Accessibility navigation


Stochastic optimal energy management system for RTG cranes network using genetic algorithm and ensemble forecasts

Alasali, F., Haben, S. and Holderbaum, W. (2019) Stochastic optimal energy management system for RTG cranes network using genetic algorithm and ensemble forecasts. Journal of Energy Storage, 24. 100759. ISSN 2352-152X

[img]
Preview
Text - Accepted Version
· Please see our End User Agreement before downloading.

922kB

It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

To link to this item DOI: 10.1016/j.est.2019.100759

Abstract/Summary

In low voltage networks, Energy Storage Systems (ESSs) play a significant role in increasing energy cost savings, peak reduction and energy efficiency whilst reinforcing the electrical network infrastructure. This paper presents a stochastic optimal management system based on a Genetic Algorithm (GA) for the control of an ESS equipped with a network of electrified Rubber Tyre Gantry (RTG) cranes. The stochastic management system aims to improve the reliability and economic performance, for given ESS parameters, of a network of cranes by taking into account the uncertainty in the RTGs electrical demand. A specific case study is presented using real operational data of the RTGs netwrok in the Port of Felixstowe, UK, and the results of the stochastic control system is compared to a standard set-point controller. In this paper, two forecast data sets with different levels of accuracy are used to investigate the impact of the crane demand forecast error in the proposed ESS control system. The results of the proposed control strategies indicate that the stochastic management system successfully increases the electric energy cost savings, the peak demand reductions and successfully outperforms a comparable set-point controller.

Item Type:Article
Refereed:Yes
Divisions:Life Sciences > School of Biological Sciences > Biomedical Sciences
ID Code:84507
Publisher:Elsevier

Downloads

Downloads per month over past year

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation