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Optimization of Experimental Model Parameter Identification for Energy Storage Systems

Author

Listed:
  • Daniele Gallo

    (Department of Industrial and Information Engineering, Second University of Naples, Via Roma 29, Aversa (CE) 81031, Italy)

  • Carmine Landi

    (Department of Industrial and Information Engineering, Second University of Naples, Via Roma 29, Aversa (CE) 81031, Italy)

  • Mario Luiso

    (Department of Industrial and Information Engineering, Second University of Naples, Via Roma 29, Aversa (CE) 81031, Italy)

  • Rosario Morello

    (Department of Information Engineering Infrastructure and Sustainable Energy, University Mediterranea of Reggio Calabria, Via Graziella (Loc. Feo Vito), Reggio Calabria 89124, Italy)

Abstract

The smart grid approach is envisioned to take advantage of all available modern technologies in transforming the current power system to provide benefits to all stakeholders in the fields of efficient energy utilisation and of wide integration of renewable sources. Energy storage systems could help to solve some issues that stem from renewable energy usage in terms of stabilizing the intermittent energy production, power quality and power peak mitigation. With the integration of energy storage systems into the smart grids, their accurate modeling becomes a necessity, in order to gain robust real-time control on the network, in terms of stability and energy supply forecasting. In this framework, this paper proposes a procedure to identify the values of the battery model parameters in order to best fit experimental data and integrate it, along with models of energy sources and electrical loads, in a complete framework which represents a real time smart grid management system. The proposed method is based on a hybrid optimisation technique, which makes combined use of a stochastic and a deterministic algorithm, with low computational burden and can therefore be repeated over time in order to account for parameter variations due to the battery’s age and usage.

Suggested Citation

  • Daniele Gallo & Carmine Landi & Mario Luiso & Rosario Morello, 2013. "Optimization of Experimental Model Parameter Identification for Energy Storage Systems," Energies, MDPI, vol. 6(9), pages 1-19, September.
    • Handle: RePEc:gam:jeners:v:6:y:2013:i:9:p:4572-4590:d:28489
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    References listed on IDEAS

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    Cited by:

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    2. Cynthia Thamires da Silva & Bruno Martin de Alcântara Dias & Rui Esteves Araújo & Eduardo Lorenzetti Pellini & Armando Antônio Maria Laganá, 2021. "Battery Model Identification Approach for Electric Forklift Application," Energies, MDPI, vol. 14(19), pages 1-26, September.
    3. Kafetzis, A. & Ziogou, C. & Panopoulos, K.D. & Papadopoulou, S. & Seferlis, P. & Voutetakis, S., 2020. "Energy management strategies based on hybrid automata for islanded microgrids with renewable sources, batteries and hydrogen," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).
    4. Janez Ribič & Jože Pihler & Robert Maruša & Filip Kokalj & Peter Kitak, 2019. "Lead-Acid Battery Sizing for a DC Auxiliary System in a Substation by the Optimization Method," Energies, MDPI, vol. 12(22), pages 1-22, November.
    5. Ziyi Zhao, 2023. "Operation Simulation and Economic Analysis of Household Hybrid PV and BESS Systems in the Improved TOU Mode," Sustainability, MDPI, vol. 15(11), pages 1-23, May.
    6. Qingxia Yang & Jun Xu & Binggang Cao & Xiuqing Li, 2017. "A simplified fractional order impedance model and parameter identification method for lithium-ion batteries," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-13, February.

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