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

Author

Listed:
  • Alfredo Nespoli

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

  • Andrea Matteri

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

  • Silvia Pretto

    (Equienergia S.r.l., c.so Sempione 62, 20153 Milan, Italy)

  • Luca De Ciechi

    (Helexia Energy Services S.r.l., Strada 8, Palazzo N, Rozzano, 20089 Milan, Italy)

  • Emanuele Ogliari

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

Abstract

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.

Suggested Citation

  • Alfredo Nespoli & Andrea Matteri & Silvia Pretto & Luca De Ciechi & Emanuele Ogliari, 2021. "Battery Sizing for Different Loads and RES Production Scenarios through Unsupervised Clustering Methods," Forecasting, MDPI, vol. 3(4), pages 1-19, September.
    • Handle: RePEc:gam:jforec:v:3:y:2021:i:4:p:41-681:d:642674
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    References listed on IDEAS

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    1. Sonia Leva, 2022. "Editorial for Special Issue: “Feature Papers of Forecasting 2021”," Forecasting, MDPI, vol. 4(1), pages 1-3, March.

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