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Integrating reliability and resilience to support the transition from passive distribution grids to islanding microgrids

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  • Wu, Raphael
  • Sansavini, Giovanni

Abstract

Reliability and resilience are the main drivers for the transition of distribution networks from passive systems to active microgrids; as such, quantifying the potential benefits of microgrids in the design phase can support the transition of passive distribution networks into microgrids. To support this transition, this paper presents a mathematical optimization model which integrates techno-economic, resilience and reliability objectives. Storage and distributed generation are optionally installed to complement renewable generation, enabling the microgrid to supply priority demands during stochastic islanding events with uncertain duration. Islanding due to external events is combined with a detailed model of internal faults for comprehensive quantification and optimization of microgrid resilience and reliability. Minimizing the interruption costs yields optimal capacities and placements of distributed energy resources and new lines for reconfiguration. The proposed method produces microgrid designs with up to 95% reliability and resilience gain and moderate cost increase in two benchmark distribution networks using data from the United States Department of Energy. The developed methodology is scalable to large networks owing to the tailored Column-and-Constraint-Generation approach, reducing the computational time by 92% compared to state-of-the-art Benders decomposition for a 123 bus network.

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  • Wu, Raphael & Sansavini, Giovanni, 2020. "Integrating reliability and resilience to support the transition from passive distribution grids to islanding microgrids," Applied Energy, Elsevier, vol. 272(C).
    • Handle: RePEc:eee:appene:v:272:y:2020:i:c:s0306261920307662
    DOI: 10.1016/j.apenergy.2020.115254
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    1. Javad Najafi & Ali Peiravi & Amjad Anvari-Moghaddam, 2020. "Enhancing Integrated Power and Water Distribution Networks Seismic Resilience Leveraging Microgrids," Sustainability, MDPI, vol. 12(6), pages 1-16, March.
    2. Björn Nykvist & Måns Nilsson, 2015. "Rapidly falling costs of battery packs for electric vehicles," Nature Climate Change, Nature, vol. 5(4), pages 329-332, April.
    3. Gandhi, Oktoviano & Rodríguez-Gallegos, Carlos D. & Zhang, Wenjie & Srinivasan, Dipti & Reindl, Thomas, 2018. "Economic and technical analysis of reactive power provision from distributed energy resources in microgrids," Applied Energy, Elsevier, vol. 210(C), pages 827-841.
    4. Adefarati, T. & Bansal, R.C., 2019. "Reliability, economic and environmental analysis of a microgrid system in the presence of renewable energy resources," Applied Energy, Elsevier, vol. 236(C), pages 1089-1114.
    5. Rahmaniani, Ragheb & Crainic, Teodor Gabriel & Gendreau, Michel & Rei, Walter, 2017. "The Benders decomposition algorithm: A literature review," European Journal of Operational Research, Elsevier, vol. 259(3), pages 801-817.
    6. Bullich-Massagué, Eduard & Díaz-González, Francisco & Aragüés-Peñalba, Mònica & Girbau-Llistuella, Francesc & Olivella-Rosell, Pol & Sumper, Andreas, 2018. "Microgrid clustering architectures," Applied Energy, Elsevier, vol. 212(C), pages 340-361.
    7. Hussain, Akhtar & Bui, Van-Hai & Kim, Hak-Man, 2019. "Microgrids as a resilience resource and strategies used by microgrids for enhancing resilience," Applied Energy, Elsevier, vol. 240(C), pages 56-72.
    8. Mavromatidis, Georgios & Orehounig, Kristina & Carmeliet, Jan, 2018. "Uncertainty and global sensitivity analysis for the optimal design of distributed energy systems," Applied Energy, Elsevier, vol. 214(C), pages 219-238.
    9. Jufri, Fauzan Hanif & Widiputra, Victor & Jung, Jaesung, 2019. "State-of-the-art review on power grid resilience to extreme weather events: Definitions, frameworks, quantitative assessment methodologies, and enhancement strategies," Applied Energy, Elsevier, vol. 239(C), pages 1049-1065.
    10. Zhou, Yutian & Panteli, Mathaios & Moreno, Rodrigo & Mancarella, Pierluigi, 2018. "System-level assessment of reliability and resilience provision from microgrids," Applied Energy, Elsevier, vol. 230(C), pages 374-392.
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