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Combining a dynamic battery model with high-resolution smart grid data to assess microgrid islanding lifetime

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  • Fares, Robert L.
  • Webber, Michael E.

Abstract

In this paper, we use experimental data collected from an Austin, Texas smart grid test bed with a system-level battery energy storage model to assess the lifetime of batteries in a microgrid operating in islanded mode during a distribution-level outage. We consider a hypothetical microgrid consisting of 21 single-family detached homes and three transformer-level community energy storage (CES) battery units ranging in size from 25kWh to 75kWh. To describe the performance of CES batteries, we implement a dynamic behavioral circuit model capable of describing voltage transients and rate-capacity effects. We use one-minute electricity production and consumption data collected from the smart grid test bed in 2012 to assess how the timing of an electric outage affects the islanding lifetime of a residential microgrid. We contrast our results with the average outage duration reported by U.S. electric utilities to quantify how often a residential microgrid could withstand a typical outage. Our results show that increasing the amount of rooftop PV in a residential microgrid does not significantly increase how often it can withstand an average-duration outage. However, combining PV with CES extends the median islanding lifetime by up to 11.6h during morning outages. Based on our results, 50kWh CES provides the best tradeoff between the cost of a CES system and its reliability benefit, allowing downstream loads to withstand an average-duration outage approximately 93% of the time.

Suggested Citation

  • Fares, Robert L. & Webber, Michael E., 2015. "Combining a dynamic battery model with high-resolution smart grid data to assess microgrid islanding lifetime," Applied Energy, Elsevier, vol. 137(C), pages 482-489.
    • Handle: RePEc:eee:appene:v:137:y:2015:i:c:p:482-489
    DOI: 10.1016/j.apenergy.2014.04.049
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    1. Chen, Yen-Haw & Lu, Su-Ying & Chang, Yung-Ruei & Lee, Ta-Tung & Hu, Ming-Che, 2013. "Economic analysis and optimal energy management models for microgrid systems: A case study in Taiwan," Applied Energy, Elsevier, vol. 103(C), pages 145-154.
    2. Parisio, Alessandra & Rikos, Evangelos & Tzamalis, George & Glielmo, Luigi, 2014. "Use of model predictive control for experimental microgrid optimization," Applied Energy, Elsevier, vol. 115(C), pages 37-46.
    3. Marzband, Mousa & Sumper, Andreas & Ruiz-Álvarez, Albert & Domínguez-García, José Luis & Tomoiagă, Bogdan, 2013. "Experimental evaluation of a real time energy management system for stand-alone microgrids in day-ahead markets," Applied Energy, Elsevier, vol. 106(C), pages 365-376.
    4. Manfren, Massimiliano & Caputo, Paola & Costa, Gaia, 2011. "Paradigm shift in urban energy systems through distributed generation: Methods and models," Applied Energy, Elsevier, vol. 88(4), pages 1032-1048, April.
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    2. Chmielewski, Adrian & Gumiński, Robert & Mączak, Jędrzej & Radkowski, Stanisław & Szulim, Przemysław, 2016. "Aspects of balanced development of RES and distributed micro-cogeneration use in Poland: Case study of a µCHP with Stirling engine," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 930-952.
    3. Asaad, Mohammad & Ahmad, Furkan & Alam, Mohammad Saad & Sarfraz, Mohammad, 2021. "Smart grid and Indian experience: A review," Resources Policy, Elsevier, vol. 74(C).
    4. Ahmadipour, Masoud & Hizam, Hashim & Othman, Mohammad Lutfi & Radzi, Mohd Amran Mohd & Murthy, Avinash Srikanta, 2018. "Islanding detection technique using Slantlet Transform and Ridgelet Probabilistic Neural Network in grid-connected photovoltaic system," Applied Energy, Elsevier, vol. 231(C), pages 645-659.
    5. Korkas, Christos D. & Baldi, Simone & Michailidis, Iakovos & Kosmatopoulos, Elias B., 2016. "Occupancy-based demand response and thermal comfort optimization in microgrids with renewable energy sources and energy storage," Applied Energy, Elsevier, vol. 163(C), pages 93-104.

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