Stochastic optimal energy management system for RTG cranes network using genetic algorithm and ensemble forecasts. (August 2019)

• Record Type: Journal Article
• Authors: Alasali, Feras; Haben, Stephen; Holderbaum, William
• Is Part Of: Journal of energy storage. Volume 24(2019)
• Publication Date: 2019-08
• Subjects: Energy storage system -- RTG crane -- Genetic algorithm -- Stochastic control model -- Load forecast; Energy storage -- Periodicals; Energy storage -- Research -- Periodicals; 621.3126
• DOI: 10.1016/j.est.2019.100759
• Languages: English
• ISSNs: 2352-152X

Abstract

Highlights: An Ensemble Forecasts is developed to present the uncertainty in crane demand. Stochastic optimal controller utilising on GA is developed to reduce peak demand on a network of cranes. The impact of the forecast error on the proposed ESS control system is investigated using different level of demand forecast accuracy. Abstract: 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.