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Optimal techno-economic multi-level energy management of renewable-based DC microgrid for commercial buildings applications

Author

Listed:
  • Ferahtia, Seydali
  • Rezk, Hegazy
  • Olabi, A.G.
  • Alhumade, Hesham
  • Bamufleh, Hisham S.
  • Doranehgard, Mohammad Hossein
  • Abdelkareem, Mohammad Ali

Abstract

An optimal techno-economic multi-level energy management strategy (EMS) is designed for a DC microgrid applied to a commercial building. The proposed power system is a grid-connected microgrid composed of a fuel cell (FC) system, a photovoltaic (PV) array, and a battery storage unit. The interaction between various types of power sources may lead to stability problems. Moreover, providing an uninterruptible and high-quality power supply, reducing operating costs, and maximizing system efficiency are essential factors. This paper presents a techno-economic EMS, one of the most significant objectives for the following years. The proposed EMS comprises two levels for minimizing the operating cost and supplying the load and another control level based on flatness control theory to improve the power quality. The first level is based on one of the following EMSs: State Machine Control (SMC), Equivalent Consumption Minimization Strategy (ECMS), and External Energy Maximization Strategy (EEMS) to improve efficiency and reduce the required power. The second level is based on the economic dispatching algorithm (EDA) to reduce the operating cost and ensure a UPS. Finally, the system performance is elaborated; the simulation results prove the superiority of the EEMS-EDA combination, where the cost-saving can be enhanced by 3.47%, and the efficiency is about 80.44%.

Suggested Citation

  • Ferahtia, Seydali & Rezk, Hegazy & Olabi, A.G. & Alhumade, Hesham & Bamufleh, Hisham S. & Doranehgard, Mohammad Hossein & Abdelkareem, Mohammad Ali, 2022. "Optimal techno-economic multi-level energy management of renewable-based DC microgrid for commercial buildings applications," Applied Energy, Elsevier, vol. 327(C).
    • Handle: RePEc:eee:appene:v:327:y:2022:i:c:s030626192201279x
    DOI: 10.1016/j.apenergy.2022.120022
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    3. Wang, Ting & Zhang, Chunyan & Hao, Zhiguo & Monti, Antonello & Ponci, Ferdinanda, 2023. "Data-driven fault detection and isolation in DC microgrids without prior fault data: A transfer learning approach," Applied Energy, Elsevier, vol. 336(C).
    4. Topa, A.O. & Gil, J.D. & Álvarez, J.D. & Torres, J.L., 2024. "A hybrid-MPC based energy management system with time series constraints for a bioclimatic building," Energy, Elsevier, vol. 287(C).

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