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A reliability-aware chance-constrained battery sizing method for island microgrid

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  • Huo, Da
  • Santos, Marcos
  • Sarantakos, Ilias
  • Resch, Markus
  • Wade, Neal
  • Greenwood, David

Abstract

Island Microgrids can coordinate local energy resources, provide post-fault reliability improvements for local customers, and aggregate local power and energy resources to offer services to the wider system. A crucial component of an Island Microgrid is the battery energy storage system, which can manage local imbalances, alleviate constraints, and improve reliability by enabling post-fault islanding. A planning and sizing method is required to quantify and maximize the benefits of battery energy storage while avoiding over-investment and under-utilization. This paper combines comprehensive reliability assessment with chance-constrained convex optimization, via second-order cone programming, to optimally size energy storage within an Island Microgrid. Chance constraints are applied to the battery state-of-charge to avoid sizing the energy storage to accommodate extreme cases of uncertainty, avoiding uneconomic investment. The probability of reaching a state-of-charge constraint also indicates the likelihood that the battery energy storage system will be unable to facilitate island operation in the event of an outage, which affects the Island Microgrid reliability. The method is demonstrated on a real Austrian distribution network as part of the MERLON project. Results illustrate that an optimal trade-off can be identified between system reliability and operating cost when the probability of violating the chance constraints is 4.8%.

Suggested Citation

  • Huo, Da & Santos, Marcos & Sarantakos, Ilias & Resch, Markus & Wade, Neal & Greenwood, David, 2022. "A reliability-aware chance-constrained battery sizing method for island microgrid," Energy, Elsevier, vol. 251(C).
    • Handle: RePEc:eee:energy:v:251:y:2022:i:c:s0360544222008817
    DOI: 10.1016/j.energy.2022.123978
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    References listed on IDEAS

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    1. Ghadi, Mojtaba Jabbari & Rajabi, Amin & Ghavidel, Sahand & Azizivahed, Ali & Li, Li & Zhang, Jiangfeng, 2019. "From active distribution systems to decentralized microgrids: A review on regulations and planning approaches based on operational factors," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    2. El-Bidairi, Kutaiba S. & Nguyen, Hung Duc & Mahmoud, Thair S. & Jayasinghe, S.D.G. & Guerrero, Josep M., 2020. "Optimal sizing of Battery Energy Storage Systems for dynamic frequency control in an islanded microgrid: A case study of Flinders Island, Australia," Energy, Elsevier, vol. 195(C).
    3. Shang, Ce & Srinivasan, Dipti & Reindl, Thomas, 2016. "Generation-scheduling-coupled battery sizing of stand-alone hybrid power systems," Energy, Elsevier, vol. 114(C), pages 671-682.
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    1. Nataliia Shamarova & Konstantin Suslov & Pavel Ilyushin & Ilia Shushpanov, 2022. "Review of Battery Energy Storage Systems Modeling in Microgrids with Renewables Considering Battery Degradation," Energies, MDPI, vol. 15(19), pages 1-18, September.
    2. Bingyin Lei & Yue Ren & Huiyu Luan & Ruonan Dong & Xiuyuan Wang & Junli Liao & Shu Fang & Kaiye Gao, 2023. "A Review of Optimization for System Reliability of Microgrid," Mathematics, MDPI, vol. 11(4), pages 1-30, February.
    3. Kassab, Fadi Agha & Celik, Berk & Locment, Fabrice & Sechilariu, Manuela & Liaquat, Sheroze & Hansen, Timothy M., 2024. "Optimal sizing and energy management of a microgrid: A joint MILP approach for minimization of energy cost and carbon emission," Renewable Energy, Elsevier, vol. 224(C).
    4. Zhang, Hui & Wang, Jiye & Zhao, Xiongwen & Yang, Jingqi & Bu sinnah, Zainab Ali, 2023. "Modeling a hydrogen-based sustainable multi-carrier energy system using a multi-objective optimization considering embedded joint chance constraints," Energy, Elsevier, vol. 278(C).

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