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

William Holderbaum, Feras Alasali, Stephen Haben

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Article number100759
JournalJournal of Energy Storage
Early online date7 May 2019
Publication statusPublished - 1 Aug 2019


  • Energy storage system
  • Genetic algorithm
  • Load forecast
  • RTG crane
  • Stochastic control model


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