Security Constrained Stochastic Power System Scheduling Algorithms - With Wind Electric Generators
Bibliographic record
Abstract
Power systems worldwide are embracing diverse supply mixes that incorporate a significant portion from renewables such as wind and solar energy. Wind energy is characterised by reliable equipment, but with an output that is uncertain and intermittent. In addition to equipment unreliability (system N-1 criterion), output uncertainties of wind electric generators (WEGs) introduce risk into daily power system schedules. This risk from the uncertainty of output from WEGs can be quantified as expected energy not served (EENS). Furthermore, the introduction of new forms of generation changes the methods of operating transmission systems, further necessitating the use of transmission security constraints in power systems optimization algorithms. This dissertation explores new approaches to stochastically model the real power output of WEGs and to efficiently tackle AC transmission system security constraints for power system optimization algorithms such as optimal power flow (OPF) and day-ahead unit commitment (UC). Usually, normal probabilistic distribution is used to model uncertainty in short-term wind power output forecast and compute EENS. In this dissertation, a new triangular approximate distribution (TAD) model is proposed which is a linear approximation of normal probabilistic distribution to model short-term wind power output forecast and compute EENS. This TAD model is used to formulate a practical risk-constrained fast OPF for transmission systems to simultaneously minimize: 1) risk due to uncertainties in power output from WEGs, and 2) the total operating cost. The integration of new energy resources causes transmission systems to operate in new, challenging, and often unforeseen operating states. Thus, it is imperative that UC algorithms incorporate AC transmission system security constraints and stochastically model output of WEGs to ensure reliable operation of transmission systems. As a first step, a successive mixed integer linear programming (MILP) method is proposed for AC transmission system security constrained unit commitment (SCUC) challenge. Fuzzy sets theory is used to model infeasible constraints in this MILP formulation. As a next step, the TAD model of WEGs is integrated into the MILP formulation of SCUC to create a fast security and risk constrained probabilistic UC algorithm. The two UC algorithms are tested on large systems.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.004 | 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".