Quantifying the benefits and risks of real-time thermal ratings in electrical networks

Abstract

Real-Time Thermal Rating (RTTR) is a technology that allows the rating of electrical conductors to be estimated using real-time, local weather conditions. In many cases this leads to an increased rating with respect to conventional approaches. It also identifies some instances in which the conventional, static, rating is greater than the true rating, and is therefore potentially unsafe. The work in this thesis comprises methodologies to improve the planning and implementation of RTTR. Techniques commonly employed in the wind energy industry have been modified for use with RTTR. Computational wind simulations were employed to allow the identification of determining conductor spans, to inform network designers of the rating potential of different conductor routes, to estimate the additional wind energy that could be accommodated through the enhanced line rating and to allow informed placement of the monitoring equipment required to implement RTTR. Furthermore, the wind simulation data were also used to allow more accurate estimation of conductor ratings during operation. Probabilistic methods have been devised to estimate the level of additional load that could be accommodated through RTTR, and quantify the risk in doing so. Finally, a method has been developed to calculate the benefit RTTR can provide to system wide reliability. State sampling and sequential Monte Carlo simulations were used to evaluate the probabilistic functions associated with the ratings, the load and failures on both the existing network and the RTTR system itself. These methods combine to address fundamental barriers to the wide scale adoption and implementation of RTTR. The majority of existing research has focussed on improving technical solutions, which are of little benefit if it is not possible to quantify the benefits of RTTR before it is implemented. This work allows quantification not only of those benefits, but of the associated risks and uncertainties as well.