A Systematic Review of Dynamic Line Rating Methods: Qualitative and Quantitative Findings

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Data may be preliminary. 9 January 2026 V1 Latest version Share on A Systematic Review of Dynamic Line Rating Methods: Qualitative and Quantitative Findings Authors : Nuri Berisha and Enis Riza 0009-0009-8341-9248 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.176792241.15526108/v1 264 views 146 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Reliability of the transmission system is often questionable due to congestion, renewable energy resource integration, and market operations between neighboring countries. These issues require managing existing assets by balancing the system and, more commonly, by adding new power lines or transformers, which have high costs for transmission operators as well as environmental and social impacts on the landscape. A relatively new and modern method to address these challenges is Dynamic Line Rating (DLR). By on site sensing or forecast based modeling, DLR gathers weather data, mechanical information of conductors, and conductor temperature in order to allow power lines to operate not only at a fixed ampacity limit but also depending on actual weather conditions. In this way, costs of new assets be avoided, wind curtailment can be reduced, and congestion can be alleviated. In this paper, a systematic literature review of on site DLR tools installations, forecasting methods, and the opportunities and challenges of the DLR model has been conducted. Weather condition unpredictability, social and environmental safety from mechanical sag, and renewable energy opportunities have been analyzed. Other technical methods and impacts have been reviewed as well. The results indicate that DLR is a valuable tool for system operators to manage congestion, support market dispatch, integrate renewable energy, and reduce costs from new projects. However, challenges such as calculation errors, weather unpredictability, and socio-environmental safety require further study and improvement before wider deployment. A Systematic Review of Dynamic Line Rating Methods: Qualitative and Quantitative Findings Nuri Berishaᵃ, Enis Rizaᵇ * ᵃ University of Pristina, Faculty of Electrical and Computer Engineering, Bregu i Diellit, p.n. 10000 Pristina, Republic of Kosovo. Email: [email protected] ᵇ University of Pristina, Faculty of Electrical and Computer Engineering, Bregu i Diellit, p.n. 10000 Pristina, Republic of Kosovo. Email: [email protected] * Corresponding author: Enis Riza, Email: [email protected] Abstract Reliability of the transmission system is often questionable due to congestion, renewable energy resource integration, and market operations between neighboring countries. These issues require managing existing assets by balancing the system and, more commonly, by adding new power lines or transformers, which have high costs for transmission operators as well as environmental and social impacts on the landscape. A relatively new and modern method to address these challenges is Dynamic Line Rating (DLR). By on site sensing or forecast based modeling, DLR gathers weather data, mechanical information of conductors, and conductor temperature in order to allow power lines to operate not only at a fixed ampacity limit but also depending on actual weather conditions. In this way, costs of new assets be avoided, wind curtailment can be reduced, and congestion can be alleviated. In this paper, a systematic literature review of on site DLR tools installations, forecasting methods, and the opportunities and challenges of the DLR model has been conducted. Weather condition unpredictability, social and environmental safety from mechanical sag, and renewable energy opportunities have been analyzed. Other technical methods and impacts have been reviewed as well. The results indicate that DLR is a valuable tool for system operators to manage congestion, support market dispatch, integrate renewable energy, and reduce costs from new projects. However, challenges such as calculation errors, weather unpredictability, and socio-environmental safety require further study and improvement before wider deployment. Keywords: Dynamic Line Rating; Transmission Congestion Management; Renewable Energy Integration; Forecasting Methods; Conductor Ampacity; System Reliability. Introduction Starting from the growing need for increased production capacity due to rising consumption, transmission system operators require modernization by applying different methods to enhance the power transfer capability of transmission networks. Traditionally, the design and construction of transmission lines has been based on the static line rating (SLR), where the main operating parameters of the conductor were considered fixed under worst-case weather conditions. On this basis, simulations and designs were carried out, and the line was commissioned with a fixed maximum carrying capacity, regardless of varying climatic conditions [1]. By using new methods, such as the Dynamic Line Rating (DLR), the system can operate under real conditions. Through sensors measuring temperature, wind, or conductor sag, real time data is collected, allowing conductors to carry power beyond static limits when atmospheric conditions permit. This enables an increase in the line’s transfer capacity and the amount of power flowing through it, whether generated from renewable sources or imported through interconnections with neighboring countries. In this way, the efficiency of the transmission grid can be significantly improved. In recent years, the need to integrate more renewable energy during peak periods, along with requirements for reliable market operation, has made DLR analysis increasingly important for short-term asset management and planning. Research has focused on how to optimize transmission capacity without new investments, instead relying on weather forecasts, environmental data, and modern technologies. DLR has been shown to significantly raise transfer capacity [2]. Some reports also highlight efficiency improvements without line-mounted sensors, using instead meteorological station data (temperature, radiation, and wind) to evaluate line capacity during congestion [3]. Allowing transmission lines to operate above manufacturer catalog limits risks overloading or underloading. Therefore, stochastic models using for example Monte Carlo Markov Chain algorithms [4], [5], [6], [7] are applied, using algorithms and past climatic records to generate probabilistic scenarios of carrying capacity. This method also has challenges, as missing variables may reduce certainty. For this reason, real sensors deployed on line sections and integrated with SCADA, are recommended for safe real-time monitoring and control [8]. In addition to line ratings, net load forecasting is equally important. This considers system conditions, weather patterns, and the operating states of renewable resources using different forecasting methods that directly improve system reliability [9]. However, safety must always remain a priority: models of networks under normal and manufacturer specified conditions must integrate dynamic characteristics to avoid overloading risks [10]. Transitioning from static to dynamic ratings, if not managed properly, can lead to premature aging of transmission assets. Hence, thermal dynamic modeling with mathematical and intelligent algorithms is necessary to maximize efficiency and ensure safe application [11]. The expected benefits of stochastic approaches include increased transfer capacity, improved supply security, and higher system efficiency. By using historical data variables for planning, system operation can be managed more successfully, reducing risks of overload or underload. Traditionally, system operators have relied on investments in new assets and other technical solutions, such as the installation of energy storage systems and compensators, in order to avoid congestion and to enable greater penetration of renewable energy. However, all of these measures involve significant costs as well as environmental impacts. For this reason, the necessity of applying Dynamic Line Rating (DLR) has increased. As has been reported from ENTSOE [12], many European transmission operators has started using DLR as their new increasing capacity methodology, ENTSOE has prepared a framework on how to put DLR in operation in a safety and reliability way [13]. The most common topic in all the research studies was the use of models and standards for DLR algorithm calculations. These are widely known as IEEE and CIGRE standards [14], [15] which are now very useful for forecasting and predicting power line capacity by considering weather conditions, conductor mechanical stresses, and other environmental factors. In this systematic literature review, the PRISMA methodology has been applied to identify and analyze the final set of studies and reports. A total of 323 papers and reports were screened, with duplicates, papers without a DOI, non open access publications, and those published before 2013 excluded. At the end of this process, 59 journal papers and 9 thesis and reports were selected for further analysis. Section 2 of the paper describes the methodology used for the SLR. Section 3 presents the technical aspects, organized into seven subgroups: forecasting, weather conditions, safety and reliability, renewable penetration, monitoring and improvement, benefits, and challenges of the DLR methodology. Methodology of the study and the overview of publications Collecting a large number of papers in order to compare approaches in Dynamic Line Rating (DLR), a total of 323 journal papers and 9 reports and thesis were gathered. All papers were identified through four different databases: Research Gate, Google Scholar, Semantic Scholar, and Google. After reaching 323 papers, the search results began to show repeated and overlapping topics, with significant duplication and saturation, so it was decided to stop further searching. The main search keywords were: “DLR”, “Dynamic Line Rating”, “Dynamic Thermal Line Rating”, and “DLR vs SLR”. After collecting all papers, the PRISMA flow diagram was applied in order to show the excluded papers based on the inclusion/exclusion criteria defined in this study. The exclusion criteria required removing papers with no DOI, without open access, published before 2013, or identified as duplicates. After filtering, 59 papers a 9 thesis and reports were listed as credible sources using the PRISMA method. A final verification of their DOIs was conducted by checking each in Scopus, IEEE Xplore, and thesis and papers from best universities and USA Energy Department, and other reliable databases to ensure all selected papers were indexed, and thesis and papers are highly reliable. As is showed in the Figure 1. Paper published per year regarding DLR from 1983 to 2025 the number of publications starting from the earliest one found in 1983 up to 2025. As shown, from 1983 until 2011 the number of DLR-related publications remained relatively low, while from 2011 onwards the number of studies increased significantly. This trend is one of the main reasons why technology is often considered relatively new. Figure 1. Paper published per year regarding DLR from 1983 to 2025 Some of the papers that were not initially Open Access were double-checked through Google by searching repeatedly until they were either found or confirmed unavailable from other sources. In several cases, papers listed as non–Open Access in databases such as Elsevier or IEEE Xplore were eventually accessed through institutional websites, for example, some papers from the University of Birmingham. Some papers were obtained through “Request for Access” options or by directly emailing the authors. The idea of methodology has come through reviewing similar papers and related journals [16]. The PRISMA flow diagram [17] was used only to illustrate the inclusion criteria, not as a methodology itself, but rather as a process tree or algorithm showing how the papers were screened and selected. As shown in Figure 2. PRISMA flow of paper exclude and include methodology, because the number of eligible journal papers was not very high, an additional 13 papers, meeting the criteria of having a DOI, being open access, and published after 2013 were included. Alongside this, 9 new reports were also added to strengthen the review base. Figure 2. PRISMA flow of paper exclude and include methodology After checking the titles of the papers and their main topics, including study cases, reviews, experiments, and other approaches, the number of papers related to this study is shown in Table 1. Grouping of all references in the main topic of title. The table also illustrates how each reviewed paper is categorized under the main topics of the SLR, namely forecasting, weather conditions, safety and reliability, renewable penetration, monitoring and improvement, as well as the benefits and challenges of Dynamic Line Rating. Forecasting [18], [19], [11], [20], [5], [21], [22], [23], [24] Weather Conditions [25], [26], [3], [6], [27], [28], [29] Safety and Reliability [30], [31], [32], [33], [34], [35], [8], [36], [37] Renewable Penetration [13], [38], [39], [38], [40], [41], [42], [43], [44] Monitoring and Improvement [5], [45], [46], [47], [48], [49], [50], [51], [52], [53], [54], [55] Benefits and Challenges [56], [57], [58], [59], [60], [61], [62], [63], [64] Thesis and Other Reports [22], [65], [66], [67], [68], [69], [70], [71] Table 1. Grouping of all references in the main topic of title Analysis of Key Technical Themes in DLR In order to review all the papers identified, based on their DLR related titles and contents, the selected studies were grouped into seven thematic categories. These categories were derived directly from the papers titles, methodologies, research results, and technical focus areas. All reasons for the categorization are presented in Table 2. Thematic Grouping of DLR Literature With Qualitative and Quantitative Findings. Forecasting In this study [18], a Line Graph Convolutional LSTM model was applied to improve forecasting accuracy under unpredictable weather conditions. This model captured spatial correlations of DLR along the line and significantly improved prediction results. According to [19], different forecasting methods for DLR were reviewed and compared with climatic data along line routes. These included real-time monitoring, ambient-adjusted daily and hourly ratings, very short-term forecasting (1–6 hours), and day-ahead forecasting. Real-time forecasting was shown to be the most successful, especially where new investments would be costly or environmentally harmful. In this reference [11], a machine learning approach was developed for hour-ahead probabilistic prediction of DLR. Using a large dataset of historical and latent weather data, the study demonstrated improved system efficiency and reduced risks of overheating transmission assets. In their review [20], state-of-the-art forecasting technologies for DLR were presented, including the use of sensors, weather stations, sag monitoring, and mechanical stress measurements. These methods were shown to enhance both safety and reliability. The analysis by [5] applied a stochastic forecasting model based on IEEE 738 to calculate dynamic parameters of power lines. The results showed that high wind penetration could be achieved by transitioning from static to dynamic parameters. However, risks such as conductor overheating within 15–30 minutes of DLR operation were identified. First steps toward DLR from traditional SLR have been started with the earliest approach of studying probabilistic forecasting. In Ref [21] the problem of the grid operators who strictly stayed at their conservative model of static rating and put the system at risk due to grid capacity has been addressed. The methodology used takes into consideration weather uncertainty and variation of the conductor ampacity by using a probabilistic model; thus, this model can be used in the day-ahead operations of the system operators. The study has taken place on a 230 kV line in Colorado, US and it was very accurate and has given the operators confidence intervals of increasing capacities. As forecasted approach of DLR in many cases is the main method of risk evaluation and benefit gain model, in their paper [22] a computational fluid dynamics system by using two datasets in Idaho desert line, Columbia River Gorge and New York has been analyzed. The results showed forecast errors of 11%, 15% and 7% in the relevant towns mentioned. In Ref [23] methods for predicting conductor ampacities during weather uncertainties in Spain have been analyzed. A time series forecasting and stochastic model with safety margins has been used. In the short term, this method leads to a forecast error which is a good margin for the system operators to maintain the system safe during overloading of the capacities based on weather conditions. In their study [24], a baseline scenario from real world has been used and based on this, three future scenarios on energy demand increase have been studied. Analysis has been done on how using DLR instead of SLR can increase the grid capacity and also reduce costs. In the base scenario in DLR terms a capacity increase of 20% and cost reduction of 67.31% resulted. In future scenarios, as one example with Power Flow scenarios, if demand increased by 20%, capacity increased 53.38% with DLR. Weather Conditions In this study [25], geographic information systems combined with weather data were used to calculate DLR. Unlike many other works, climate data were collected across multiple locations along a line, producing varying results. To ensure reliability, the lowest calculated value was chosen for operation. According to [26], a DLR model applied in rainy conditions highlighted how Joule losses and ambient environmental factors affect conductor heating. Comparisons between in-line measurements and static reference testing confirmed that relying on a single constant value (e.g., one location) could lead to mismanagement of load capacity. In their study [3], indirect weather-based approaches were used, including weather station data, forecasting models, and conductor thermal models. A case study in Ireland’s 110 kV system showed that capacity could increase by 0.2 pu in 75–95% of operating hours. In this reference [6], a Monte Carlo error-based method was used to quantify the effect of weather uncertainties on DLR. The findings emphasize that unpredictable weather must be accounted for in planning and real-time operation. In order to not invest in new assets, a study case realized in Sri Lanka [27], where weather conditions such as ambient temperature and wind speed effects on effectiveness of the network for capacity increase has been carried out. The researchers wanted to know the effect of different samples in minimizing errors, so they first collected data and then did this in different samples as 1s, 5s, 12s, 20s, 30s and 60s as sample intervals of temperature and wind speed methodology. A set of samples have been collected; for example, for 1-second sampling time error is almost 0. And also, for 5 to 20s sampling the errors were in lower limits and these samplings have been appointed as good parameters for minimizing errors in DLR. Besides all other forecasting variables, wind speed is also less studied in the impact of the DLR model. In their paper [28] through a mathematical model of series Fourier, a model has been designed by knowing the effective wind speed to make DLR more effective and to increase accuracy of the model. By series Fourier the predictive modules have been done for 10 minutes ahead forecasts and in that way a significant reduction of conservative bias in traditional approaches has been achieved. This method of effective wind speed is a cooling method to minimize the errors due to system overheating. Meanwhile, in Ref [29], modeling a Monte Carlo simulation with combinations of weather variables from real world across all line routes and conductor temperature combined with many uncertainties came from real scenarios in order to have enormous variables and in that manner reduce the risk of uncertainties of weather in the power line work. Multi-parameter analysis seems to be mandatory in order to reduce weather uncertainty risks. Safety and Reliability In this study [30], DLR reliability was tested through conductor temperature estimation. Two cases were assessed: one with 24-hour profiling of units where conductor temperature was dependent on operating conditions, and another using Monte Carlo simulation. It was observed that ampacity estimation errors could exceed 150%, surpassing technical standard limits. According to [31], cyber-physical failures in ICT networks were modeled to analyze their impact on DLR reliability. Using Monte Carlo and Markov models, it was found that ICT failures could increase generation costs in IEEE 24-bus systems and reduce reliability in IEEE 14-bus systems. In this paper [32], two methods for improving safety under DLR were examined: field measurements and algorithm-based simulations. In a 400 kV test line, capacity increased by 20–30% annually when these methods were applied. As reported by [33], both direct and indirect DLR monitoring methods were evaluated, including sensors, communication infrastructure, management systems, and analytical tools. The study highlighted that phasor measurement units are essential for critical spans during adverse weather. During the literature review, almost 99% of the studies analyzed only dynamic rating of the power lines. In Ref [34] Dynamic Line Rating and Dynamic Transformer Rating models have been studied in order to have a more real view on how renewable energy and other generation sources could be penetrated in the grid by using dynamic variables. Transformer dynamic rating has also shown a decrease of transformer overload by moving to dynamic rating. One huge report that gives real results from a real project is studied in the US at the Oncor project in Texas, real sensors for DLR forecasting in eight overhead lines have been installed. “The project proved that line capacity was 6–14% higher for 345 kV lines and 8–12% higher for 138 kV lines more than 80% of the time.” [35] Among all US transmission systems, in 2016 the total cost of managing congestions by investments has been around 4.8 billion dollars [8], and this report concluded that while considering this amount of investment cost in order to manage congestions by investing in generation units or new assets for transmission by increasing new capacities, DLR solutions have resulted very useful and costless solutions in order to decrease congestions in the system. In a policy report [36] the cost-benefit of reconductoring, rebuilding and DLR of an overhead line system has been compared and concluded that for reconductoring and rebuilding it needs outage, cost over 0.5 million per mile per reconductor and 2–3 million per mile to rebuild, while DLR cost in total is less than 1 million. Extended capacity from reconductoring reached +34%, rebuilding +106% and DLR +10% to 30%. The study in [37] developed a cyber physical power system model incorporating real sensor data, sensor failure behavior, and their combined technical and cyber impacts on network operation. Various sensor placement strategies were tested across IEEE bus systems, and a meta heuristic optimization method was applied to minimize the number of sensors required for reliable DLR estimation. The results showed that optimized placement reduced communication latency by 25% in the IEEE 24-bus system. In the IEEE 72-bus system, annual operational availability increased from approximately 4500 hours to 6500 hours when cyber network failures were mitigated through optimized DLR sensor placement. The study emphasizes the importance of cyber physical resilience in future DLR implementations. Renewable Penetration In this study [38], Renewable Energy Zones were analyzed. Results showed that shifting from static to dynamic ratings increased the carrying capacity of a 275 kV line from 1700 MW to over 2800 MW during peak demand, reducing the need for new investments. According to [39], DLR plays a crucial role in transmitting renewable generation when combined with flexibility options such as demand response, electric vehicles, pump hydro storage (PHES), and other energy storage systems. Stochastic modeling demonstrated that PHES is the most efficient option for dispatching excess wind power. In their work [40], the unit commitment problem during congestion was studied under wind integration. Using Monte Carlo modeling with IEEE RTS 24-bus simulations, it was shown that by increasing conductor temperature by 7°C, load capacity rose by 37%, reducing both costs and curtailment. In this paper [41], coordinated investment strategies for wind-rich regions were explored, combining DLR with static series compensators and storage. Using IEEE RTS 24-bus, the results showed that DLR and compensators could significantly mitigate congestion compared to storage alone. Study case [42] in an integration of new 60 MW wind turbines into a 130 kV sub-transient overhead line in the Fortum transmission operator by integrating DLR to VL3 (conductor phase 3). By implementing DLR across conductor upgrading and new line construction the benefits of SLR have resulted in 0.29 Million Swedish Krona per GWh compared with 0.14 MSEK per GWh by conductor upgrading and 0.09 MSEK per GWh by constructing new line. ENTSO-E as the main regulator of all European transmission operators, in their report with analysis of DLR in 15 TSOs [13], where some TSOs used meteorological data from stations, some others used sensors for temperature monitoring, some thermomechanical models, fixed limits, vibration models etc. The results showed that many 400 kV and 220 kV lines are overloaded. DLR has been accepted as solution for existing infrastructures and not for grid development. Some countries which built their network 40 years ago have old calculation models that can be improved via DLR, and finally it was concluded that DLR can increase the capacity of system for short period of time and must take into consideration uncertainty of weather. The paper [43] evaluated the integration of Dynamic Thermal Rating with energy storage systems and network topology reconfiguration to improve wind power accommodation in transmission networks. The study examined operational scenarios in which DTR increases available line capacity, while battery storage mitigates wind curtailment. A decision algorithm determines when DTR should be activated, when storage should be discharged, and how to maintain system reliability during fluctuating wind conditions. The results showed that combining DTR with energy storage significantly enhances the capability of existing networks to integrate wind generation. A similar approach was presented in [44], where the Smart Regulation Reliability Index (SRRI) was used to assess the effect of Vehicle-to-Grid (V2G) integration. When wind generation exceeded demand, EV batteries were charged, during shortages, the batteries supplied energy back to the grid. In combination with DLR, this strategy reduced energy not supplied by 28.8% and increased the SRRI by 12.9%. Weather data and EV charging profiles were incorporated into the IEEE 738 thermal model to obtain the reported results. Monitoring and Improvement In this reference [45], phasor measurement units were used to measure conductor temperature across three phases. The advantage was avoiding costly temperature sensors while maintaining ±5% accuracy. PMU data were then applied for load-flow forecasting based on DLR. In this study [46], different monitoring systems were compared, including conductor cooling methods. Results indicated that such approaches enhance line capacity and improve wind integration under variable conditions. According to [47], literature reviews confirmed that DLR benefits system reliability through methods such as sag measurement, climatic analysis, and real-time monitoring. The analysis by [48] demonstrated that DLR can serve as a tool for balancing system imbalances. In the other study [5], Monte Carlo-based simulations showed that accurate scheduling under DLR requires large datasets, while smaller sample sizes resulted in unreliable predictions. In this work [49], machine learning was applied to sensor data for day-ahead predictions. Results showed that deviations in utilization limits dropped from 18% to 5%, enhancing planning accuracy. In ref [50], DLR was incorporated into Security Constrained Unit Commitment using IEEE 6-bus and 118-bus systems. The findings demonstrated improved efficiency and asset management when scheduling reconfigurable grids. According to [51], Spanish pilot projects on both underground and overhead lines confirmed cost reductions and improved reliability. Meteorological stations and inclinometers validated the results against real data. In this reference [52], a prototype non-contact cooling tester was designed and tested as a low-cost alternative to DLR sensors. Results showed that conductor temperature management through ambient conditions and wind cooling increased line capacity more effectively than SLR. A monitoring project has been done by Southern California Edison company, and a pilot project report [53] has shown that monitoring of overhead line weather conditions by installing sensors, and the project was not used to install but only to check the possibilities of monitoring the sensor through SCADA by installed RTU which has been achieved. Power Transfer Distribution Factor (PTDF) as a corrected method together with vast calculation of DLR from three different models with IEEE bus systems and real data has been proposed as a real-time power market clearing model [54] as a very efficient solution while DLR is going to be used. One of the most important aspects of Dynamic Line Rating is the identification of critical spans along transmission routes. In the study by [55], the authors analyzed long overhead line routes to determine which spans experience the most severe thermal or weather related stress. Identifying these spans is essential for optimizing sensor placement, as each span is exposed to different meteorological conditions that influence conductor loading. In their work, 25 weather stations were installed, each representing a critical span, allowing precise monitoring of conductor temperature, sag, and environmental behavior. By identifying these spans, the study-maintained ground clearance under high loading conditions, reduced the risk of conductor overheating, and minimized conductor ageing. The collected data were then applied across the entire line to create a more accurate and reliable DLR profile. Benefits and Challenges In their study [56], corona losses were analyzed and shown to have measurable impacts on DLR. Although accounting for less than 2% of Joule losses, they reduced maximum current capacity by around 1%, indicating they should not be ignored. According to [57], although DLR can improve efficiency and reduce costs, external environmental stress accelerates conductor aging, reducing durability of ACSR conductors. In ref [58], corrective control measures were implemented in pilot projects to address forecasting errors. The study confirmed that redispatch strategies and error corrections improved overall trust in DLR operation. In this paper [59], the integration of Virtual Power Plants with DLR was studied, where conductor temperature adjustments and ampacity optimization using IEEE 33- and 123-bus systems removed transmission barriers and improved flexibility. As shown by [60], the ERCOT transmission grid case demonstrated that DLR doubled benefits compared to ambient-adjusted and static ratings in terms of CO₂ reduction, cost savings, and renewable integration. In this study [61], financial assessments showed that integrating wind energy through DLR reduced overall system operation costs by 14.8% in a 24-bus system. In Ref [62] a review of the impact of DLR on consumption and cost as well as risks of this model due to unpredictable generation and consumption and weather conditions has been concluded. As a result, it has been shown that DLR can help increase the capacity for a period of time until investments are made, so DLR has been concluded as a transition solution until new assets take place. In order to minimize the risk of many uncertainties in the system starting from weather conditions, wind fluctuations, load and consumption, a model of IEEE-RTBS 6-node system and the IEEE-RTS79 system are employed to validate the correctness and effectiveness [63] by using MATLAB and other computational models and as a result the combination of methods using different variables of uncertainty has shown that the system will behave more safely even in these circumstances. A novel integration of Dynamic Line Rating with an Operational Tripping Scheme (OTS) was presented in [64], enabling higher renewable-energy penetration into the transmission system. The method uses a fuzzy-logic-based OTS to determine which generators should be tripped during congestion events. By incorporating DLR into the scheme, the system avoids unnecessary generator disconnections that would occur under static line limits. Instead, generators continue operating according to the dynamically available thermal capacity of the lines. This approach, demonstrated on the Saudi Arabian transmission network, significantly improved system stability and allowed greater utilization of renewable energy sources. Thesis and Other Reports In view of the methods regarding the DLR analysis, by studying the impact of the sensors and aging of the conductors in the DLR process by using IEEE 738 and CIGRE standards, and by comparing the results of these two standards which are widely used and resulted to be almost the same [65]. In Ref [66] concentrated on the impact of the DLR in increasing the share of renewables into the grid, reducing cost for dispatch, and improving performance of the system, which resulted with positive potential. A pilot project from Ellevio AB and Heimdall Power has been analyzed in [67] and due to line temperature measurements malfunctioning, the DLR analysis was weather-based and showed an increase of capacity of 23% when DLR was used. Meanwhile [68] prediction of DLR in two-day ahead and one-day ahead has been concluded. Error profile resulted with 9% for day ahead and 11% for two days ahead. In Ref [69] a LabVIEW environment and IEEE 738 standard have been created in order to compare SLR and DLR and this also led to a DLR benefit from SLR. A Topical Report [70] demonstrated that real projects increased capacity by 25% by using DLR. On the other hand, [22] Computational Fluid Dynamic simulations with accurate wind field and demonstrated results of identifying capacity of conductors have been presented. The High-Resolution Rapid Refresh (HRRR) model points over a 2-year span within a region in south eastern Idaho have been analyzed using 4, 10, 17, 26, and 35 HRRR model points along two transmission line paths and the results showed that by increasing number of HRRR line ampacity decreased. Another study [71] used HRRR in their model to study the risks of using DLR in the grid. Study case has been done in two existing overhead transmission lines in New York, and they used historical HRRR hour-ahead and day-ahead weather forecast and it was seen that uncertainties will lead to overload. 1. Forecasting Real-time and day-ahead models such as LSTM, ML, stochastic and CFD-based methods improved prediction accuracy, captured spatial correlations, detected overheating periods, and enhanced operational decision-making through forecasting insights [18], [19], [20], [21], [23]. Forecast errors of 11%, 15%, and 7%. Overheating risk 15–30 min. DLR base scenario increased capacity by 20% and reduced costs by 67.31% [11], [5], [22], [24]. Grouped because these studies develop or apply forecasting and predictive models for DLR estimation. 2. Weather Conditions Emphasis on weather variability, multi-parameter meteorological inputs, spatial differences, GIS impacts, and uncertainty propagation. Highlighted need to avoid single-point weather measurements [25], [26], [3], [27], [28], [29]. Capacity increase 0.2 pu in 75–95% of hours. Sampling error ~0 at 1s; stable at 5–20s. Effective wind models improved performance [3], [27], [28], [6]. These papers analyze the influence of ambient temperature, wind, rain, and sampling intervals on DLR. 3. Safety and Reliability ICT failures, cyber vulnerabilities, sensor reliability, and operational risk factors affecting DLR trustworthiness. DLR as cost-effective alternative to reconductoring. Cyber-physical resilience emphasized [30], [31], [32], [33], [34], [8], [36], [37]. Ampacity errors up to 150%. 20–30% capacity increase (400 kV). 6–14% increase (345 kV). 25% latency reduction; availability improved 4500h to 6500h [30], [32], [35], [37]. These studies evaluate risks, failures, cyber-physical effects, and reliability impacts of DLR. 4. Renewable Penetration DLR reduces wind curtailment, increases renewable output, integrates storage and V2G, and enhances flexibility through improved line capacity. EV-V2G coordinated with DLR increases system reliability [13], [38], [39], [41], [42], [43]. 1700 MW to 2800 MW line capacity. 37% capacity increase with +7°C conductor temperature. 0.29 MSEK/GWh benefit. [44] ENS reduction 28.8%, SRRI +12.9% [38], [40], [42], [44]. Group reflects all papers focused on renewable integration supported by DLR. 5. Monitoring and Improvement PMUs, temperature sensors, inclinometers, ML-based monitoring, and prototype technologies demonstrated improvements in real-time DLR applications. Critical span identification improved sag control and reduced ageing [45], [46], [48], [51], [52], [53], [54], [54], [55]. PMU accuracy ±5%. ML deviation reduction 18% → 5%. New cooling prototypes improved performance. 25 critical spans identified [45], [49], [50], [52], [55]. These papers focus on practical monitoring technologies and techniques for improving DLR accuracy and implementation. 6. Benefits and Challenges DLR enhances system flexibility, reduces curtailment, and provides transitional benefits. Challenges include corona losses, uncertainty, conductor ageing, and dynamic operational limits. Fuzzy DLR–OTS reduced unnecessary generator trips [56], [57], [58], [59], [62], [63], [64]. Corona losses ~1%. DLR doubled benefits vs SLR. 14.8% cost reduction. Additional model validation metrics [56], [60], [61], [63]. Papers assessing the net benefits and technical challenges of implementing DLR. 7. Thesis and Other Reports Comparative case studies, standard evaluations (IEEE 738 vs CIGRE), and renewable integration assessments. Provide methodological foundations [65], [66], [69], [70], [71]. 23% capacity increase in pilot project. Forecast errors 9% (day-ahead) and 11% (two-day). Real projects show ~25% capacity increases [67], [68], [22]. Classified separately because these are thesis, project reports, or non-journal documents with supportive methodologies. Table 2. Thematic Grouping of DLR Literature With Qualitative and Quantitative Findings Conclusions and Future Works In this systematic literature review, a year of publication filtering process has been applied, where papers from 2014 to 2025 were selected and reviewed on the basis of moving from traditional power line parameters to dynamic line rating (DLR) parameters. As shown in this review, DLR has a very high impact on increasing the capacity of transmission networks at relatively low cost, and in that regard, it also contributes to CO₂ emission reduction due to renewable energy penetration into the grid, but CO 2 emission reduction has not been involved in any paper reviewed. Another important aspect revealed in the reviewed papers is that site implementation through sensors and meteorological stations installed along the line route, together with algorithms and mathematical models based on IEEE and CIGRE standards as well as machine learning approaches, can lead to a significant increase in capacity while keeping the system safe and reliable. However, challenges remain, particularly with weather uncertainty and errors in calculations. As a direction for future work, minimizing calculation errors and addressing the research gap regarding the impact on power transformers and other network parameters, such as protection relays, when increasing the capacity of power lines will be essential for further improving safety, reliability, and risk management of other assets. Conflicts of interest The authors declare there is no conflict of interest. Author contributions The research idea was initiated by Prof. N. Berisha, who structured the overall paper model and guided the research approach. E. Riza conducted the paper search, designed the review structure, and prepared the written manuscript. Acknowledgments NA References [1] R. Martinez et al. , “Dynamic rating management of overhead transmission lines operating under multiple weather conditions,” Energies (Basel) , vol. 14, no. 4, Feb. 2021, doi: 10.3390/en14041136. [2] R. Peña, A. Colmenar-Santos, and E. 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Keywords capacity planning (manufacturing) climate mitigation conductors (electric) electrical conductivity ieee standards thermal conductivity transformers transmission lines and cables transmission networks Authors Affiliations Nuri Berisha University of Prishtina View all articles by this author Enis Riza 0009-0009-8341-9248 [email protected] University of Prishtina Hasan Prishtina View all articles by this author Metrics & Citations Metrics Article Usage 264 views 146 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Nuri Berisha, Enis Riza. A Systematic Review of Dynamic Line Rating Methods: Qualitative and Quantitative Findings. Authorea . 09 January 2026. DOI: https://doi.org/10.22541/au.176792241.15526108/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. 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