Demand Response for Electric Vehicles Using Options
Bibliographic record
Abstract
Electricity spot prices in emerging electricity markets exhibit high volatilities and occasional distinctive price spikes due to the non-storable nature of electricity. Furthermore, the inherent variability and uncertainty of renewable energy generation require balancing supply and demand all the time for electric power systems. In the context of reliable electricity service, demand response (DR) programs allow end-users to adapt their electricity usage to changes in the price of electricity over time. DR programs include price-based and incentive-based DR programs. Real-time pricing (RTP) is a price-based DR program, which charges customers electricity rates based on the utility’s real-time production costs. Electric vehicles (EVs) can utilize their flexibility in load curtailment to provide potential demand response opportunities. However, the current DR programs available do not fully consider EVs’ potential for renewable energy integration through demand-side management. In this research, we propose introducing financial options as additional incentives for EV users under RTP, reinforcing their encouragement to align the renewable energy supply peak better. Also, the proposed DR program can guarantee peak load reduction reducing utility’s risk exposure in demand peak. A realistic study on the Ontario electricity market shows that the proposed DR program significantly saves EV users’ charging costs. Also, the research results show that the proposed DR program allows electricity utilities to dynamically optimize electric grid operations without increasing the price volatility and defer the need to construct new generation capacity.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".