Minimizing Demand Transmission Service Charges in Optimal Sizing and Scheduling Of Campus Microgrids
Bibliographic record
Abstract
Microgrids have paved the way through the exploitation of distributed generation resources and eliminating the requisite of high-duty transmission infrastructure. The study presented in this thesis develops a two-stage optimization problem for the optimal sizing and operation of campus/institutional microgrids considering delivery charges. At the first stage, a mixed integer linear programming (MILP) is implemented to determine the optimal configuration of microgrid which consists of solar Photo-Voltaics (PVs), batteries and microturbines (MTs). The incorporation of delivery charges leads to a significant reduction in transmission charges without sacrificing the power exchange limit. Resulted savings on electricity bill grants the capital investment in microgrid components. In the second stage, a mixed integer non-linear programming (MINLP) on a rolling horizon basis is formulated for the optimal operation of the microgrid under sizing results obtained in the first stage. Moreover, an efficient Peak Load (PL) hour forecast framework is established to minimize the coincident PL charges. Both volatile and flat electricity price scenarios are studied to investigate the impact of electricity prices on microgrid optimal sizing and operation. In order to test the proposed methodology, University of Calgary main campus is selected as the case study and historical hourly load data are 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".