Electric Vehicle Flexibility Harnessing Through Local Energy Community Operation Optimization: Maximizing Local Energy Utilization
Bibliographic record
Abstract
With an uprising penetration, local energy communities (LECs) face the local challenge of reaching energy self-sufficiency (catering for high generation-demand time mismatch) and the system-wide challenge of securely operating within the distribution network. In this sense, a novel strategy for LEC operation is proposed to overcome these challenges, aiming to maximize local consumption by controlling the charging/discharging of EVs and exploiting distributed generation resources at full, guaranteeing a distribution system secure operation. For this, the local operation of LECs (encompassing conventional and flexible demand members and prosumer members) is represented by a mixed-integer linear programming model, also considering the LEC interaction with the distribution network, and distribution system technical limits. To validate the proposed strategy, the impact of the economic interaction among LECs as well as EV charging/discharging control on LECs’ self-consumption, was analyzed in a case study under different operation conditions. The numerical results demonstrate that, when compared to the baseline scenario, EV charging/discharging coordination enabled the local supply of 80.5% of the LECs' daily energy, increasing to 88.31% when the interaction between LECs is enabled.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".