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Energies 2017, 10(10), 1598; https://doi.org/10.3390/en10101598

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

1
School of Systems Engineering, University of Reading, Whiteknights, Reading RG6 6AY, UK
2
Mathematical Institute, University of Oxford, Andrew Wiles Building, Oxford OX2 6GG, UK
3
School of Engineering, University of Portsmouth, Anglesea Road, Portsmouth PO1 3DJ, UK
4
School of Engineering, Manchester Metropolitan University, Manchester M1 5GD, UK
*
Authors to whom correspondence should be addressed.
Received: 21 September 2017 / Revised: 10 October 2017 / Accepted: 11 October 2017 / Published: 13 October 2017
(This article belongs to the Section Electrical Power and Energy System)
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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. View Full-Text
Keywords: energy storage system; Rubber Tyre Gantry (RTG) crane; cost optimization; model predictive control; stochastic load; forecast energy storage system; Rubber Tyre Gantry (RTG) crane; cost optimization; model predictive control; stochastic load; forecast
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Alasali, F.; Haben, S.; Becerra, V.; Holderbaum, W. Optimal Energy Management and MPC Strategies for Electrified RTG Cranes with Energy Storage Systems. Energies 2017, 10, 1598.

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