Dynamic modeling and optimal control schemes for an offshore-wind powered direct air capture system with energy storage options
Bibliographic record
Abstract
Direct air capture of CO 2 is a technically feasible solution for reducing atmospheric CO 2 concentrations at-scale, building on decades of global research. However, powering such systems with CO 2 -intensive fossil fuels results in reduced net CO 2 capture. This paper proposes an offshore-wind energy powered atmospheric CO 2 capture system. A key challenge is the variable nature of renewable wind-energy to meet direct air capture (DAC) system power requirements. One solution to mitigate this challenge is to integrate the wind- CO 2 capture system with advanced Energy Storage Systems (ESS). Previous research in this direction has been carried out, however the optimal ESS is still an open question due to the limitations and constraints of each ESS technology. The constraints include concerns over degradation, ESS response times, and overall costs. This paper proposes an advanced energy management strategy (EMS) within the CO 2 capture system to address the outlined problems. It presents a dynamic model for offshore-wind direct air capture of CO 2 system coupled with a battery-based energy storage system with the objective of maximizing CO 2 removal from air while fulfilling the overall system operational constraints and dynamics. More specifically, the proposed model investigates the flexibility of the CO 2 capture system with respect to wind power supply in different seasons. The DAC models proposed in this research consists of a three state automaton, namely: OFF, Adsoprtion, and Desorption which handle the three operational states of the proposed CO 2 capture system. In order to maximally utilize wind power availability, each operational state of the proposed model is considered as a separate load and dispatched separately. The proposed approach is then compared and analyzed by scheduling the system as a whole. Numerical results illustrate the feasibility of powering CO 2 capture system via variable wind power.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".