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Numerical and Experimental Efficiency Estimation in Household Battery Energy Storage Equipment

Author

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  • Matteo Moncecchi

    (Politecnico di Milano, Dipartimento di Energia, Via La Masa 34, 20156 Milano, Italy)

  • Alessandro Borselli

    (Politecnico di Milano, Dipartimento di Energia, Via La Masa 34, 20156 Milano, Italy)

  • Davide Falabretti

    (Politecnico di Milano, Dipartimento di Energia, Via La Masa 34, 20156 Milano, Italy)

  • Lorenzo Corghi

    (UNE srl Universal Nature Energy, Via Modena 48/E, 42015 Correggio (RE))

  • Marco Merlo

    (Politecnico di Milano, Dipartimento di Energia, Via La Masa 34, 20156 Milano, Italy)

Abstract

Battery energy storage systems (BESS) are spreading in several applications among transmission and distribution networks. Nevertheless, it is not straightforward to estimate their performances in real life working conditions. This work is aimed at identifying test power profiles for stationary residential storage applications capable of estimating BESS performance. The proposed approach is based on a clustering procedure devoted to group daily power profiles according to their battery efficiency. By performing a k-means clustering on a large dataset of load and generation profiles, four standard charge/discharge profiles have been identified to test BESS’ performances. Different clustering approaches have been considered, each of them splitting the dataset according to different properties of the profiles. A well-performing clustering approach resulted, based on the adoption of reference parameters for the clustering process of the maximum power exchanged by the BESS and the variation of battery energy content. Firstly, the results have been proven through a numerical procedure based on a BESS electrical model and on the definition of a key performance index. Then, an experimental validation has been carried out on a pre-commercial sodium-nickel chloride BESS: this device is available in the IoT lab of Politecnico di Milano within the H2020 InteGRIDy project.

Suggested Citation

  • Matteo Moncecchi & Alessandro Borselli & Davide Falabretti & Lorenzo Corghi & Marco Merlo, 2020. "Numerical and Experimental Efficiency Estimation in Household Battery Energy Storage Equipment," Energies, MDPI, vol. 13(11), pages 1-19, May.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:11:p:2719-:d:364329
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    References listed on IDEAS

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    1. Fabio Bignucolo & Massimiliano Coppo & Giorgio Crugnola & Andrea Savio, 2017. "Application of a Simplified Thermal-Electric Model of a Sodium-Nickel Chloride Battery Energy Storage System to a Real Case Residential Prosumer," Energies, MDPI, vol. 10(10), pages 1-29, September.
    2. Rhodes, Joshua D. & Cole, Wesley J. & Upshaw, Charles R. & Edgar, Thomas F. & Webber, Michael E., 2014. "Clustering analysis of residential electricity demand profiles," Applied Energy, Elsevier, vol. 135(C), pages 461-471.
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    Cited by:

    1. Mehrshad Pakjoo & Luigi Piegari & Giuliano Rancilio & Silvia Colnago & Joseph Epoupa Mengou & Federico Bresciani & Giacomo Gorni & Stefano Mandelli & Marco Merlo, 2023. "A Review on Testing of Electrochemical Cells for Aging Models in BESS," Energies, MDPI, vol. 16(19), pages 1-26, September.

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