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Influence and Impact of Data Averaging and Temporal Resolution on the Assessment of Energetic, Economic and Technical Issues of Hybrid Photovoltaic-Battery Systems

Author

Listed:
  • Alessandro Burgio

    (Independent Researcher, 87036 Rende, Italy)

  • Daniele Menniti

    (Department of Mechanical, Energy and Management Engineering, University of Calabria, 87036 Rende, Italy)

  • Nicola Sorrentino

    (Department of Mechanical, Energy and Management Engineering, University of Calabria, 87036 Rende, Italy)

  • Anna Pinnarelli

    (Department of Mechanical, Energy and Management Engineering, University of Calabria, 87036 Rende, Italy)

  • Zbigniew Leonowicz

    (Wroclaw University of Science and Technology, Wyb. Wyspianskiego 27, 50370 Wroclaw, Poland)

Abstract

The temporal resolution of the demand and generation profiles may have a significant impact on the estimation of self-sufficiency and self-consumption for consumers and prosumers. As an example, measuring the load profile, with a low temporal resolution, may lead to the under-estimation of energy consumption, while measuring solar irradiation with a low temporal resolution may lead to the over-estimation of on-site energy generation. Storage systems may reduce errors due to the lower temporal resolution by 8–10 times or even more, depending on the capacity of the batteries. Besides self-generation and self-consumption, there are other indicators that can be influenced by temporal resolution that deserve to be investigated. This is a detailed study of the influence of temporal resolution and the time averaging on a hybrid photovoltaic-battery system; this study encompasses both economic and technical aspects, from the calculation of savings on the electricity bill to the estimation of the equivalent cycles of battery storage system. To this end, the three-minute load profile of a real case study is used to obtain other three load profiles with temporal resolution equal to 15, 30, and 60 min via data averaging. Therefore, the authors analyze the influence and the impact of temporal resolution and data averaging in terms of: The size of the photovoltaic generator and the capacity of the storage system; the savings in the electricity bill and the balance between costs and savings; the peak values and the average values of power flows during high generation and low generation; the profile of the storage system over the year; the utilization rate of the storage system and the rated power of the electronic converter that regulates the charge and the discharge; the profile of the state of charge of the storage system and the life-time estimation of batteries through the calculation of the equivalent number of cycles.

Suggested Citation

  • Alessandro Burgio & Daniele Menniti & Nicola Sorrentino & Anna Pinnarelli & Zbigniew Leonowicz, 2020. "Influence and Impact of Data Averaging and Temporal Resolution on the Assessment of Energetic, Economic and Technical Issues of Hybrid Photovoltaic-Battery Systems," Energies, MDPI, vol. 13(2), pages 1-26, January.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:2:p:354-:d:307417
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    4. Schmid, Fabian & Winzer, Joscha & Pasemann, André & Behrendt, Frank, 2021. "An open-source modeling tool for multi-objective optimization of renewable nano/micro-off-grid power supply system: Influence of temporal resolution, simulation period, and location," Energy, Elsevier, vol. 219(C).
    5. Amedeo Buonanno & Martina Caliano & Antonino Pontecorvo & Gianluca Sforza & Maria Valenti & Giorgio Graditi, 2022. "Global vs. Local Models for Short-Term Electricity Demand Prediction in a Residential/Lodging Scenario," Energies, MDPI, vol. 15(6), pages 1-18, March.
    6. Seok-Jun Bu & Sung-Bae Cho, 2020. "Time Series Forecasting with Multi-Headed Attention-Based Deep Learning for Residential Energy Consumption," Energies, MDPI, vol. 13(18), pages 1-16, September.
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    9. Bompard, Ettore & Ciocia, Alessandro & Grosso, Daniele & Huang, Tao & Spertino, Filippo & Jafari, Mehdi & Botterud, Audun, 2022. "Assessing the role of fluctuating renewables in energy transition: Methodologies and tools," Applied Energy, Elsevier, vol. 314(C).
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