Optimal energy management of a retrofitted Rubber Tyred Gantry Crane with energy recovery capabilities

In this work, an optimal energy management model for the grid-powered electric RTG, with a battery storage system, is developed. The aim of the model is to reduce the operation cost, by minimizing the component linked to the maximum demand charges from the grid, as well as the component linked to the Time of Use (ToU) pricing structure. As a case study, a RTG crane operating in South Africa has been selected. The load profile, size the battery storage system, ToU tariff, as well as the maximum demand charges, are used as input for the developed model. Simulations, for a complete RTG handling cycle, have been conducted to evaluate the techno-economic performances of the developed model, used to optimally dispatch the power flow in the system during the different phases of operation. Three main configurations have been simulated as energy sources for the RTG crane, namely, the exclusive supply from the grid, grid/battery hybrid system without energy recovery during the lowering phase and grid/battery hybrid with energy recovery, during the lowering phase. As compared to the baseline, the simulation results have shown that using the proposed model shows a possible 50.36, 60.6 and 64.4% cost reduction, per full handing cycle, are possible in off-peak standard and peak pricing period, for the selected winter day. The results also show that the maximum demand charges may be reduced by 45.20%. Further economic analyses have been conducted; and the results indicate that the proposed system successfully reduce electrical energy costs, the peak demand and outperforms each of the presented baselines.

Automated summary

An optimal energy management model for a grid-powered electric RTG with a battery storage system was developed in this study, which was evaluated through a case study in South Africa. Simulation results showed that using this model could potentially reduce operating costs and maximum demand charges in different pricing periods.