Multi‐agent transactive energy management system considering high levels of renewable energy source and electric vehicles
Bibliographic record
Abstract
The future smart grids (SGs) consist of considerable amount of renewable energy sources (RESs), electrical vehicles (EVs), and energy storage systems (ESSs). The uncertainties associated with EVs and uncontrollable nature of RESs have magnified voltage stability challenges and the importance of an effective energy management system (EMS) in SGs as a practical solution. This study presents a multi‐agent transactive energy management system (TEMS) to control demand and supply in the presence of high levels of RESs and EVs, and maximises profit of each participant in addition to satisfying voltage regulation constraints. For this purpose, a real‐time pricing is considered based on Cournot oligopoly competition model for demand and merit order effect for production to compensate RESs’ fluctuations in real time by an indirect control method. Simulations are conducted in the modified IEEE 37‐bus test system with 1141 customers, 670 EVs, two solar plants, four wind turbines, and one ESS. The results show that the proposed multi‐agent TEMS can indirectly control EVs, elastic loads, and ESSs to balance the RESs oscillation, minimise customers cost, and regulate voltage in a real‐time manner.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".