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Planning of Distributed Energy Storage Systems in μGrids Accounting for Voltage Dips

Author

Listed:
  • Fabio Mottola

    (Department of Electrical Engineering and Information Technology, University of Naples Federico II, I-80125 Naples, Italy)

  • Daniela Proto

    (Department of Electrical Engineering and Information Technology, University of Naples Federico II, I-80125 Naples, Italy)

  • Pietro Varilone

    (Department of Electrical Engineering and Information “Maurizio Scarano”, University of Cassino and Southern Lazio, I-03043 Cassino, Italy)

  • Paola Verde

    (Department of Electrical Engineering and Information “Maurizio Scarano”, University of Cassino and Southern Lazio, I-03043 Cassino, Italy)

Abstract

This paper deals with the optimal planning of the electrical energy storage systems in the microgrids aimed at cost minimization. The optimization accounts for the compensation of the voltage dips performed by the energy storage systems. A multi-step procedure, at the first step, identifies a set of candidate buses where the installation of a storage device produces the maximum benefit in terms of dip compensation; then, the life cycle costs in correspondence of different alternatives in terms of size and location of the storage systems are evaluated by considering an optimized use of the energy storage systems. The simulations on a medium voltage microgrid allowed validating the effectiveness of the proposed procedure.

Suggested Citation

  • Fabio Mottola & Daniela Proto & Pietro Varilone & Paola Verde, 2020. "Planning of Distributed Energy Storage Systems in μGrids Accounting for Voltage Dips," Energies, MDPI, vol. 13(2), pages 1-20, January.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:2:p:401-:d:308431
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    References listed on IDEAS

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    1. Fantauzzi, M. & Lauria, D. & Mottola, F. & Scalfati, A., 2017. "Sizing energy storage systems in DC networks: A general methodology based upon power losses minimization," Applied Energy, Elsevier, vol. 187(C), pages 862-872.
    2. Grover-Silva, Etta & Girard, Robin & Kariniotakis, George, 2018. "Optimal sizing and placement of distribution grid connected battery systems through an SOCP optimal power flow algorithm," Applied Energy, Elsevier, vol. 219(C), pages 385-393.
    3. Nojavan, Sayyad & Majidi, Majid & Esfetanaj, Naser Nourani, 2017. "An efficient cost-reliability optimization model for optimal siting and sizing of energy storage system in a microgrid in the presence of responsible load management," Energy, Elsevier, vol. 139(C), pages 89-97.
    4. Saboori, Hedayat & Hemmati, Reza & Ghiasi, Seyyed Mohammad Sadegh & Dehghan, Shahab, 2017. "Energy storage planning in electric power distribution networks – A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 1108-1121.
    5. Sinan Küfeoğlu & Matti Lehtonen, 2016. "Macroeconomic Assessment of Voltage Sags," Sustainability, MDPI, vol. 8(12), pages 1-12, December.
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    Citations

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    Cited by:

    1. Yuriy Bilan & Marcin Rabe & Katarzyna Widera, 2022. "Distributed Energy Resources: Operational Benefits," Energies, MDPI, vol. 15(23), pages 1-7, November.
    2. Michele De Santis & Leonardo Di Stasio & Christian Noce & Paola Verde & Pietro Varilone, 2021. "Initial Results of an Extensive, Long-Term Study of the Forecasting of Voltage Sags," Energies, MDPI, vol. 14(5), pages 1-26, February.
    3. Michele Zanoni & Riccardo Chiumeo & Liliana Tenti & Massimo Volta, 2021. "Advanced Machine Learning Functionalities in the Medium Voltage Distributed Monitoring System QuEEN: A Macro-Regional Voltage Dips Severity Analysis," Energies, MDPI, vol. 14(23), pages 1-25, November.
    4. Felipe J. Zimann & Eduardo V. Stangler & Francisco A. S. Neves & Alessandro L. Batschauer & Marcello Mezaroba, 2020. "Coordinated Control of Active and Reactive Power Compensation for Voltage Regulation with Enhanced Disturbance Rejection Using Repetitive Vector-Control," Energies, MDPI, vol. 13(11), pages 1-18, June.
    5. Michele Zanoni & Riccardo Chiumeo & Liliana Tenti & Massimo Volta, 2023. "What Else Do the Deep Learning Techniques Tell Us about Voltage Dips Validity? Regional-Level Assessments with the New QuEEN System Based on Real Network Configurations," Energies, MDPI, vol. 16(3), pages 1-24, January.

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