Numerical and Experimental Efficiency Estimation in Household Battery Energy Storage Equipment

Matteo Moncecchi, Alessandro Borselli, Davide Falabretti, Lorenzo Corghi and Marco Merlo
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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

Energies, 2020, vol. 13, issue 11, 1-19

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.

Keywords: battery energy storage systems; PV production profiles; cluster analysis; k-means algorithm; battery test profiles; sodium-nickel chloride battery; lab tests (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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