Forecasting for dynamic line rating

Abstract

International audienceThis paper presents an overview of the state of the art on the research on Dynamic Line Rating forecasting. It is directed at researchers and decision-makers in the renewable energy and smart grids domain, and in particular at members of both the power system and meteorological community. Its aim is to explain the details of one aspect of the complex interconnection between the environment and power systems. The ampacity of a conductor is defined as the maximum constant current which will meet the design, security and safety criteria of a particular line on which the conductor is used. Dynamic Line Rating (DLR) is a technology used to dynamically increase the ampacity of electric overhead transmission lines. It is based on the observation that the ampacity of an overhead line is determined by its ability to dissipate into the environment the heat produced by Joule effect. This in turn is dependent on environmental conditions such as the value of ambient temperature, solar radiation, and wind speed and direction. Currently, conservative static seasonal estimations of meteorological values are used to determine ampacity. In a DLR framework, the ampacity is estimated in real time or quasi-real time using sensors on the line that measure conductor temperature, tension, sag or environmental parameters such as wind speed and air temperature. Because of the conservative assumptions used to calculate static seasonal ampacity limits and the variability of weather parameters, DLRs are considerably higher than static seasonal ratings. The latent transmission capacity made available by DLRs means the operation time of equipment can be extended, especially in the current power system scenario, where power injections from Intermittent Renewable Sources (IRS) put stress on the existing infrastructure. DLR can represent a solution for accommodating higher renewable production whilst minimizing or postponing network reinforcements. On the other hand, the variability of DLR with respect to static seasonal ratings makes it particularly difficult to exploit, which explains the slow take-up rate of this technology. In order to facilitate the integration of DLR into power system operations, research has been launched into DLR forecasting, following a similar avenue to IRS production forecasting, i.e. based on a mix of statistical methods and meteorological forecasts. The development of reliable DLR forecasts will no doubt be seen as a necessary step for integrating DLR into power system management and reaping the expected benefits

Understanding the Benefits of Dynamic Line Rating Under Multiple Sources of Uncertainty

“…The authors in [12] highlight the need to develop DLR forecast models to facilitate its application and present a state-of-art review on the forecasting techniques. Machine learning techniques [13] and ensemble weather forecast [14] are among the most widely used method in the forecasting of DLR.…”

Combining energy storage and real-time thermal ratings to solve distribution network problems: benefits and challenges

“…The uncertainty associated with the incorporation of RTTR techniques can be offset somewhat through implementation of an appropriate forecasting scheme. A review of state of the art forecasting techniques for thermal ratings of overhead lines can be found in [9]. The accurate forecasting of RTTR however represents a significant challenge to the implementation of a combined system, if optimal scheduling of the EES devices is to be achieved.…”

Conductor Temperature Estimation and Prediction at Thermal Transient State in Dynamic Line Rating Application

“…The ampacity limit at steady state using DLR can estimate with weather forecast [8]. On the other hand, to compute dynamic limits (at thermal transient state) with DLR, it is necessary to know on-line both the conductor temperature and the atmospheric conditions.…”