Optimal Energy Management of a SAGD Microgrid Participating in a Volatile Electricity Market
Bibliographic record
Abstract
This study proposes the formulation of an energy management system (EMS) for the optimal scheduling and economic dispatch of a microgrid system incorporating distributed sources of energy generation as well as energy storage. A microgrid located on an Alberta SAGD facility, comprising a waste heat-to-electricity Rankine cycle, gas turbine and energy storage system is considered. An alternate scenario in the Pennsylvania-New Jersey-Maryland market is also considered. The EMS maximizes the net present value (NPV) by utilizing a dynamic real-time optimization (D-RTO) approach. The optimization is discretized into a moving horizon that is updated and solved at each time step. The updates are provided by an EMS forecaster using Bayesian DLM and timeseries ARIMA forecast models. The DLM has the ability to incorporate several exogenous inputs with resultant improvements: accuracies of up to 85% are achieved. The EMS provides optimal economic dispatch for generation and storage units within the energy mix. The microgrid energy management problem is solved for a total simulation time of 1 year, and the system optimal policy is assessed on the basis of its NPV at the end of this period.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".