Using short‐term resource scheduling for assessing effectiveness of CCS within electricity generation subsector
Bibliographic record
Abstract
A new methodology for assessing the effectiveness of carbon capture and storage (CCS) that does explicitly consider the detailed operation of the target electricity system is proposed. The electricity system simulation consists of three phases, each one using a modified version of an economic dispatch problem that seeks to maximize the producers’ and consumers’ surplus while satisfying the technical constraints of the system. The economic dispatch is formulated as a dynamic mixed‐integer nonlinear programming problem and implemented in general algebraic modelling system (GAMS). The generating unit with CCS is designed and simulated using Aspen Plus®. In the first case study, the operation of the IEEE RTS ’96 (Institute of Electrical and Electronics Engineers One‐Area Reliability Test System—1996) is simulated with greenhouse gas (GHG) regulation implemented in the form of CO2permits that generators need to acquire for every unit of CO2that it is emitted. In the second case study, CCS is added at one of the buses and the operation of the modified IEEE RTS ’96 is again simulated with and without GHG regulation. The results suggest that the detailed operation of the target electricity system should be considered in future assessments of CCS and a general procedure for undertaking this for any GHG mitigation option is proposed. Future work will use the novel methodology for assessing the effectiveness of generating units with flexible CO2capture. © 2015 American Institute of Chemical EngineersAIChE J, 61: 4210–4234, 2015
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".