1. Introduction
Recent economic growth has resulted in a significant increase in electrical consumption, driving an increase in the demand for electrical power [
1,
2]. This will require the addition of more capacity in all parts of the electrical system, including generation, transmission, and distribution. However, the addition of new infrastructure not only increases costs, but also construction time [
3]. As a result, power utilities, government organizations, and professional bodies are exploring ways to optimally utilize the existing power system infrastructure [
4]. The main component that limits power transfer is not only the generation plant itself, but also the transmission lines that carry power from the plants to the substations [
3]. The ampacity of the conductors in transmission and distribution lines depends on the maximum allowable conductor temperature. Factors such as sag and tension often limit this temperature. The rating considered by the utilities, which is known as the “nominal rating” or “static rating (SR)”, is calculated by considering worst-case weather conditions, which are often very conservative. The weather parameters typically used to calculate the SR are a wind speed of 0.6
, a 40 °C ambient temperature, and a solar radiation of 1000
[
5]. It should also be noted that the simultaneous occurrence of these conditions is unlikely in real-world scenarios [
2]. Consequently, the SR of overhead lines is usually much lower than the actual ampacity of the line. The dynamic thermal line rating (DTLR) represents the actual current that the conductor could carry at a given time by incorporating real-time weather conditions. It allows transmission system operators to move more power from existing transmission corridors while maintaining a safe operating environment. Due to its many advantages, DTLR has recently been employed in many transmission and distribution applications and has also been an important topic of research.
Research on overhead conductors, which also leads to the topic of DTLR, goes back to the period before World War 2 [
6]. Around this time, several studies were developed regarding the heat transfer of conductors in still air, but a major experiment was based on the forced convection of conductors, conducted in 1930 by Schurig and Frick [
7]. The results were used until recently; however, the actual behavior of wind speed, direction, and gustiness made it challenging to accept their observed results. In 1959, House and Tuttle investigated the current temperature characteristics of ACSR conductors [
8], where a current-carrying capacity formula was derived considering the conductor’s heat loss and heat gain due to the effects of wind, solar radiation, ambient temperature, and surface conditions. However, the first academic article that applied the thermal rating of overhead conductors using real-time weather conditions [
9] was published in 1977.
Since then, this topic has been widely studied and researched in various geographical regions, mainly for alternating current (AC) systems and their applications, with proven results. In 2000, Raniga and Rayudu [
10] described a real-time application of the New Zealand transmission system using the line tension monitoring method. In 2008, the DTLR was calculated based on meteorological data for a 132 kV double circuit transmission line in England. It was found that 20% to 50% more wind energy can be incorporated [
11]. In 2011, a pilot experiment on a sag monitoring device known as ‘Ampacimon’ was conducted for a 400 kV twin conductor line in France. The real-time line ampacity was calculated, incorporating the measured sag measurements and most of the time resulting in the line having more capacity than the static rating by at least 20% [
12]. In 2013, a case study was conducted for a double-circuit transmission line in Korea to analyze the benefits of using DTLR. The results showed that the maximum allowable load can be increased by up to 135% [
13]. In 2015, the Idaho National Laboratory in collaboration with Alberta TSO, Altalink, conducted a study on a weather-based DLR system called GLASS, concluding that there is a minimum of a 22% increase in ampacity 76% of the time [
4]. In 2023, Glaum and Hofmann [
14] discussed a German case study on the potential of optimizing and using the grid capacity more efficiently. This was not just limited to overhead transmission systems; the researchers also extended this concept to applications for distribution systems [
15] as well as underground systems [
16]. Nevertheless, considering the previous research, the majority has focused predominantly on employing DTLR in AC systems and its presence in DC applications is limited. Borbáth et al. [
17] discuss how the dynamic capacity of HVDC interconnectors can allow HVDC system operators to increase their profits and provide faster investment recovery. However, a thorough investigation of the theoretical understanding of how a dynamic rating is achieved in HVDC interconnectors has yet to be conducted.
With the growing awareness of climate change and its negative impacts, many sectors that generate greenhouse gases (GHGs) are concentrating on reducing their emissions [
18]. Canada, along with many other countries, pledged to achieve net zero emissions by 2050 during the United Nations Climate Change Conference in Glasgow in 2021 (COP26). Therefore, the electricity sector, one of the major contributors to GHG emissions, is under pressure to adopt zero-emission power generation technologies [
19] such as renewable energy projects [
20] and green generation options including wind, solar, geothermal, biomass, and small hydro to reduce reliance on fossil fuels [
21].
With this recent movement of utilities towards renewable energy sources, significant attention has been paid to the incorporation of wind energy into the power system using DTLR, which uses cold weather and wind to cool down the overheated transmission lines, increasing their thermal capacity [
22]. A field study was conducted in N. Ireland to develop a statistical model to calculate the DTLR for a wind-intensive area [
23] using line current and weather data. Schell et al. [
24] presented a situation in Belgium, in which the Belgian TSO was required to add more wind power to their 70 kV network, which was already saturated with the traditional calculations. Talpur et al. [
25] investigate dynamic rating calculations for a 130 kV sub-transmission system to check the feasibility and best location to integrate a 60 MW wind power park in the same line. In conclusion, the relationship between wind energy and dynamic ratings holds the possibility of integrating more renewable energy while reducing network congestion.
With the expansion of electricity demand and the growth in renewable generation, the grid is becoming more decentralized, with some generation sources located at greater distances from consumers [
26]. As a result, utilities are exploring more economical long-distance transmission options, such as HVDC, which has brought the overloading capability of transmission lines in general and in HVDC systems in particular to our attention. The overload capability of HVDC can be categorized into three categories depending on the duration of overload: continuous, short-time, and transient. Known systems have a broad range of short-term overload capabilities, spanning from 1 to 8 h, and a transient overload range of 3 to 15 s. Line commutated converters (LCCs), which consist of thyristor valves, and voltage source converters (VSCs), which utilize insulated-gate bipolar transistor (IGBT) switches, have distinct overloading capacities. Notably, LCC-based HVDC systems have a higher capacity to withstand overload compared to VSC-based systems [
27]. The 50 MW overload capacity of the BritNed LCC-HVDC link [
28], 20 MW overload capacity of Nemolink VSC-HVDC [
29], 15 MW overload capacity of Eastlink-1 VSC-HVDC and 16 MW overload capacity of Eastlink-2 LCC-HVDC [
30] are some of the known examples of using the overload capacity of HVDC systems in real-time applications. However, when DTLR is implemented in HVDC systems, the overload capabilities of their components must also be considered. This rating depends on the thermal rating of key components of the system, such as the converter transformer, power electronic devices, and conductors, as well as the cooling systems and the ambient temperature [
31]. Extensive care must be taken if the converters are overloaded while the conductor operates at its dynamic rating.
Considering the current situation of renewables in the province of Alberta (southern Alberta is identified as more favorable for wind and solar), there is a need to transfer power over long distances from the south to the north. As Calgary, Red Deer, and Edmonton are the three largest cities in Alberta, responsible for one-third of the provincial load, transmission system operators plan to use the two HVDC lines, which are the Eastern Alberta Transmission Line (EATL) and the Western Alberta Transmission Line (WATL), as the transmission corridor between north and south [
32]. Further, the Alberta Electric System Operator (AESO) [
33] reports a maximum capacity of 3853 MW for wind and 1292 MW for solar. It is also evident that these numbers will increase significantly by 2035, as forecasted for different scenarios [
19]. According to the AESO, many renewable energy projects were added in 2021 and 2022, including 718 MW of solar projects and 1547 MW of wind power projects [
19]. After implementing these projects, there are further plans for additional solar and wind to come from corporate power purchase agreements (PPAs) and distributed energy resources (DERs). This raises concerns about whether the existing system is adequate to integrate additional clean energy generation without significant investment in infrastructure development.
In conclusion, the increase in electricity demand has resulted in an improvement in the existing transmission infrastructure as well as the need for long-distance transmission such as HVDC systems. The overview of DTLR, coupled with the examples of past studies, establishes a better understanding of the evolution of the technique and how it has been used in real-time networks. However, a notable gap in the literature surfaces—the existing studies on DTLR predominantly experiment with AC systems. This study steps into the spotlight by bringing in a novel approach: adding DTLR to HVDC transmission corridors, intending to maximize the utilization of their capacity and facilitate an increased integration of renewable energy. The primary objective of this study is to maximize the capacity utilization of HVDC transmission corridors and facilitate the increased integration of renewable energy by utilizing DTLR. The excess capacity gained by DTLR is used to integrate renewable energy into the grid to meet demand. In this way, GHG emissions can be reduced by replacing the energy supplied by conventional power plants with renewable sources, and this reduction can be quantified. This study contributes to the reduction in GHG emissions by facilitating the integration of more renewable energy into the generation mix, thereby advancing the goal of achieving net-zero emissions. Furthermore, this study encouraged the adoption of DTLR in future HVDC systems in practical aspects as well as from a research perspective.
6. Discussion
Integration of DTLR into HVDC transmission systems is of significant importance for several reasons. As illustrated in
Figure 7 and
Figure 9, the significant fluctuations in the DTLR reveal the dynamic interaction with time. In general, the winter months exhibit a greater increase in current carrying capacity above the SR. In contrast, there are some instances during the summer when the DTLR is lower than the SR. This is due to the elevated ambient temperatures in summer, which lead to higher line temperatures, causing a reduced line capacity. In contrast, during winter, lower ambient temperatures allow for more heat dissipation from the line, allowing for higher dynamic ratings. The influence of wind is crucial in DTLR by allowing more convective cooling during high wind conditions, potentially improving the capacity of the line. This might explain the variations in line ratings over the day or year.
According to the results described in
Section 5.1, on average, the line rating can increase from its nominal value by up to 64% during winter and 34% during summer. Consequently, the total line gains an additional capacity of 1766.68 MW during winter and 935.66 MW during summer. However, since the focus of this study is on an HVDC transmission line, it would not be possible to use all of the capacity provided by DTLR in all cases. The maximum additional capacity that can be used would be limited by the overloading capability of the connected equipment, especially the converters. On a positive note, as noted in the examples described in
Section 1, the overloading capability of HVDC systems is already being leveraged commercially. This presents an opportunity for HVDC transmission operators to use the advantages of DTLR to meet increasing demand and contribute to reducing GHG emissions.
Assuming a conservative 5% overload for converters for a brief period and considering the examples in
Section 1, this still gives a 100 MW additional capacity which can be used during peak demand hours during the day. As demonstrated by the case study in
Section 5.1, the excess capacity provided by DTLR is more than enough to compensate for the 100 MW allowed. However, more studies are needed to investigate the overloading capacity of the HVDC converters. Such explorations will enable the strategic utilization of the total excess transmission capacity provided by DTLR.
According to wind power generation calculations, on average, JWPP2 independently contributes 37.11 MW to the line (with the current configuration of 13 turbines). Additionally, the fluctuations in DTLR over 24 hours, as illustrated in
Figure 12, are correlated with the changing ambient conditions throughout the day. This provides valuable insights when the DTLR is higher and when it is lower. By aligning these fluctuations with wind power generation patterns (cf.
Figure 15), more renewable energy can be optimally dispatched to the grid without any curtailment. This strategic approach, based on DTLR indications of a higher capacity, enables more efficient integration of renewable energy.
Furthermore, from the turbine power output graph in
Figure 14, it is evident that the average power generated by a single turbine is less than its rated capacity. The average output slightly surpasses 50% of its nominal rating. This underutilization allows developers to construct much larger wind plants and utilities to connect them to the existing network. Furthermore, in this case study, it has been found that it would take 35 Enercon E-147 turbines to generate a power equal to the excess 100 MW achieved by DTLR. This represents a significant contribution to the reduction in greenhouse gas emissions and underscores the potential to scale up wind power projects to maximize their environmental impact.
7. Conclusions
As the electrical industry is facing challenges in moving forward with net-zero emission goals, efficiently incorporating more renewable energy into the grid becomes crucial. The surge in demand and the addition of more renewable energy to the system raise doubts about the sufficiency of the existing infrastructure and highlight the need for additional investments in new assets. According to the literature, maximizing the utilization of conductors has been predominantly studied in AC systems with the concept of DTLR. By identifying the opportunity to apply DTLR in HVDC systems for seamless renewable integration, this study aims to bridge this knowledge gap.
Essentially, the results show that incorporating DTLR into an HVDC line allows for more capacity than when using the SR of the conductor. Importantly, the excess capacity offered by DTLR changes with time, mirroring the changes in ambient conditions. These temporal fluctuations highlight the importance of selecting the right moment to employ dynamic capacity, allowing utilities to capture their full benefits. Allocating additional capacity for a few hours enables tapping more renewable energy into the grid. By doing so, pollutant-emitting coal-fired power plants can be replaced by renewable sources, thereby reducing GHG emissions.
Nevertheless, the realization of the full capacity offered by DTLR in HVDC systems is restricted due to the limitations of the overloading capability of the converters. This limitation unveils many opportunities to conduct more research on the overloading of HVDC converters to fully utilize the maximum possible benefits of DTLR. By factoring in the contribution from DTLR, developers and utilities can move forward with net-zero emission goals with significantly less investment in the transmission infrastructure while adding more renewable generation to the grid.
In conclusion, in this study, we have demonstrated a pioneering application of DTLR which is specifically tailored for HVDC transmission corridors. This study would be one of the first studies to apply the concept of DTLR to an HVDC transmission corridor utilizing a real-time case study and real-time meteorological data. Our findings highlight the importance of future research in this area to assess the converter capabilities and optimize the deployment of real-time weather data to enhance the efficiency, reliability, and sustainability of HVDC grids. It is also shown how the same technique used in traditional AC systems can be used for HVDC systems, but it significantly varies, necessitating consideration of additional equipment such as converters and associated control strategies. Additionally, the same concepts and benefits from utilizing DTLR in HVDC corridors are not only limited to renewable generation but can also be extended to other types of power generation, i.e., essentially any type of power injection.