Energy routing in the smart grid for Delay-Tolerant Loads and Mobile Energy Buffers
Bibliographic record
Abstract
Energy routing has not been feasible in the traditional power grid due to real-time nature of the electrical services. Electricity is generated and used almost in real time where the balance between the two is maintained by regulation services. Additionally, small number of fixed storage units are utilized to store some portion of the generated energy. In the future electricity grids, Mobile Energy Buffers (MEB) together with local energy buffering capabilities, will be the enabler of energy routing. In this paper, we propose an energy routing framework for low and medium voltage electricity distribution systems that house prioritized MEBs, local buffers and Delay-Tolerant electrical Loads (DTL). We model the distribution system as a token-based system where energy transfer between MEBs and local storage units rely on the availability of tokens that are generated by DTLs. Coordination of token advertisement, storage interest and token access is maintained by machine-to-machine communications. The underlying communication technology can be PowerLine Communications (PLC) or a medium-range wireless communication technology. We provide a mathematical analysis of the token-network with DTLs and prioritized MEBs. We show that coordination and prioritization allow lower blocking rates for high priority MEBs. Our analysis provides valuable insights for utility planning decisions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".