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Optimal operating strategy of high-temperature heat and power storage system coupled with a wind farm in energy market

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  • Lasemi, Mohammad Ali
  • Arabkoohsar, Ahmad

Abstract

Due to the fluctuating nature of solar and wind power plants, they are mostly accompanied by energy storage units to be able to offer dispatchable energy output. One of the main challenges of energy storage units in renewable power plants, however, is determining an efficient and optimal energy trading strategy as the majority of the electricity should be traded in the day-ahead market. As a result, the plants are imposed on huge penalties due to less- or over-supply compared to that bided on the day before. This work presents a novel model to optimize the bidding strategy of a wind farm coupled with a high-temperature heat and power energy storage system (based on different designs) in the energy market. This novel storage technology is highly efficient and offers cogeneration of heat and electricity. The proposed problem determines the best bidding value to maximize the total profit of energy sales from a monthly perspective. The case study includes a real wind farm and Danish energy market. The results reveal the importance of having a precise model of marketing for such plants and the effect of small deviation from the optimal marketing strategy on the net annual revenue of the power plant.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:210:y:2020:i:c:s0360544220316534
    DOI: 10.1016/j.energy.2020.118545
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    Cited by:

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    2. He, Yang & MengWang, & Chen, Haisheng & Xu, Yujie & Deng, Jianqiang, 2021. "Thermodynamic research on compressed air energy storage system with turbines under sliding pressure operation," Energy, Elsevier, vol. 222(C).
    3. Lu, Xiaohui & Yang, Yang & Wang, Peifang & Fan, Yiming & Yu, Fangzhong & Zafetti, Nicholas, 2021. "A new converged Emperor Penguin Optimizer for biding strategy in a day-ahead deregulated market clearing price: A case study in China," Energy, Elsevier, vol. 227(C).
    4. Yang, Peiwen & Dong, Jun & Lin, Jin & Liu, Yao & Fang, Debin, 2021. "Analysis of offering behavior of generation-side integrated energy aggregator in electricity market:A Bayesian evolutionary approach," Energy, Elsevier, vol. 228(C).
    5. Ghandehariun, Samane & Ghandehariun, Amir M. & Ziabari, Nima Bahrami, 2023. "Performance prediction and optimization of a hybrid renewable-energy-based multigeneration system using machine learning," Energy, Elsevier, vol. 282(C).
    6. Jun Dong & Dongran Liu & Xihao Dou & Bo Li & Shiyao Lv & Yuzheng Jiang & Tongtao Ma, 2021. "Key Issues and Technical Applications in the Study of Power Markets as the System Adapts to the New Power System in China," Sustainability, MDPI, vol. 13(23), pages 1-29, December.

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