Stochastic Demand Response under Random Renewable Power Generation in Smart Grid
Bibliographic record
Abstract
Rooftop photovoltaic (PV) generation combined with battery energy storage provides a promising solution for solar energy integration in smart grid. Specifically, the home battery energy storage systems can improve the efficiency and reliability of PV integration while reducing the greenhouse gas emissions. In this paper, we investigate the randomness of home PV generation and the residential random load demand, which may affect the efficiency and reliability of the power grid. A bilevel stochastic programming problem is formulated to provide a pricing strategy to customers for the optimal demand response in smart grid. In particular, the operators model represents the cost minimization of the power system operation, while the customers' model represents the cost minimization of their household energy demand. In the operators model, power loss calculated based on power flow analysis is used as the system loss, while the stochastic model of the household load demand is used instead of the expected value to characterize the human random behaviour. The performance of the proposed stochastic demand response scheme is evaluated through extensive simulations. Simulation results indicate that this novel scheme can help both power system operators and electrical customers to better decide on their operating schedule and energy usage, respectively.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".