Battery Dispatch Optimization for Electric Vehicle Aggregators: A Decentralized Mixed-Integer Least-Squares Approach with Disjunctive Cuts
Bibliographic record
Abstract
To address battery dispatch optimization (BDO) for an electric vehicle (EV) aggregator, this paper develops a decentralized mixed-integer least-squares (DMILS) approach formulated with a master-problem and multiple sub-problems based on decomposition via the alternating direction method of multipliers (ADMM) algorithm. The aggregator’s master-problem aims to coordinate the Lagrange multiplier vector in all sub-problems, whilst each EV’s sub-problem concentrates on its individual BDO solution using the updated Lagrange multipliers. To accelerate solution convergence, disjunctive cuts for battery operation are incorporated into the proposed DMILS approach. Given parallel computation of sub-problems and a well-designed warm-start strategy, numerical case studies demonstrate that the proposed DMILS approach converges to the optimal solution in less than 14.00% of the time taken by the benchmark centralized counterpart for the case of N ⩾ 500 EVs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".