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Optimal Management of Battery and Fuel Cell-Based Decentralized Generation in DC Shipboard Microgrids

Author

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  • Massimiliano Luna

    (Istituto di Ingegneria del Mare (INM), Consiglio Nazionale delle Ricerche (CNR), Via Ugo La Malfa 153, 90146 Palermo, Italy)

  • Giuseppe La Tona

    (Istituto di Ingegneria del Mare (INM), Consiglio Nazionale delle Ricerche (CNR), Via Ugo La Malfa 153, 90146 Palermo, Italy)

  • Angelo Accetta

    (Istituto di Ingegneria del Mare (INM), Consiglio Nazionale delle Ricerche (CNR), Via Ugo La Malfa 153, 90146 Palermo, Italy)

  • Marcello Pucci

    (Istituto di Ingegneria del Mare (INM), Consiglio Nazionale delle Ricerche (CNR), Via Ugo La Malfa 153, 90146 Palermo, Italy)

  • Andrea Pietra

    (Merchant Ship Division, Fincantieri S.p.A., Passeggio Sant’Andrea, 6/A, 34123 Trieste, Italy)

  • Maria Carmela Di Piazza

    (Istituto di Ingegneria del Mare (INM), Consiglio Nazionale delle Ricerche (CNR), Via Ugo La Malfa 153, 90146 Palermo, Italy)

Abstract

This paper proposes an energy management system (EMS) that aims at managing the modular direct current (DC) microgrids (MGs) of a hybrid DC/AC power system onboard cruise ships. Each shipboard microgrid is an electrically self-sufficient system supplied only by a fuel cell (FC) and a Lithium battery, and it powers the ship’s hotel services. However, continuously varying power demand profiles negatively affect the FC. Thus, the proposed EMS aims to minimize the FC operating point excursion on the source’s characteristic. This goal is pursued by exploiting the battery capability to manage load fluctuations and compensate for power demand forecasting errors. Furthermore, it is accomplished while satisfying all the operational constraints of the shipboard microgrids and ensuring daily battery charging/discharging cycles. The proposed EMS is based on two subsystems: (1) a rule-based microgrid supervisor, which makes the EMS capable of managing black start, normal operating conditions, and transient or faulty conditions; (2) an energy management (EM) algorithm, which allows achieving the desired goal without oversizing the battery, thus granting the cost-effectiveness of the solution and a reduced impact on technical volumes/weights on board. The EMS was tested with specific reference to a real-world case study, i.e., a 48,000 gross tonnage cruise ship under different operating scenarios, including black start and multi-day period operation of shipboard MGs. Test results showed that the operating points of the FC were always in the neighborhood of the point chosen by the MG designer, that the voltage variations were always well below 5%, guaranteeing stable operation, and that the black start operation was suitably handled by the EMS. According to the obtained results, the effectiveness of the proposed approach was assessed.

Suggested Citation

  • Massimiliano Luna & Giuseppe La Tona & Angelo Accetta & Marcello Pucci & Andrea Pietra & Maria Carmela Di Piazza, 2023. "Optimal Management of Battery and Fuel Cell-Based Decentralized Generation in DC Shipboard Microgrids," Energies, MDPI, vol. 16(4), pages 1-21, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:4:p:1682-:d:1061484
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    References listed on IDEAS

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    1. Di Piazza, A. & Di Piazza, M.C. & La Tona, G. & Luna, M., 2021. "An artificial neural network-based forecasting model of energy-related time series for electrical grid management," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 184(C), pages 294-305.
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