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A stochastic method for battery sizing with uninterruptible-power and demand shift capabilities in PV (photovoltaic) systems

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  • Tan, Chee Wei
  • Green, Tim C.
  • Hernandez-Aramburo, Carlos A.

Abstract

This paper presents a stochastic simulation using Monte Carlo technique to size a battery to meet dual objectives of demand shift at peak electricity cost times and outage protection in BIPV (building integrated photovoltaic) systems. Both functions require battery storage and the sizing of battery using numerical optimization is popularly used. However, the weather conditions, outage events and demand peaks are not deterministic in nature. Therefore, the sizing of battery storage capacity should also be based on a probabilistic approach. The Monte Carlo simulation is a rigorous method to sizing BIPV system as it takes into account a real building load profiles, the weather information and the local historical outage distribution. The simulation is split into seasonal basis for the analysis of demand shifting and outage events in order to match the seasonal weather conditions and load profiles. Five configurations of PV (photovoltaic) are assessed that cover different areas and orientations. The simulation output includes the predicted PV energy yield, the amount of energy required for demand management and outage event. Therefore, consumers can base sizing decisions on the historical data and local risk of outage statistics and the success rate of meeting the demand shift required. Finally, the economic evaluations together with the sensitivity analysis and the assessment of customers’ outage cost are discussed.

Suggested Citation

  • Tan, Chee Wei & Green, Tim C. & Hernandez-Aramburo, Carlos A., 2010. "A stochastic method for battery sizing with uninterruptible-power and demand shift capabilities in PV (photovoltaic) systems," Energy, Elsevier, vol. 35(12), pages 5082-5092.
    • Handle: RePEc:eee:energy:v:35:y:2010:i:12:p:5082-5092
    DOI: 10.1016/j.energy.2010.08.007
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    References listed on IDEAS

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    1. Diaf, S. & Diaf, D. & Belhamel, M. & Haddadi, M. & Louche, A., 2007. "A methodology for optimal sizing of autonomous hybrid PV/wind system," Energy Policy, Elsevier, vol. 35(11), pages 5708-5718, November.
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    Cited by:

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    4. Mostafa Farrokhabadi, 2019. "Data-Driven Mitigation of Energy Scheduling Inaccuracy in Renewable-Penetrated Grids: Summerside Electric Use Case," Energies, MDPI, vol. 12(12), pages 1-23, June.
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    8. Khezri, Rahmat & Mahmoudi, Amin & Aki, Hirohisa, 2022. "Optimal planning of solar photovoltaic and battery storage systems for grid-connected residential sector: Review, challenges and new perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
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    11. Virulkar, Vasudeo & Aware, Mohan & Kolhe, Mohan, 2011. "Integrated battery controller for distributed energy system," Energy, Elsevier, vol. 36(5), pages 2392-2398.
    12. Tareen, Wajahat Ullah & Mekhilef, Saad, 2016. "Transformer-less 3P3W SAPF (three-phase three-wire shunt active power filter) with line-interactive UPS (uninterruptible power supply) and battery energy storage stage," Energy, Elsevier, vol. 109(C), pages 525-536.
    13. Zahraee, S.M. & Khalaji Assadi, M. & Saidur, R., 2016. "Application of Artificial Intelligence Methods for Hybrid Energy System Optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 617-630.
    14. Musolino, V. & Pievatolo, A. & Tironi, E., 2011. "A statistical approach to electrical storage sizing with application to the recovery of braking energy," Energy, Elsevier, vol. 36(11), pages 6697-6704.
    15. Lujano-Rojas, Juan M. & Dufo-López, Rodolfo & Bernal-Agustín, José L., 2013. "Probabilistic modelling and analysis of stand-alone hybrid power systems," Energy, Elsevier, vol. 63(C), pages 19-27.
    16. Yang, Yuqing & Bremner, Stephen & Menictas, Chris & Kay, Merlinde, 2018. "Battery energy storage system size determination in renewable energy systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 109-125.
    17. Carlo Bianca, 2022. "On the Modeling of Energy-Multisource Networks by the Thermostatted Kinetic Theory Approach: A Review with Research Perspectives," Energies, MDPI, vol. 15(21), pages 1-22, October.
    18. Kaplani, E. & Kaplanis, S., 2012. "A stochastic simulation model for reliable PV system sizing providing for solar radiation fluctuations," Applied Energy, Elsevier, vol. 97(C), pages 970-981.
    19. Erdinc, O. & Uzunoglu, M., 2012. "Optimum design of hybrid renewable energy systems: Overview of different approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(3), pages 1412-1425.
    20. Sultan, Ali J. & Ingham, Derek B. & Hughes, Kevin J. & Ma, Lin & Pourkashanian, Mohamed, 2021. "Optimization and performance enhancement of concentrating solar power in a hot and arid desert environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    21. Li, Jianwei & Yang, Qingqing & Robinson, Francis. & Liang, Fei & Zhang, Min & Yuan, Weijia, 2017. "Design and test of a new droop control algorithm for a SMES/battery hybrid energy storage system," Energy, Elsevier, vol. 118(C), pages 1110-1122.
    22. Spiliotis, Konstantinos & Gonçalves, Juliana E. & Saelens, Dirk & Baert, Kris & Driesen, Johan, 2020. "Electrical system architectures for building-integrated photovoltaics: A comparative analysis using a modelling framework in Modelica," Applied Energy, Elsevier, vol. 261(C).

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