Robust Energy Management of a Microgrid with Uncertain Price, Renewable Generation, and Load using Taguchi’s Orthogonal Array Method
Bibliographic record
Abstract
Energy management system in a microgrid with uncertainties in load, renewable generation and market price is critical for stable operation of the microgrid. Scenario-based robust energy management exploiting upper and lower bounds is used to deal with the uncertainties. Taguchi’s orthogonal array method is used to reduce the large number of scenarios, considering all the possible combinations of max and min values of loads and renewable generations. In this study, uncertainty of market price is handled by robust optimization method, and worst case scenario with the maximum total cost is defined as the output result. Furthermore, demand response program is also considered for the flexible loads, which help the microgrid to operate robustly with a lower cost, in the presence of the uncertainties. In this study, two cases with typical microgrids are considered to evaluate the effectiveness of the proposed method, and GAMS tool is used for implementation of simulations. Additionally, Monte Carlo simulation is applied for verifying the effectiveness of the method.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".