Real‐time energy management in a microgrid with renewable generation, energy storages, flexible loads and combined heat and power units using Lyapunov optimisation
Bibliographic record
Abstract
In this study, the real‐time energy management system (RT‐EMS) of a microgrid (MG) is proposed to deal with different uncertainties due to the errors in the prediction of renewable generation, load and market price. In the day‐ahead EMS, the error in prediction of data; thus, the uncertainties in the scheduling are dealt with using different scheduling methods. Nonetheless, utilising the online RT measurements is an advanced solution to eliminate the uncertainties because there would be no prediction error in employing the RT information. In this study, a RT‐EMS of a MG is designed using the Lyapunov optimisation method. In RT‐EMS, satisfying the time‐coupled constraints such as the battery energy limit and provision of load quality of service is a demanding challenge. This problem is addressed in Lyapunov optimisation by defining distinct virtual queues for satisfaction of time‐coupled constraints. Moreover, the variable V algorithm is employed to provide a better compromise between stabilising the virtual queues and the total operation cost. A test MG system consisting of combined heat and power units, renewable energy sources, energy storage systems and flexible loads is used for evaluation. The underlying distribution network and power distribution loss are further considered satisfying the voltage limits.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".