Comparative analyses of scheduling scenarios to facilitate optimal operation of interconnected micro energy grids
Bibliographic record
Abstract
Micro energy grids (MEGs) are small local grids that operate at low voltage or medium voltage levels and include loads, control systems, and distributed generators (DGs). Distribution systems are expected to operate according to interconnected MEGs. A MEG can be scheduled to continue feeding its own loads with minimum power exchange with the main grid. There are demanding procedures for their optimal scheduling for performance enhancement. This paper presents a comparative analyses of scheduling scenarios to facilitate optimal operation of interconnected MEGs. Comprehensive hour-by-hour energy system analyses are conducted of a complete system meeting electricity and heat demands, and including CHP (combined heat and power), renewable resources (PV and wind), and boilers. The scheduling approach determines the optimal outputs of DGs based on i) compromising between the operational cost and emission of the entire interconnected MEGs system and/or ii) minimum power exchange with the main grid for self-sufficient MEGs system. In conclusion, the most efficient and least-cost scheduling scenarios are identified through energy system and feasibility analyses.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".