A Risk-based Two-stage Stochastic Optimal Power Flow Considering the Impact of Multiple Operational Uncertainties
Bibliographic record
Abstract
his paper shows an application of a scenario-based method for risk constrained stochastic optimal power flow (RC-SOPF) problem in electricity utilities. A two-stage stochastic programming framework is developed for dealing with various uncertainties. Customers' demand, wind power generation, and electricity price are considered as the uncertain parameters in the proposed RC-SOPF problem. The aim is to minimize the energy procurement costs, while preserving an acceptable risk level. The energy procurement cost consists of generators active power generation costs, cost of energy procurement from external network (e.g. pool market or upstream network) and operation & maintenance cost of wind farms. To control the negative impacts of the uncertainties, variance and conditional value at risk (CVAR) are used as risk measures. The proposed model is implemented on the 39-bus New England test system. The obtained results show that CVAR is suitable index for management of the risk associated with uncertain parameters in comparison with variance.
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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".