Dynamic line rating for electric utilities

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Dynamic line rating (DLR), also known as real-time thermal rating (RTTR), and in German Freileitungsmonitoring (FLM), is an electric power transmission operation philosophy aiming at maximizing load, when environmental conditions allow it, without compromising safety. Research, prototyping and pilot projects were initiated in the 1990s, but the emergence of the "smart grid" stimulated electric utilities, scientists and vendors to develop comprehensive and sustainable solutions.

Principles and applications[edit]

The current-carrying capacity, or ampacity, of overhead lines starts with the type of conductor used. The conductor choice provides the physical parameters for dynamic line rating. Transmission ratings are set with a maximum allowable conductor temperature (annealing temperature) and minimum clearance rules to adhere to legislation. Both the temperature and the minimum clearance of a transmission line is affected by the number of amps flowing through the wire, corresponding to the Joule Effect[1]

Ampacity[2] has traditionally been limited by conductor thermal capacity defined in terms of Static Rating (SR), based on a predetermined set of worst-case weather scenarios with high reliability levels (typically: simultaneous occurrence of (i) conservative minimum perpendicular wind speed of about 0.5-0.6 [m/s], (ii) conservative high solar radiation of about 1000 [W/m²] and (iii) conservative high ambient temperature defined by season).

Real Time Dynamic Line Rating[edit]

More often than not, there are unused margins between the limits defined by static ratings and the "true limits" measured by a DLR/RTTR system. Several methods have been developed since the 1990s. Most of them rely upon sensors deployed on overhead lines, measuring parameters in real-time. Other systems utilize weather stations that monitor environmental conditions without contacting the line. Data received from any method is reported to a main computer for processing. Control center operators access usable data (line temperature, ratings, forecasts, historical values) in pseudo-real-time through a human-machine interface (HMI).

Wind Generation Integration[edit]

Wind generation requires an area that has consistent wind conditions. When new wind generation is prospected the grid connection point must also be analyzed. Adding new generation to the grid typically requires an infrastructure buildup that can potentially cost millions of dollars.[3] In some circumstances a phenomenon, known as concurrent cooling, can be utilized to mitigate the expenses. Concurrent cooling is the cooling of transmission lines, thus increasing ampacity, when the wind power is generating. The exact parameters of the system are vital to determine feasibility, however, there has been successful cases [4]

Dynamic Line Rating Reception[edit]

DLR methods and technologies are considered "mature" by industry groups like ENTSO-E after field validations for various applications. Transmission utilities in Asia, Europe, North America, and South America have included deployment of DLR in their grid development roadmaps. IEEE and CIGRÉ devised standard thermal modeling of conductors for ampacity calculations.[5] CIGRÉ issued guidelines for DLR.[6]

Deploying DLR consists in equipping circuits likely to benefit from significant capacity gains with sensors and using the resulting capacity increases when required and possible. Typical applications:

  • Relaxing congestions due to increasing load
  • Improving economic dispatch scenarios in N-1 contingencies
  • Integrating renewable/distributed energy sources without grid reinforcement
  • Deferring or avoiding uprating of circuits
  • Maximizing the use of alternate lines when main corridors are undergoing long maintenance works/overhauls
  • Maximizing transit on interconnectors and "bottleneck" topologies
  • Addressing potential safety/conformance issues

These subjects are addressed at load dispatch center level, and by planning and maintenance departments. However, to decide on priorities, simulations based on 3D line profile analysis and weather data are sometimes performed prior to deployment.

There are two categories of DLR computation methods:[7]

  • Direct measurement methods use devices that are directly coupled to the line. These devices measure temperature, tension, sag or clearance from which the thermal rating is determined (or a combination of the above)
  • Indirect measurement methods use weather stations and modeling

Direct methods give actual conductor state condition with respect to clearance rules and/or maximum conductor temperature. Indirect methods provide estimates based on prevailing, forecast, or measured weather conditions around the line. Computational fluid dynamics models can be used to provide a larger effective area to a single weather station by mapping wind patterns around the terrain [8] Direct methods are strongly recommended by CIGRÉ standards TB 207 and TB 498 - Guidelines for thermal rating and real-time monitoring).

Several kinds of ratings are available, depending on input parameters and algorithms. Real-time ratings allow control room engineers to adjust power flows according to normal operational events or contingencies. They are based on "steady-state (equilibrium) ampacity" calculations. Emergency ratings are based on transient equations and models: they provide permissible overload ratings for a short and adjustable time (typically 5 to 30 minutes). Forecasting methods have been developed to determine intraday and day-ahead ampacity forecasts. They combine DLR historical measurements and weather forecasts. These methods are proprietary, they are promoted by expert and industry associations like CIGRÉ's Study Committee B2 and the WATT Coalition.

While DLR solutions can be implemented as stand-alone, their destination is system integration in the control room with the electric utility SCADA. Energy Management Solution and Distribution Management Solutions vendors propose this type of integration to enhance their offering.

History and perspectives[edit]

Pioneer companies, now defunct, followed initiatives by the US Department of Energy, electric utiities ONCOR and NYPA, ENTSO-E and the European Commission's Energy Research programme. They contributed in setting the standards and paved the way for more advanced solutions. The most active manufacturers originate in North America and Europe where market stimulation is strongest. Experiments were conducted in order to field-test available technologies and to quantify benefits, in terms of available transit gains and capital expenditure savings.[9][10][11][12][13] Operation expenditure gains are more delicate to evaluate since they depend on grid codes, local regulation rules, incentives and penalties. However they can be classified as "additional transit revenues", "improved economic dispatch" and "avoided penalties". As of June 2018 around 20 electric utilities in the Americas and Europe[14][full citation needed][15] use DLR in daily transmission operations. Benefits recorded have attracted the attention of investors, governments and international development aid institutions, who include a DLR share in the scope of their green-field powerline construction projects.

An academic view of these technologies, by Jean-Louis Lilien, Honorarium Professor, University of Liège, Belgium:

Dynamic (electrical overhead-)line rating is a great opportunity for transmission line operators (any voltage from 15 kV to 735 kV and over). It has been studied for more than 20 years inside CIGRE and IEEE working groups. But technology (sensors, weather forecast), as well as national or supra national rules, didn’t allow to generate a financial return for the owners of the line, which is nowadays (2018) possible in many countries. Moreover, the electricity generation mix is strongly changing in many countries due to the large deployment of wind and solar farms. The power flows are therefore changing a lot because of renewables and intercountry exchanges, where potential larger transit (owing to DLR) allows lower market electricity cost.

There are large opportunities owing to technology miniaturization (accelerometers, digital signal processing for embedded data treatment, telecommunications, etc.) as well as new development in weather forecasts (which are nowadays much more accurate and require less computational power than only a few years ago), including for these last, up to 72 hours ahead. Availability of this equipment and data for reasonable costs help to develop new sensors and systems which may be implemented into the EMS (energy management system) of national or local dispatching. These dynamic line rating sensors may sometimes warranty up to 30% more power transit in the monitored lines, depending on wind speed which is actually and locally evaluated by appropriate sensors using intelligent data processing.

The original development of sensors, including advanced treatment (reinforced learning for example) help to largely deploy these sensors on power lines.

Capacity forecasting seems to be the new promise of DLR, mixing on-line sensors with reliable weather data forecast, up to two days ahead.[16] In this field, significant social welfare benefits are expected inasmuch as capacity forecasts favour converging zonal prices and a decrease in congestion rents.

Computation methods and applicable standards[edit]

Direct measurement methods use field data measured to inform on line condition (conductor temperature, sagging, clearances) and on weather parameters (ambient temperature, solar radiation, wind velocity). Spans deemed "critical", i.e. likely to reach thermal or clearance limits first, are monitored in real-time, providing data on their status with respect to safety limits. The state change equation of the conductor, using its thermal features (absorptivity, emissivity) converts data on span geometry into valid temperature data for the "ruling span" of the monitored section. If weather conditions are more favorable than those used for the calculation of static limits, a margin probably exists. To be able to provide reliable and safe values, real-time parameters (load current, conductor temperature, weather parameters) are fed into the conductor's thermal model as per IEEE and CIGRÉ guidelines. Measurements, software and specialized algorithms are housed in a dedicated computer set up, forming a "DLR server", with communication facilities. After processing and formatting, "ratings" are made available to human operators via a user interface (computer displays and data files). Several kinds of ratings are available, from real-time, indicating ampacity immediately available, without time limits should conditions remain identical, to emergency ratings (higher ratings for a limited duration), to same-day or day-ahead forecasts, using sophisticated mathematical techniques and ever-improving Machine Learning algorithms.

Note 1: DLR computations deal with "near real-time" data, i.e. information updated more or less every 5 minutes. Shorter time cycles would require uneconomical computing power and would not make sense because of the thermal inertia of power transit phenomena. Furthermore, in current applications, DLR involves human-made decisions in control rooms of electrical utility companies, without automation.

Note 2: Weather parameters affect line ratings in decreasing order, as follows:

  • wind is the most important factor (reference neded)
  • ambient temperature (reference needed)
  • solar radiation (reference needed)
  • rain is not taken into account by standards (as of June 2018).

Standards published by IEEE and CIGRÉ cover the following subjects, necessary to perform DLR:

  • on conductors and thermal ratings: Thermal behavior of overhead conductors (TB207), Rating of overhead conductors (TB299, TB601), Thermal modelling of overhead conductors (IEEE738)
  • on dynamic line rating specifically (TB498).

Integration into control room processes[edit]

DLR translates into benefits when dispatch engineers apply optimized ratings to transit operations from their national or regional control center. Stand-alone DLR solutions provide real-time data for day-to-day operations and historical data for statistical analysis. The ultimate destination of a DLR solution is integration into control room tools and systems. Typically, the DLR server can be configured to send standard telecontrol frames to the electric utility's scada front-end acquisition units. These frames can then be processed for display and calculations by the utility energy management system or distribution management system. Short-term network operation decisions are based on optimized rating information, as well as load-flow calculations and economic dispatch scenarios. The latter also benefit from short-term forecasts in contingency analysis.

Communications, IT and cybersecurity considerations[edit]

Input data (weather parameters, circuit load, infrastructure design, field measurements) are public domain, proprietary if not confidential, and must be managed accordingly. Output data (line condition, ratings and forecasts) are definitely proprietary and confidential. To ensure provisions of the CIA triad, the utility and the vendor implement secured communications with cyphering, access control and restrictions. Industry trends favor deployments in SaaS (Software as a service) mode. Sensitive areas are:

  • communications between field devices and data concentrator (most likely an IT setup comprising a data server)
  • protection of the data and software (firewalls and anti-virus against unwanted access and malware)
  • uptime commitments through redundancy strategies.


  1. ^ David M. Greenwood, Student Member, IEEE, Jake P. Gentle, Member, IEEE, Kurt S. Myers, Peter J. Davison,Isaac J. West, Jason W. Bush, Grant L. Ingram, and Matthias C. M. Troffaes (2014), "A Comparison of Real-Time Thermal Rating Systems in the U.S. and the U.K.".IEEE Transactions on Power Delivery, Vol 29, No 4, August 2014
  2. ^ “The Ampacity of a conductor is that current which will meet the design, security and safety criteria of a particular line on which the conductor is used” (in CIGRÉ TB-498 - Guide for Application of Direct Real-Time Monitoring Systems)
  3. ^ Western Electricity Coordinating Council, pg.2-3, [https://www.wecc.biz/Reliability/2014_TEPPC_Transmission_CapCost_Report_B+V.pdf]
  4. ^ Bishnu P. Bhattarai, Jake P. Gentle, Timothy McJunkin, Porter J. Hill, Kurt S. Myers, Alexander W. Abboud, Rodger Renwick, David Hengst, "Improvement of Transmission Line Ampacity Utilization by Weather-Based Dynamic Line Rating", Power Delivery IEEE Transactions on, vol. 33, no. 4, pp. 1853-1863, 2018.
  5. ^ "IEEE 738-2006 - IEEE Standard for Calculating the Current-Temperature of Bare Overhead Conductors". standards.ieee.org. Retrieved 18 October 2018.
  6. ^ "Thermal behaviour of overhead conductors". e-cigré. Retrieved 18 October 2018.
  7. ^ Morozovska, Kateryna; Hilber, Patrik (2017). "Study of the Monitoring Systems for Dynamic Line Rating". Energy Procedia. 105: 2557–2562. doi:10.1016/j.egypro.2017.03.735.
  8. ^ Bishnu P. Bhattarai, Jake P. Gentle, Timothy McJunkin, Kurt S. Myers, Alexander W. Abboud, Rodger Renwick, David Hengst (2018). Improvement of Transmission Line Ampacity Utilization by Weather-Based Dynamic Line Rating. IEEE Transactions on Power Delivery. Vol 33, Issue 4, Aug 2018. Pg. 1853-1863
  9. ^ "Dynamic Line Rating for overhead lines" (PDF). European Network of Transmission System Operators for Electricity. 30 March 2015.
  10. ^ "Evaluation of Instrumentation and Dynamic Thermal Ratings for Overhead Lines" (PDF). Electric Power Research Institute. October 11, 2013.
  11. ^ Uski-Joutsenvuo, Sanna; Pasonen, Riku (February 28, 2013). "Maximising power line transmission capability by employing dynamic line ratings – technical survey and applicability in Finland" (PDF). VTT Technical Research Centre of Finland.
  12. ^ Morozovska, Kateryna (2018). Dynamic Rating of Power Lines and Transformers for Wind Energy Integration (Licentiate Thesis). KTH Royal Institute of Technology.
  13. ^ Estanqueiro, Ana; Ahlrot, Claes; Duque, Joaquim; Santos, Duarte; Gentle, Jake P.; Abboud, Alexander W.; Morozovska, Kateryna; Hilber, Patrik; Lennart, Söder; Kanefendt, Thomas (2018). "DLR use for optimization of network design with very large wind (and VRE) penetration" (Conference paper). Energynautics GmbH.
  14. ^ "Descapteurs sur les lignes électriques pour mesurer le vent et intégrer plus d'énergie renouvelable en Haute-Marne" (PDF). Rte-france.com. Retrieved 19 October 2018.
  15. ^ "Dynamic line rating - Elia". Elia.be. Retrieved 18 October 2018.
  16. ^ Michiorri, Andrea; Nguyen, Huu-Minh; Alessandrini, Stefano; Bremnes, John Bjørnar; Dierer, Silke; Ferrero, Enrico; Nygaard, Bjørn-Egil; Pinson, Pierre; Thomaidis, Nikolaos; Uski, Sanna (2015). "Forecasting for dynamic line rating". Renewable and Sustainable Energy Reviews. 52: 1713–1730. doi:10.1016/j.rser.2015.07.134.