Examination of Dynamic Line Rating Deviations in Day-Ahead Congestion Forecast

Abstract:
The transition to renewable energy sources is driving a fundamental shift in power generation, with increasing distances between decentralized generation and demand centers causing significant grid congestion. Dynamic line rating (DLR) offers a promising solution to optimize the utilization of existing transmission infrastructure by leveraging real time and forecasted thermal capacities. While DLR has proven effective in real time congestion management, its integration into operational planning processes, such as day-ahead congestion forecasts (DACF), introduces uncertainties due to forecast deviations. These deviations can result in discrepancies between planned and realtime thermal limits, potentially under-mining system reliability and efficiency. This paper evaluates the deviations in DLR threshold values between DACF and real time operations within the 380 kV transmission network of TenneT Germany. Focusing on permanent thermal bottleneck currents of eight representative circuits, the analysis examines seasonal variations and their implications for grid operation. Additionally, the paper discusses the role of safety margins in addressing forecast uncertainties, providing insights into their optimization for improved planning reliability. By bridging gaps in existing literature, this study contributes to the effective application of DLR in operational planning, enhancing grid performance and supporting the energy transition.