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Preventive maintenance scheduling of multi energy microgrid to enhance the resiliency of system

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  • Gargari, Milad Zamani
  • Hagh, Mehrdad Tarafdar
  • Zadeh, Saeid Ghassem

Abstract

Recently, the application of multi-energy infrastructure has been widespread since the emergence of new technologies in this field. Accordingly, modeling and providing energy supply for consumers at an acceptable level of security is vital. It is also important for sustainable development in all directions to develop and support resources and infrastructures against natural disasters. In this study, the preventive maintenance scheduling of multi-energy microgrid is introduced to enhance the resiliency of system in unpredictable circumstances. The proposed formulation intends to make the best use of the profitable levels of freedom related to the maintenance and operational variables by considering the feasible operating region of components. By the use of sequential approach the near-optimal solutions in operation and maintenance schedule is obtained to meet the resiliency requirement. The exact model of electricity and natural gas is adopted to validate the simulation results. Three different cases is examined to prove the effectiveness of the proposed approach in standard multi-energy microgrid. According to the results, the utilization of the proposed method improves the resiliency of system in critical conditions.

Suggested Citation

  • Gargari, Milad Zamani & Hagh, Mehrdad Tarafdar & Zadeh, Saeid Ghassem, 2021. "Preventive maintenance scheduling of multi energy microgrid to enhance the resiliency of system," Energy, Elsevier, vol. 221(C).
    • Handle: RePEc:eee:energy:v:221:y:2021:i:c:s0360544221000311
    DOI: 10.1016/j.energy.2021.119782
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    Cited by:

    1. Zhong, Zhiming & Fan, Neng & Wu, Lei, 2024. "Multistage Stochastic optimization for mid-term integrated generation and maintenance scheduling of cascaded hydroelectric system with renewable energy uncertainty," European Journal of Operational Research, Elsevier, vol. 318(1), pages 179-199.
    2. Yang, Xiao & Li, Yuanzheng & Zhao, Yong & Yu, Yaowen & Lian, Yicheng & Hao, Guokai & Jiang, Lin, 2023. "Data-driven nested robust optimization for generation maintenance scheduling considering temporal correlation," Energy, Elsevier, vol. 278(C).
    3. Zamani Gargari, Milad & Tarafdar Hagh, Mehrdad & Ghassem Zadeh, Saeid, 2023. "Preventive scheduling of a multi-energy microgrid with mobile energy storage to enhance the resiliency of the system," Energy, Elsevier, vol. 263(PC).
    4. Toubeau, Jean-François & Pardoen, Lorie & Hubert, Louis & Marenne, Nicolas & Sprooten, Jonathan & De Grève, Zacharie & Vallée, François, 2022. "Machine learning-assisted outage planning for maintenance activities in power systems with renewables," Energy, Elsevier, vol. 238(PC).
    5. Abulanwar, Sayed & Ghanem, Abdelhady & Rizk, Mohammad E.M. & Hu, Weihao, 2021. "Adaptive synergistic control strategy for a hybrid AC/DC microgrid during normal operation and contingencies," Applied Energy, Elsevier, vol. 304(C).
    6. Zuo, Jian & Cadet, Catherine & Li, Zhongliang & Bérenguer, Christophe & Outbib, Rachid, 2024. "A deterioration-aware energy management strategy for the lifetime improvement of a multi-stack fuel cell system subject to a random dynamic load," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    7. Sandelic, Monika & Peyghami, Saeed & Sangwongwanich, Ariya & Blaabjerg, Frede, 2022. "Reliability aspects in microgrid design and planning: Status and power electronics-induced challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).

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