Liander and University of Twente: Collaborating to improve THE DYNAMIC LOAD CAPACITY OF ELECTRICITY CABLES for Distribution System Operators in the Netherlands
As Distribution System Operator in the Netherlands, Liander manages and maintains a wide range of interconnected subsystems in the electricity grid, from transformers and substations to underground cables.
Increased, decentralized, and fluctuating demand for electricity transport is causing congestion in existing medium and low voltage grids. The increase arises from growth in both generation and load moreover, this is compounded by the energy transition. DSO’s such as Liander cannot immediately meet the current and future demand for network expansion and that, in order to provide society with the best possible facilities, Liander is opting to make optimal use of the existing networks so that as many customers as possible can still be connected. To this end, it is important that the DSO clearly knows the load limits of their assets. Therefore, fully utilizing existing networks is of paramount importance to enable the energy transition and to alleviate immediate congestion in the coming years.
This research project builds on a well-established tradition of the University of Twente of advancing research in the areas of smart and innovative forms of maintenance. The goal of this research is to develop a framework (methods, models, monitoring systems) that enables Liander to dynamically load existing infrastructure components by briefly operating them above their nominal design capacity in situations where this can be safely achieved. Setting dynamic thresholds and implementing real-time monitoring of key performance parameters such as current and temperature are crucial in managing the degradation over the life cycle.
In addition, a health monitoring plan is needed to implement maintenance policies for dynamic loading in a timely manner. The use of physics-based prediction models proposed in IDLEC can improve the accuracy and efficiency of energy network operations. This integration combines domain knowledge and physical understanding of forecasting models with data-driven insights which can be used to build more reliable, resilient and optimized energy grid systems. Furthermore, the research on organizing CBLM will lead to plans and specific strategies to overcome the challenges associated with CBLM.
University of Twente Team
