Methods for enhanced utilization of the regional distribution grid system (DSO) in the interplay with the transmission system (TSO)

With the increasing demand for electric power in the Norwegian power grid, we are facing challenges with the capacity of the existing grid. Electrification of transport, developing electro-intensive industrial projects and new factories for battery production and data-centres are pushing the grid capacity to its limits. A significant challenge in this is the grids ability to maintain operation also in case of faults. A central standard for reliability in the power grid is known as the "N-1 criteria" which dictates that teh power-grid must be able to maintain operation even if a single component, such as a power-line, fails, without causing large outages or disturbances in the grid. The grid-companies, often called Distribution System Operator (DSO) are left with the choice between increasing the grid-capacity or exploiting the existing capacity better. To increase the capacity conventionally means to add more power cables or power lines. This is both costly, takes time and affects the environment. The alternative is to optimize how we use the utilisation of the existing capacity in a more intelligent way and thus obtain a better utilization of the grid. This may be quicker and less costly, but it could affect the grids ability to withstand faults. This project is working on re-evaluating how we asses the available capacity in the grid and how we can utilize this capacity in real-time. The project takes advantage of advanced methods, from probability-analysis to machine-learning, to make the power-grid both more efficient while maintaining the reliability. In this way we hope to meet the increasing demand for electric power in a sustainable way while maintaining a stable and reliable power supply.

With the ever-increasing demand for electrical power in the electricity grid in Norway, the capacity of the existing electric grid is no longer sufficient. The electrification of the transport sector, support for the development of electro-intensive industries, and the facilitation of new initiatives such as battery factories and data centers are pushing the grid capacity to its limits. One of the main limitations of the grid is its ability to maintain operation in the event of faults. The power system's capability to withstand the loss of any single component, such as a transmission line, without causing a widespread blackout or major disruption to the power grid, is one of the primary factors considered when adding new loads. As the demand for electrical power increases, grid operators are faced with two choices: either to increase the grid capacity through traditional grid reinforcements or to maximize the utilization of the existing grid capacity. This research aims to investigate how the available capacity in the regional distribution grid (24-132 kV) can be assessed more accurately and how this capacity can be utilized in coordination with the transmission network to enhance the real-time grid capacity. This includes assessing dynamic constraints such as Dynamic Line Rating (DLR) and implementing prediction methods to forecast the load flow in the grid for both short and long terms. Additionally, stochastic and probabilistic planning methods (e.g., Probabilistic Power Flow) and probabilistic methods for power system reliability will be employed. These approaches will be implemented within a comparative AI/ML-based framework to compare and estimate the grid's performance in different scenarios and assess the ability of professionals working in planning and operation to utilize this information.