Electric power system cost/loss optimization using Dynamic Thermal Rating and linear programming
Bibliographic record
Abstract
Electric power systems consist of generation, transmission, and distribution components. As the demand for electricity grows seemingly endless, it is expected that a number of constraints, such as environmental, regulatory and economic, prevent the construction of new power plants and transmission lines. Finding improved ways to utilize the capacity supplied by existing power generation facilities and power transmission infrastructure is the problem that engineers, equipment manufacturers, and regulatory agencies are now facing. This paper introduces an optimization method using Dynamical Thermal Rating (DTR) and linear programming (LP) to minimize generation costs or transmission losses. DTR values are derived from a spatially resolved thermal model of the transmission system based on actual weather conditions along the line. This allows determination of line ampacity based on thermal bottlenecks that can exist at different locations along the line. The thermal model can also account for power losses more accurately, by considering actual distribution of temperature-dependent conductor resistance along the line. The model is used in a case study involving a simplified power transmission system with two types of generators, and a single load center. The simulation results show that more energy from hydro power plant can be transmitted to the load center, instead of using more expensive and polluting thermal generation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".