Development of an Autonomous Light Control Algorithm with a Simulation Model of a Container Terminal

Abstract In the ongoing decade, port logistics in Europe have been exposed to intense competition. Especially container ports experience competition not only for ships but also for supply chains. To adapt to this trend, terminal operators are improving the efficiency in port logistics through, among other things, automation of port operations. Most of these efforts concern processes handling shipped goods. Instead of concentrating on these well-researched improvements, we explored the feasibility and economic potential of an intelligent lighting control system. Often automating handling processes need high investments in hard- and software. This smart lighting system is supposed to control demand-actuated outdoor lights in a harbour environment. In this research, movement profiles of straddle carriers and infrastructural data were gathered and refined to build a model of the observed container terminal. The modelled infrastructure includes the position of every lighting tower and its illuminant as well as their specific power consumption and the respective area, which those lights illume. To control these lights, we developed a control algorithm. We investigated through simulation experiments the power savings for this autonomous lighting control system. Multiple influential factors of this control concept were evaluated by varying the parameterization of the algorithm. A gathered data set, containing trajectories of operating straddle carriers during full terminal utilization, is the basis for the mentioned experiments. The results of varying the utilization rate of the terminal have identified a relationship between capacity and percental power consumption, in the form of a root function. The more straddle carriers run, the less the overall power consumption is increasing. Using the intelligent light control system on full utilization saves 14.67% of the total energy. In the case of 80 % terminal utilization, 18.22 % of total energy is subject for saving effects. Besides these results, we were able to explore the influences of the algorithm parameterization on the energy savings and could assess these solutions economically.