Application of PSO to optimize the operation of electric water heaters for reserve provision
Bibliographic record
Abstract
Power demand on average is increasing with time on a global scale. This becomes a challenge to meet because of negative impacts like global warming due to increased emission of greenhouse gases and increased operating costs of the fossil fuel-based generation. Of late, a lot of research has been conducted on Demand Side Management (DSM) programs, which try to adjust quantities from the load-side in order to curtail or shift demand. Using DSM, controllable loads can also become providers of ancillary services, in which case the consumers become “prosumers”. This paper introduces an algorithm based on Particle Swarm Optimization (PSO) to optimize the operation of water heaters (WHs) to provide synchronous reserve. The developed algorithm is applied to two test systems including WHs and the results show substantial savings in terms of operating costs from the independent system operator (ISO) point of view, without affecting the consumers' satisfaction. The developed approach will not only ensure an inexpensive mean of providing synchronous reserve but will also be environmentally friendly.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".