On the Performance of Distributed and Cloud-Based Demand Response in Smart Grid
Why this work is in the frame
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Bibliographic record
Abstract
By locally solving an optimization problem and broadcasting an update message over the underlying communication infrastructure, demand response program based on the distributed optimization model encourage all users to participate in the program. However, some challenging issues present themselves, such as the existence of an ideal communication network, especially, when utilizing wireless communication, and the effects of communication channel properties, like the bit error rate, on the overall performance of the demand response program. To address the issues, this paper first defines a cloud-based demand response (CDR) model, which is implemented as a two-tier cloud computing platform. Then a communication model is proposed to evaluate the communication performance of both the CDR and distributed demand response models. This paper shows that when users are finely clustered, the channel bit error rate is high and the user datagram protocol (UDP) is leveraged to broadcast the update messages, making the optimal solution unachievable. Contradictory to UDP, the transmission control protocol will be caught up with a higher bandwidth and increase the delay in the convergence time. Finally, this paper presents a cost-effectiveness analysis which confirms that achieving higher demand response performance incurs a higher communication cost.
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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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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 it