Operational concepts for the deployment of AI-supported maintenance strategies at Distribution System Operators to enable the energy and mobility transition

Abstract

Climate change is leading to massive changes, especially in the areas of energy and mobility. As a connecting element of the energy and mobility transition, electricity grids will play a key role. Bidirectional energy flows and massive fluctuation in generation and consumption patterns lead to high stresses on components and systems, especially in the distribution grid. This confronts Distribution System Operators (DSOs) with new challenges to continue to ensure security of supply in an economical and resource-efficient manner. New maintenance strategies can enable operators to address these challenges. Novel sensors and artificial intelligence enable the technical use of methods such as Predictive Maintenance to detect and predict the probability of failure of critical components based on current condition data. Whereas Predictive Maintenance is already being used in many areas of the manufacturing industry today, the procedures are still new in operating medium-voltage switchgear in the distribution grid, which are critical for ensuring the security of supply. Today's maintenance processes are not automated and are based on Preventive Maintenance strategies and differ very much from those of production environments. For example, the used IT-systems differ as well as the level of involvement of service contractors and regulative requirements and limitations. The use of Predictive Maintenance in the operation of critical infrastructures therefore places special demands on existing maintenance strategies at DSOs to economically ensure security of supply. This paper proposes an operational concept consisting of a process model, IT-system landscape and information logistics model compatible with the current process and system architecture to deploy new maintenance strategies at DSOs.