Technological Change in Electric Power Supply Chain: Quantifying Economic Benefits of General Electric's GT11N2 M
Bibliographic record
Abstract
Abstract This paper examines General Electric's new combined-cycle gas turbine GT11N2 M upgrade. The new technology provides operational flexibility and promises output and cost efficiencies. To investigate the benefis of this technology, we propose a power supply chain model and construct cost functions for generation and service and maintenance using actual market and firm level data. The upstream firm is General Electric (GE) who invests in GT11N2 generators. The investment results in innovation of GT11N2 M upgrade facilitating different operational modes and efficiencies. The downstream firm is TransAlta's Sarnia plant which utilizes this new technology to produce and sell electricity to residential, small business, industrial, and wholesale market customers in Ontario, Canada. We quantify equilibrium prices and outputs under various efficiency rates in costs of fuel, service, and maintenance. We find a large variation in electricity generation depending on which operational mode ("Maximum Continuous Load" or "Performance" or "Lifetime") of GT11N2 M is selected. Under a mixed usage of all modes, we expect 44% output expansion to the industrial customers and 0.2% sales increase in the Ontario wholesale electricity market. Under this mode, GE's price should go down by 0.4% due to fuel cost efficiency. If GE's cost was $2.8 per MWh, GE should have asked Trans-Alta an average price of $5.822 per MWh for service and maintenance prior to the new technology. With the new technology, GE should charge $5.502 per MWh to Trans Alta. While GE's sales to wholesale market are almost stable, the sales to industrial customers increase nonlinearly in downstream efficiency rates. This shows that the amount of greenhouse gas emissions will be largely impacted by the choice of operational mode and how long it is used.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".