Optimization of Generation Cost, Environmental Impact, and Reliability of a Microgrid Using Non-dominated Sorting Genetic Algorithm-II
Bibliographic record
Abstract
Over the past years, energy sectors have accomplished considerable progress in the transition from conventional fossil-based energy to low-carbon energy production, and microgrids are playing important roles in this sustainable energy transition. One of the key challenges for microgrids is to deliver power with the least possible cost and that too with such an approach that the environmental impact is the lowest and the overall system reliability is high enough. For this reason, generation cost, emission entities, and system reliability need to be efficiently optimized. Towards this goal, an online multi-objective technique has been employed to optimize cost, emission and system reliability taking these three factors in pairs at a time. The optimization model is designed using the non-dominated sorting genetic algorithm-II (NSGA-II), and the algorithm has been employed for several double objective scenarios considering reliability as an objective and later as a constraint. To evaluate the performance of the proposed approach, the simulation results are compared with the relative parameters from a different model that uses the strength pareto evolutionary algorithm (SPEA). The results show that the proposed technique satisfies the multi-objective optimization goals and provides good trade-offs between the conflicting objective functions while finding the optimal dispatch.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".