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Optimal scheduling in a microgrid with a tidal generation

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  • Faridnia, N.
  • Habibi, D.
  • Lachowicz, S.
  • Kavousifard, A.

Abstract

Tidal energy is one of the most abundant renewable energy sources. Due to its intermittent nature, the integration of tidal energy within a Microgrid (MG) requires appropriate energy management strategies. Some important considerations are tidal energy generation prediction, energy storage requirements, load profile and demand within the Microgrid, and energy pricing structures. This paper considers an MG scenario that includes a small scale tidal farm near Darwin in north of Australia. A tidal power prediction model is introduced using tidal current speed data. Then, for a fixed-size energy storage system (ESS), an optimal scheduling strategy is devised using Particle Swarm Optimization (PSO) to achieve minimum operating costs in the MG. The work presented in this paper provides a framework for the planning of MGs that include tidal generation in their energy mix.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:171:y:2019:i:c:p:435-443
    DOI: 10.1016/j.energy.2018.12.079
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    Cited by:

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    2. Md Shafiullah & Akib Mostabe Refat & Md Ershadul Haque & Dewan Mabrur Hasan Chowdhury & Md Sanower Hossain & Abdullah G. Alharbi & Md Shafiul Alam & Amjad Ali & Shorab Hossain, 2022. "Review of Recent Developments in Microgrid Energy Management Strategies," Sustainability, MDPI, vol. 14(22), pages 1-30, November.
    3. Younes Zahraoui & Ibrahim Alhamrouni & Saad Mekhilef & M. Reyasudin Basir Khan & Mehdi Seyedmahmoudian & Alex Stojcevski & Ben Horan, 2021. "Energy Management System in Microgrids: A Comprehensive Review," Sustainability, MDPI, vol. 13(19), pages 1-33, September.
    4. Tayab, Usman Bashir & Lu, Junwei & Yang, Fuwen & AlGarni, Tahani Saad & Kashif, Muhammad, 2021. "Energy management system for microgrids using weighted salp swarm algorithm and hybrid forecasting approach," Renewable Energy, Elsevier, vol. 180(C), pages 467-481.

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