Coordinated dispatch of renewable energy sources and HVAC load using stochastic programming
Bibliographic record
Abstract
This paper investigates the potential of using aggregated heating, ventilation, and air-conditioning (HVAC) loads to smooth out the power fluctuation of a wind farm and/or a solar farm to make these stochastic resources more dispatchable. Specially, we consider a Virtual Power Plant (VPP) which consists of several wind/solar power units, a number of buildings with their HVAC systems, and a battery storage facility. A portion of the power generation from renewable energy sources (RESs) is used to operate HVAC systems and the rest (if any) is sold to the main grid. The design goal is to determine an optimal hourly scheduled power dispatch that the VPP must submit to electricity market to maximize its benefit. The short-term fluctuation of renewable energy generation (i.e., over intrahour intervals) is mitigated by smartly adjusting the flexible HVAC load, which enables the VPP to provide a firmed hourly dispatch. The underlying optimization problem is formulated as a two-stage stochastic program where system uncertain parameters are modeled using Monte-Carlo simulation. Building thermal dynamics model and users' climate comfort criteria are considered in the proposed optimization framework. Numerical results is presented to illustrate the effectiveness of the proposed model.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".