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Study on method of electricity and heat storage planning based on energy demand and tidal flow velocity forecasts for a tidal microgrid

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  • Obara, Shin’ya
  • Morizane, Yuta
  • Morel, Jorge

Abstract

The rapid tidal current near a lake inlet is transformed into electrical energy with Darius-type hydraulic turbine generators. When the tidal power generation is insufficient, the stored excess electric power generated from midnight to early morning of a representative day is used. The balance of energy supply and demand for all sampling events in a representative day must be predicted very accurately in a system with energy storage. In this study, electric power and heat demand are forecasted on the basis of weather data obtained from the Internet, and the corresponding values are used to plan the storage of electricity and heat from midnight to early morning. The results of the case analysis show the influence of the economic efficiency of the heating system, the capacity of the tidal power generator, the prediction error of the tidal power generator, and the insulation efficiency (Q-value) on the energy cost. Optimization of the introduced simulation model was considered. The objective functions of optimization were minimization of operation cost and facilities cost of the simulation model.

Suggested Citation

  • Obara, Shin’ya & Morizane, Yuta & Morel, Jorge, 2013. "Study on method of electricity and heat storage planning based on energy demand and tidal flow velocity forecasts for a tidal microgrid," Applied Energy, Elsevier, vol. 111(C), pages 358-373.
    • Handle: RePEc:eee:appene:v:111:y:2013:i:c:p:358-373
    DOI: 10.1016/j.apenergy.2013.05.018
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    References listed on IDEAS

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    Cited by:

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    2. Haas, J. & Cebulla, F. & Cao, K. & Nowak, W. & Palma-Behnke, R. & Rahmann, C. & Mancarella, P., 2017. "Challenges and trends of energy storage expansion planning for flexibility provision in low-carbon power systems – a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 603-619.
    3. Shang, Ce & Ge, Yuyou & Zhai, Suwei & Huo, Chao & Li, Wenyun, 2023. "Combined heat and power storage planning," Energy, Elsevier, vol. 279(C).
    4. Oh, Se-Young & Binns, Michael & Yeo, Yeong-Koo & Kim, Jin-Kuk, 2014. "Improving energy efficiency for local energy systems," Applied Energy, Elsevier, vol. 131(C), pages 26-39.
    5. Faridnia, N. & Habibi, D. & Lachowicz, S. & Kavousifard, A., 2019. "Optimal scheduling in a microgrid with a tidal generation," Energy, Elsevier, vol. 171(C), pages 435-443.
    6. Roy, Sukanta & Saha, Ujjwal K., 2015. "Wind tunnel experiments of a newly developed two-bladed Savonius-style wind turbine," Applied Energy, Elsevier, vol. 137(C), pages 117-125.
    7. Zhang, Jingrui & Wu, Yihong & Guo, Yiran & Wang, Bo & Wang, Hengyue & Liu, Houde, 2016. "A hybrid harmony search algorithm with differential evolution for day-ahead scheduling problem of a microgrid with consideration of power flow constraints," Applied Energy, Elsevier, vol. 183(C), pages 791-804.

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