Addressing Trust Issues in Vehicle to Building Enabled Demand Response Using Blockchains
Bibliographic record
Abstract
Power system operators incentivize large consumers of electricity to reduce their demand during peak periods, where consumers can promise a fixed demand reduction in the form of a contract known as demand response (DR). Commercial building owners, particularly those who have electric vehicle (EV) charging infrastructure, can utilize vehicle to building (V2B) technology to participate in DR, while also aggregating other on-site distributed energy resources (DERs) such as solar and batteries. However, the provision of V2B-enabled DR creates trust issues between EV owners, building owners, and power system operators in ensuring that the contracted DR capacity is delivered, and that operational preferences, such as the minimum state of charge of EVs participating, is respected. Thus, this paper proposes a blockchain-based system to create a shared ledger that stores contract details, including contract bids and EV state of charge, and executes automated smart contracts to record, monitor, and dispatch the DERs to ensure that the contracted capacity is realized without violating constraints. Through real-world scalability testing and DR event demonstration, the proposed system shows that it can support up to 10,000 DERs and deliver 20 kW of contracted capacity for a 4 hour DR event. vehicle to building, demand response
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".