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

Feras Alasali, Stephen Haben, Victor Becerra and William Holderbaum
Additional contact information
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

Energies, 2017, vol. 10, issue 10, 1-18

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.

Keywords: energy storage system; Rubber Tyre Gantry (RTG) crane; cost optimization; model predictive control; stochastic load; forecast (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
https://www.mdpi.com/1996-1073/10/10/1598/pdf (application/pdf)
https://www.mdpi.com/1996-1073/10/10/1598/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:10:y:2017:i:10:p:1598-:d:114881

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().