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Optimal design of a storage system coupled with intermittent renewables

Author

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  • Bridier, Laurent
  • David, Mathieu
  • Lauret, Philippe

Abstract

In this paper, two ways of increasing the integration of wind and solar energy into the electricity grid through energy storage are analyzed. The first service (S1) to the electricity grid is related to a smoothed and hourly scheduled daily production while the second one (S2) concerns a constant and guaranteed minimal production. A power bid, based on meteorological forecasts, is transmitted a day ahead by the producer to the utility grid operator. This leads to a yearly default time rate for which the actual power supplied does not meet the announcement within a given tolerance. The modelling approach developed in this study enables to infer the optimal operation of the system and more specifically the optimal size of the energy storage, aiming at reducing the default time rate (DTR) under 5%. The simulations consider PV or wind with storage systems having discharge time in the range of minutes. Two real test cases are examined: Guadeloupe Island for wind and Reunion Island for PV. The results show that both of the two services can be achieved under specific conditions and that an optimal day-ahead power bid with a 2% DTR is possible with a storage capacity of 1 MWh per installed MWp. In addition, a linear strategy of forecasting this optimal power is highly correlated to the precision of upstream meteorological forecast.

Suggested Citation

  • Bridier, Laurent & David, Mathieu & Lauret, Philippe, 2014. "Optimal design of a storage system coupled with intermittent renewables," Renewable Energy, Elsevier, vol. 67(C), pages 2-9.
    • Handle: RePEc:eee:renene:v:67:y:2014:i:c:p:2-9
    DOI: 10.1016/j.renene.2013.11.048
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    Citations

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    Cited by:

    1. Wu, Yunna & Zhang, Ting & Xu, Chuanbo & Zhang, Xiaoyu & Ke, Yiming & Chu, Han & Xu, Ruhang, 2019. "Location selection of seawater pumped hydro storage station in China based on multi-attribute decision making," Renewable Energy, Elsevier, vol. 139(C), pages 410-425.
    2. Mason, I.G., 2015. "Comparative impacts of wind and photovoltaic generation on energy storage for small islanded electricity systems," Renewable Energy, Elsevier, vol. 80(C), pages 793-805.
    3. Bridier, Laurent & Hernández-Torres, David & David, Mathieu & Lauret, Phillipe, 2016. "A heuristic approach for optimal sizing of ESS coupled with intermittent renewable sources systems," Renewable Energy, Elsevier, vol. 91(C), pages 155-165.
    4. Shivashankar, S. & Mekhilef, Saad & Mokhlis, Hazlie & Karimi, M., 2016. "Mitigating methods of power fluctuation of photovoltaic (PV) sources – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 1170-1184.
    5. Zheng, Xuyue & Qiu, Yuwei & Zhan, Xiangyan & Zhu, Xingyi & Keirstead, James & Shah, Nilay & Zhao, Yingru, 2017. "Optimization based planning of urban energy systems: Retrofitting a Chinese industrial park as a case-study," Energy, Elsevier, vol. 139(C), pages 31-41.
    6. Philippe Lauret & Mathieu David & Hugo T. C. Pedro, 2017. "Probabilistic Solar Forecasting Using Quantile Regression Models," Energies, MDPI, vol. 10(10), pages 1-17, October.
    7. Yi Tang & Jing Ling & Tingting Ma & Ning Chen & Xiaofeng Liu & Bingtuan Gao, 2017. "A Game Theoretical Approach Based Bidding Strategy Optimization for Power Producers in Power Markets with Renewable Electricity," Energies, MDPI, vol. 10(5), pages 1-16, May.
    8. Oliveira, Isabela Alves de & Schaeffer, Roberto & Szklo, Alexandre, 2017. "The impact of energy storage in power systems: The case of Brazil’s Northeastern grid," Energy, Elsevier, vol. 122(C), pages 50-61.
    9. Jannet Jamii & Mohamed Trabelsi & Majdi Mansouri & Mohamed Fouazi Mimouni & Wasfi Shatanawi, 2022. "Non-Linear Programming-Based Energy Management for a Wind Farm Coupled with Pumped Hydro Storage System," Sustainability, MDPI, vol. 14(18), pages 1-17, September.

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