multiple units system in pumped storage plants," Renewable Energy, Elsevier, vol. 160(C), pages 1012-1022.
    3. Lasemi, Mohammad Ali & Arabkoohsar, Ahmad, 2020. "Optimal operating strategy of high-temperature heat and power storage system coupled with a wind farm in energy market," Energy, Elsevier, vol. 210(C).
    4. Karakoyun, Ece Cigdem & Avci, Harun & Kocaman, Ayse Selin & Nadar, Emre, 2023. "Deviations from commitments: Markov decision process formulations for the role of energy storage," International Journal of Production Economics, Elsevier, vol. 255(C).
    5. 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).
    6. Abbasi, Mohammad Hossein & Taki, Mehrdad & Rajabi, Amin & Li, Li & Zhang, Jiangfeng, 2019. "Coordinated operation of electric vehicle charging and wind power generation as a virtual power plant: A multi-stage risk constrained approach," Applied Energy, Elsevier, vol. 239(C), pages 1294-1307.
    7. 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.
    8. Dranka, Géremi Gilson & Ferreira, Paula & Vaz, A. Ismael F., 2021. "A review of co-optimization approaches for operational and planning problems in the energy sector," Applied Energy, Elsevier, vol. 304(C).
    9. Díaz, Guzmán & Coto, José & Gómez-Aleixandre, Javier, 2019. "Optimal operation value of combined wind power and energy storage in multi-stage electricity markets," Applied Energy, Elsevier, vol. 235(C), pages 1153-1168.
    10. Tian, Yuyu & Chang, Jianxia & Wang, Yimin & Wang, Xuebin & Zhao, Mingzhe & Meng, Xuejiao & Guo, Aijun, 2022. "A method of short-term risk and economic dispatch of the hydro-thermal-wind-PV hybrid system considering spinning reserve requirements," Applied Energy, Elsevier, vol. 328(C).
    11. Ding, Yihong & Tan, Qinliang & Shan, Zijing & Han, Jian & Zhang, Yimei, 2023. "A two-stage dispatching optimization strategy for hybrid renewable energy system with low-carbon and sustainability in ancillary service market," Renewable Energy, Elsevier, vol. 207(C), pages 647-659.
    12. El Hage, Hicham & Herez, Amal & Ramadan, Mohamad & Bazzi, Hassan & Khaled, Mahmoud, 2018. "An investigation on solar drying: A review with economic and environmental assessment," Energy, Elsevier, vol. 157(C), pages 815-829.
    13. Al-Lawati, Razan A.H. & Crespo-Vazquez, Jose L. & Faiz, Tasnim Ibn & Fang, Xin & Noor-E-Alam, Md., 2021. "Two-stage stochastic optimization frameworks to aid in decision-making under uncertainty for variable resource generators participating in a sequential energy market," Applied Energy, Elsevier, vol. 292(C).
    14. Sriyakul, Thanaporn & Jermsittiparsert, Kittisak, 2021. "Risk-controlled economic performance of compressed air energy storage and wind generation in day-ahead, intraday and balancing markets," Renewable Energy, Elsevier, vol. 165(P1), pages 182-193.
    15. 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).
    16. Zhou, Yizhou & Wei, Zhinong & Sun, Guoqiang & Cheung, Kwok W. & Zang, Haixiang & Chen, Sheng, 2018. "A robust optimization approach for integrated community energy system in energy and ancillary service markets," Energy, Elsevier, vol. 148(C), pages 1-15.

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