TY - JOUR
T1 - Energy management systems for a network of electrified cranes with energy storage
AU - Holderbaum, William
AU - Alasali, Feras
AU - Haben, Stephen
PY - 2019/3/1
Y1 - 2019/3/1
N2 - 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.
AB - 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.
KW - Cost saving potentials
KW - Energy forecast
KW - Energy storage
KW - RTG crane
KW - Stochastic model predictive control
UR - https://linkinghub.elsevier.com/retrieve/pii/S0142061518315631
UR - http://www.scopus.com/inward/record.url?scp=85054409830&partnerID=8YFLogxK
U2 - 10.1016/j.ijepes.2018.10.001
DO - 10.1016/j.ijepes.2018.10.001
M3 - Article
SN - 0142-0615
VL - 106
SP - 210
EP - 222
JO - International Journal of Electrical Power & Energy Systems
JF - International Journal of Electrical Power & Energy Systems
ER -