Forecasting for dynamic line rating. (December 2015)

• Record Type: Journal Article
• Authors: Michiorri, Andrea; Nguyen, Huu-Minh; Alessandrini, Stefano; Bremnes, John Bjørnar; Dierer, Silke; Ferrero, Enrico; Nygaard, Bjørn-Egil; Pinson, Pierre; Thomaidis, Nikolaos; Uski, Sanna
• Is Part Of: Renewable & sustainable energy reviews. Volume 52(2015:Dec.)
• Page Start: 1713
• Page End: 1730
• Publication Date: 2015-12
• Subjects: AGL above ground level -- ANM active network management -- CMC Canadian Meteorological Centre -- CFD Computational Fluid Dynamic -- COSMO Consortium for Small-scale Modelling -- DMO Direct Model Output -- DSO Distribution System Operators -- DLRs dynamic line ratings -- EPRI Electric Power Research Institute -- ENTSO-E European Network of Transmission System Operators for Electricity -- EPS Ensemble Prediction System -- EAFs Eulerian Autocorrelation Functions -- FACTS Flexible Alternated Current Transmission Systems -- GLAMEPS Grand Limited Area Ensemble Prediction System -- HIRLAM High Resolution Limited Area Modelling -- ICT Information and Communication Technology -- IEEE Institute of Electrical and Electronics Engineers -- IRS Intermittent Renewable Sources -- CIGRE International Council for Large Electric Systems -- LAM Limited Area Model -- LWS Low Wind Speed -- MEMS Micro-Electro-Mechanical Systems -- NCEP National Centre for Environmental Prediction -- NOAA National Oceanic and Atmospheric Administration -- NTC Net Transfer Capacity -- NWP Numerical Weather Prediction -- PDF probability density function -- RTTR Real Time Thermal Rating -- REE Red Electrica de España -- ROI Return on Investment -- RMSE Root Mean Square Error -- TSO Transmission System Operator -- WIRE Weather Intelligence for Renewable Energies -- WAsP Wind Atlas Analysis and Application Programme; Rating -- Overhead lines -- Forecast -- Smart grid; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.rser.2015.07.134
• Languages: English
• ISSNs: 1364-0321

Abstract

Abstract: This 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.