Energy Management for Industrial Robots based on AutomationML

Motivated by political regulations and rising energy prices, companies strive for implementing energy management and for reducing their energy consumption. It is an expenditure of work for the energy manager to gather all relevant information about the facilities and machines (e.g. meaning of measurement and control signals, limits of these quantities, installed nominal capacities). In many cases, there is digital planning data from the planning, construction and commissioning phases. Using AutomationML, this information can be used collaboratively not only for engineering and maintenance but for energy monitoring and optimization. In this study, an industrial robot and two conveyors are integrated in the wide-spread energy management software suite EnEffCo. We document the method and results. Implications for deriving optimization formulations are discussed.