Abstract/Summary

An Energy Storage System (ESS) is a potential solution to increase the energy efficiency of low voltage distribution networks whilst reinforcing the power system. In this article, energy management systems have been developed for the control of an ESS connected to a network of electrified Rubber Tyre Gantry (RTG) cranes. Considering the highly volatile crane demand behaviour and uncertainty in the RTG crane demand prediction as a nonlinear optimisation problem, this paper presents and verifies an optimal energy control strategy based on a Stochastic Model Predictive Control (SMPC) algorithm. The SMPC controller aims to improve the reliability and economic performance of a network of RTG cranes, under a given ESS and network specification. A specific case, using different ESS locations, is presented and the results of the proposed SMPC and MPC control models are compared to a set-point controller using data collected from an instrumented electrified RTG cranes at the Port of Felixstowe, UK. The results indicate that the SMPC controller successfully reduce electrical energy costs, the peak demand and outperforms each of the presented control techniques.