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Optimal Energy Management and MPC Strategies for Electrified RTG Cranes with Energy Storage Systems

Author

Listed:
  • Feras Alasali

    (School of Systems Engineering, University of Reading, Whiteknights, Reading RG6 6AY, UK)

  • Stephen Haben

    (Mathematical Institute, University of Oxford, Andrew Wiles Building, Oxford OX2 6GG, UK)

  • Victor Becerra

    (School of Engineering, University of Portsmouth, Anglesea Road, Portsmouth PO1 3DJ, UK)

  • William Holderbaum

    (School of Systems Engineering, University of Reading, Whiteknights, Reading RG6 6AY, UK
    School of Engineering, Manchester Metropolitan University, Manchester M1 5GD, UK)

Abstract

This article presents a study of optimal control strategies for an energy storage system connected to a network of electrified Rubber Tyre Gantry (RTG) cranes. The study aims to design optimal control strategies for the power flows associated with the energy storage device, considering the highly volatile nature of RTG crane demand and difficulties in prediction. Deterministic optimal energy management controller and a Model Predictive Controller (MPC) are proposed as potentially suitable approaches to minimise the electric energy costs associated with the real-time electricity price and maximise the peak demand reduction, under given energy storage system parameters and network specifications. A specific case study is presented in to test the proposed optimal strategies and compares them to a set-point controller. The proposed models used in the study are validated using data collected from an instrumented RTG crane at the Port of Felixstowe, UK and are compared to a standard set-point controller. The results of the proposed control strategies show a significant reduction in the potential electricity costs and peak power demand from the RTG cranes.

Suggested Citation

  • Feras Alasali & Stephen Haben & Victor Becerra & William Holderbaum, 2017. "Optimal Energy Management and MPC Strategies for Electrified RTG Cranes with Energy Storage Systems," Energies, MDPI, vol. 10(10), pages 1-18, October.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:10:p:1598-:d:114881
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    References listed on IDEAS

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    Cited by:

    1. Feras Alasali & Khaled Nusair & Lina Alhmoud & Eyad Zarour, 2021. "Impact of the COVID-19 Pandemic on Electricity Demand and Load Forecasting," Sustainability, MDPI, vol. 13(3), pages 1-22, January.
    2. Anthony Roy & François Auger & Jean-Christophe Olivier & Emmanuel Schaeffer & Bruno Auvity, 2020. "Design, Sizing, and Energy Management of Microgrids in Harbor Areas: A Review," Energies, MDPI, vol. 13(20), pages 1-24, October.
    3. Feras Alasali & Antonio Luque & Rayner Mayer & William Holderbaum, 2019. "A Comparative Study of Energy Storage Systems and Active Front Ends for Networks of Two Electrified RTG Cranes," Energies, MDPI, vol. 12(9), pages 1-14, May.
    4. Feras Alasali & Stephen Haben & Husam Foudeh & William Holderbaum, 2020. "A Comparative Study of Optimal Energy Management Strategies for Energy Storage with Stochastic Loads," Energies, MDPI, vol. 13(10), pages 1-19, May.
    5. Dawei Chen & Wangqiang Niu & Wei Gu & Nigel Schofield, 2019. "Game-Based Energy Management Method for Hybrid RTG Cranes," Energies, MDPI, vol. 12(18), pages 1-23, September.
    6. Nur Najihah Abu Bakar & Josep M. Guerrero & Juan C. Vasquez & Najmeh Bazmohammadi & Yun Yu & Abdullah Abusorrah & Yusuf A. Al-Turki, 2021. "A Review of the Conceptualization and Operational Management of Seaport Microgrids on the Shore and Seaside," Energies, MDPI, vol. 14(23), pages 1-31, November.
    7. Feras Alasali & Husam Foudeh & Esraa Mousa Ali & Khaled Nusair & William Holderbaum, 2021. "Forecasting and Modelling the Uncertainty of Low Voltage Network Demand and the Effect of Renewable Energy Sources," Energies, MDPI, vol. 14(8), pages 1-31, April.

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