An Online Energy Management System for a Grid-Connected Hybrid Energy Source
Bibliographic record
Abstract
An online energy management system (EMS) for a grid-connected hybrid energy source is proposed in this paper. The hybrid source combines renewable energy resources (wind and photovoltaic), battery storage, variable speed diesel generator, and load management system. The proposed EMS consists of two-level optimization algorithm: 1) the rolling optimization and 2) the feedback intrasample correction. The rolling optimization part is established to schedule operation based on the forecast data using the model-predictive control approach. The rolling dispatch scheduling is then adjusted based on an intrasample feedback correction that compensates for the prediction error of the forecast data. The optimization problem was formulated as mixed-integer linear programming framework with two objectives: 1) to minimize the total operating cost and 2) to minimize the pollutant gas emissions. The battery daily number of cycles and the minimum state of charge are considered as decision variables that are optimally determined by the EMS to minimize the total system operating cost while considering all the practical constraints of the different energy sources. Different case studies with different market profiles demonstrate the effectiveness of the proposed approach, and the results have showed a significant reduction in the total system cost.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".