Multiobjective Integrated Planning and Scheduling of the Energy Infrastructure of the Oil Sands Industry Incorporating Intermittent Renewable Energy
Bibliographic record
Abstract
The energy infrastructure for oil sands operations can be classified as a decentralized energy system, in which energy requirements (i.e., power, heat and hydrogen) are generated near the end-users, and can operate with interactions with the local Alberta grid, in which it feeds surplus power generated to it. In this study, a mathematical optimization model is developed for the integrated planning and scheduling of the energy infrastructure of the oil sands industry. The contributions of various energy sources including conventional, renewables, and nuclear are investigated. Power-to-gas for energy storage is incorporated to manage surplus power generated from intermittent renewable energy sources, particularly wind. The wind-electrolysis system included incorporates two hydrogen recovery pathways, which are power-to-gas and power-to-gas-to-power using natural gas generators. The problem is modeled as a multiobjective and multiperiod mixed integer linear programming model that minimizes the system cost (energy production and storage), grid cost, and total greenhouse gas emissions. In addition to including the grid cost and emissions in the objective function, grid-interaction is incorporated in the optimization model through the unit commitment operations of the existing power generation units of the grid. The proposed model is designed to evaluate the optimal operation and sizing of the energy producers and the energy storage system as well as the interactions between them. The epsilon constraint method is used to solve the multiobjective aspect of the proposed model. To illustrate its applicability, the model is applied to a case study based on the oil sands industry in Alberta for the integrated planning and scheduling of its energy infrastructure for the year 2017.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".