Operational experience with Dynamic Line Rating forecast-based solutions to increase usable network transfer capacity

SUMMARY This proposed paper focuses on the criteria that need to be met to allow network operators to effectively use Dynamic Line Ratings in real-life operations. Today DLR has reached a point where the focus of research & testing has moved on from how to determine the most reliable & accurate rating to how to effectively integrate DLR in the processes of the network operators and maximize the use of the network. Different pilot projects in Europe and around the world have proven the DLR technology works in the field and many papers within CIGRE and other organizations highlight those results, but now the focus is shifting to how this technology can be put to best use: where it makes sense and how the results should be used to maximize the benefits while reducing the operational risks. The paper will highlight the experience the Belgian TSO, Elia, has had regarding those questions. The specific aspects detailed here is the usability of day-ahead forecasting of DLR. Indeed, most decisions regarding network operation and Electricity Market are taken many hours/days in advance; therefore if DLR is to influence these decisions, a reliable forecast of the dynamic rating values is required. This is very similar to the need for the forecast of wind & sun production that allows the safe integration of those intermittent energy sources into the power system. Within the EU-funded FP7 Twenties project, the University of Liege, Belgium, together with Ampacimon has developed such a capability. Elia and Coreso have evaluated the usability of such a DLR forecast value to increase the flexibility of the network, to allow more exchange capacities for the market, and help solve pan-european congestion issues related to the increasing share of intermittent power from RES (Renewable Energy Sources) in the energy mix. Two-day ahead DLR forecast has shown an average capacity improvement of more than 10% over seasonal rating with 98% confidence. The confidence interval may be adjusted to operational needs, as a tradeoff between more gain and more confidence in the forecast has to be set, depending on the risk policy. Different cases may even feature different risk policies, making them flexible as well, e.g. what are the other available options to increase flexibility at that moment? These forecast capacities can then further be used for dayahead management of PSTs (Phase-Shifting Transformers) in a coordinated way, together 21,� rue� d’Artois,� F -75008 PARIS