Using Dynamic Thermal Rating of HVDC Transmission Corridors to Increase Penetration of Renewable Energy
Bibliographic record
Abstract
As the world shifts its focus toward achieving net-zero emissions, every contributor to greenhouse gas emissions, including the electricity industry, is transitioning to eco-friendly solutions such as renewable generation. Simultaneously, the significant increase in electrical consumption has also highlighted the need to increase the capacity of the transmission infrastructure. As a result, much attention has been paid to the large-scale use of renewable energy through high-voltage direct current (HVDC) transmission technology, ascribing to its economic feasibility. The growing demand for electricity and the increasing penetration of renewable energy sources has prompted the electric power industry to explore methods to optimize the use of existing grid infrastructure. Dynamic Thermal Line Rating (DTLR) is one of the promising techniques that allow transmission lines to operate close to their actual maximum capacity considering real-time operating conditions such as conductor temperature, sag, tension, and weather parameters. Numerous practical implementations and studies on this subject have been carried out thus far starting from the period before World War 2. However, the majority of existing research on this topic has been limited to employing DTLR in classical alternating current based power systems. To this end, this study presents a novel approach by employing DTLR for an HVDC transmission system to maximize the utilization of the transmission capacity and to improve the penetration of renewable energy. This approach can allow the transmission utility companies to expand their utilization of renewable energy integration to the generation mix while reducing or even avoiding capital investments into new transmission line infrastructure. The feasibility and performance of the proposed approach are evaluated by conducting a case study for an HVDC transmission line in Alberta, Canada. The study results find that, on average, the mean increase in HVDC line conductor ampacity rating above the static rating is 64% during winter and 34% during summer. This additional capacity is proposed to integrate wind energy, replacing coal-fired energy generation. This would lead to a significant reduction in greenhouse gas emissions, especially a 13.78 tons per hour reduction in carbon dioxide (CO2). Furthermore, the financial benefits comparison indicates an additional benefit of CA$ 0.10 M/GWh when using DTLR for enhancing the transmission capacity rather than the conventional line upgrading method. Ultimately, this study offers a practical approach to reducing greenhouse gas emissions by integrating more renewable energy resources into the generation mix and reducing curtailment. Furthermore, looking into the global scale, since the long distance transmission from HVDC is gaining more popularity, this will allow the utility companies to optimally choose the best locations where renewable sources are available even though they are either offshore or far away from the load centers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".