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Article

Battery Sizing for Different Loads and RES Production Scenarios through Unsupervised Clustering Methods

1
Politecnico di Milano, Dipartimento di Energia, Via La Masa 34, 20156 Milan, Italy
2
Equienergia S.r.l., c.so Sempione 62, 20153 Milan, Italy
3
Helexia Energy Services S.r.l., Strada 8, Palazzo N, Rozzano, 20089 Milan, Italy
*
Authors to whom correspondence should be addressed.
Academic Editor: Cong Feng
Received: 6 August 2021 / Revised: 14 September 2021 / Accepted: 21 September 2021 / Published: 24 September 2021
(This article belongs to the Special Issue Feature Papers of Forecasting 2021)
The increasing penetration of Renewable Energy Sources (RESs) in the energy mix is determining an energy scenario characterized by decentralized power production. Between RESs power generation technologies, solar PhotoVoltaic (PV) systems constitute a very promising option, but their production is not programmable due to the intermittent nature of solar energy. The coupling between a PV facility and a Battery Energy Storage System (BESS) allows to achieve a greater flexibility in power generation. However, the design phase of a PV+BESS hybrid plant is challenging due to the large number of possible configurations. The present paper proposes a preliminary procedure aimed at predicting a family of batteries which is suitable to be coupled with a given PV plant configuration. The proposed procedure is applied to new hypothetical plants built to fulfill the energy requirements of a commercial and an industrial load. The energy produced by the PV system is estimated on the basis of a performance analysis carried out on similar real plants. The battery operations are established through two decision-tree-like structures regulating charge and discharge respectively. Finally, an unsupervised clustering is applied to all the possible PV+BESS configurations in order to identify the family of feasible solutions. View Full-Text
Keywords: battery energy storage system; battery sizing; photovoltaic power production; performance ratio; electrical load; decision tree; k-means clustering battery energy storage system; battery sizing; photovoltaic power production; performance ratio; electrical load; decision tree; k-means clustering
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MDPI and ACS Style

Nespoli, A.; Matteri, A.; Pretto, S.; De Ciechi, L.; Ogliari, E. Battery Sizing for Different Loads and RES Production Scenarios through Unsupervised Clustering Methods. Forecasting 2021, 3, 663-681. https://0-doi-org.brum.beds.ac.uk/10.3390/forecast3040041

AMA Style

Nespoli A, Matteri A, Pretto S, De Ciechi L, Ogliari E. Battery Sizing for Different Loads and RES Production Scenarios through Unsupervised Clustering Methods. Forecasting. 2021; 3(4):663-681. https://0-doi-org.brum.beds.ac.uk/10.3390/forecast3040041

Chicago/Turabian Style

Nespoli, Alfredo, Andrea Matteri, Silvia Pretto, Luca De Ciechi, and Emanuele Ogliari. 2021. "Battery Sizing for Different Loads and RES Production Scenarios through Unsupervised Clustering Methods" Forecasting 3, no. 4: 663-681. https://0-doi-org.brum.beds.ac.uk/10.3390/forecast3040041

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