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A simulation approach to sizing batteries for integration with net-zero energy residential buildings

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  • Heine, Karl
  • Thatte, Amogh
  • Tabares-Velasco, Paulo Cesar

Abstract

Utilities have greatly increased the use of time-of-use (TOU) rate structures for residential customers to more closely match the cost of delivered electricity as well as demand tariffs to incentivize a temporal shift in residential energy use. However current research tends to focus on a particular climate or location. This study analyzes four residences across four different climate zones in Arizona and explores the value of adding battery storage to a net-zero energy (NZE) photovoltaic (PV) system. Using the National Renewable Energy Lab's Building Optimization (BEopt) and System Advisor Model (SAM) tools, this study performs parametric analysis by varying battery size to determine a capacity that produces a maximum net present value (NPV). A sensitivity analysis on three possible future battery price trends is also performed. This study finds the NZE PV systems are only able to mitigate 37–44% of the peak electricity purchases and 4–12% of demand charges due to mismatch between solar potential and on-peak hours. Adding large batteries, 1.5–1.6 times larger than required to meet the annual peak energy purchase requirements, provides a maximum NPV for PV-battery systems at locations with favorable utility rates. We conclude that the best economics are achieved, at both current and expected future battery prices, when batteries are sized to never require replacement during the PV system lifetime.

Suggested Citation

  • Heine, Karl & Thatte, Amogh & Tabares-Velasco, Paulo Cesar, 2019. "A simulation approach to sizing batteries for integration with net-zero energy residential buildings," Renewable Energy, Elsevier, vol. 139(C), pages 176-185.
    • Handle: RePEc:eee:renene:v:139:y:2019:i:c:p:176-185
    DOI: 10.1016/j.renene.2019.02.033
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    References listed on IDEAS

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    Cited by:

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    2. Nawaz Edoo & Robert T. F. Ah King, 2021. "Techno-Economic Analysis of Utility-Scale Solar Photovoltaic Plus Battery Power Plant," Energies, MDPI, vol. 14(23), pages 1-22, December.
    3. Gul, Eid & Baldinelli, Giorgio & Bartocci, Pietro & Bianchi, Francesco & Domenghini, Piergiovanni & Cotana, Franco & Wang, Jinwen, 2022. "A techno-economic analysis of a solar PV and DC battery storage system for a community energy sharing," Energy, Elsevier, vol. 244(PB).
    4. Moez Krichen & Yasir Basheer & Saeed Mian Qaisar & Asad Waqar, 2023. "A Survey on Energy Storage: Techniques and Challenges," Energies, MDPI, vol. 16(5), pages 1-29, February.
    5. Hannan, M.A. & Faisal, M. & Jern Ker, Pin & Begum, R.A. & Dong, Z.Y. & Zhang, C., 2020. "Review of optimal methods and algorithms for sizing energy storage systems to achieve decarbonization in microgrid applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    6. Ahmadiahangar, Roya & Karami, Hossein & Husev, Oleksandr & Blinov, Andrei & Rosin, Argo & Jonaitis, Audrius & Sanjari, Mohammad Javad, 2022. "Analytical approach for maximizing self-consumption of nearly zero energy buildings- case study: Baltic region," Energy, Elsevier, vol. 238(PB).
    7. Kotowicz, Janusz & Uchman, Wojciech, 2021. "Analysis of the integrated energy system in residential scale: Photovoltaics, micro-cogeneration and electrical energy storage," Energy, Elsevier, vol. 227(C).
    8. Oh, Eunsung & Son, Sung-Yong, 2020. "Theoretical energy storage system sizing method and performance analysis for wind power forecast uncertainty management," Renewable Energy, Elsevier, vol. 155(C), pages 1060-1069.
    9. Neves, Rebecca & Cho, Heejin & Zhang, Jian, 2021. "Pairing geothermal technology and solar photovoltaics for net-zero energy homes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 140(C).

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    Keywords

    PV; Battery; Energy storage; BEopt; SAM; TOU;
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