A coordinated CVR-EVDC approach for electric vehicle charging demand management
Bibliographic record
Abstract
This paper presents a novel coordinated Conservation Voltage Reduction (CVR) and Electric Vehicle Demand Control (EVDC) method for energy-efficient power system operation . The goal of coordinated CVR-EVDC is to minimise the total energy consumed by the distribution network through coordination of the scheduling of EV charging, the OLTC transformer tap position, and the switching of the capacitors on the network. Using publicly available statistics of journey length and travel time of car drivers in the UK, a stochastic model is developed for EV battery State-of-Charge (SOC) and its availability for home charging to generate EV charging demand profiles for different levels of EV penetration. These are then used in conjunction with Particle Swarm Optimisation (PSO) and a relaxation equality constraint to explore the potential for optimally coordinating CVR operation and the scheduling of EV charging on LV distribution networks. Results for five different LV network scenarios show that a coordinated CVR-EVDC approach enables more significant energy savings than with independent operation of CVR and EVDC – 4 % greater with dumb charging and 3 % greater with valley-filling smart charging. This study shows that distribution network operators can manage the networks by coordinating CVR and EVDC to manage consumer charging requirements for better energy savings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".