BC Hydro approach to load modeling in state estimation
Bibliographic record
Abstract
Modeling of loads in energy management system (EMS) impacts significantly the robustness and quality of state estimator solution. Loads are traditionally used as pseudo measurements to fill in for unavailable real-time analog telemetry in order to provide required observability for state estimator to solve. The quality of load model also directly impacts the quality of state estimator and power flow solution. There are several important factors to consider when modeling for real-time applications in EMS. Those include (i) the granularity of load model i.e. whether loads are lumped for the entre station bus or each individual feeder in a substation is represented as a separate load, (ii) load voltage sensitivity which describes the variations of real and reactive loads with changes of bus voltage, (iii) load frequency sensitivity which emulates impacts of frequency deviations on loads and (iv) load accuracy model that assigns accuracy class to loads to be used by state estimator. In addition, the aspects of load model such as load allocation in real-time as well as modeling of loads in the external system also need to be addressed. The objective of this paper is to discuss the load model that BC Hydro has implemented in the network model in EMS to support the state estimator and other EMS advanced applications in real-time.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".