Robust Optimal Dispatch of Power Systems with Wind Farm
Bibliographic record
Abstract
With the rapid development of new energy power generation, large-scale wind power generation has been integrated into power grids. However, the fluctuation and discontinuity of wind power have introduced challenges to the safe and reliable operation of power systems. Therefore, constructing a reasonable dispatching method considering the uncertainty of wind power output has become an important topic. This study aims to establish a reasonable power system dispatching optimization method considering the uncertainty of wind power output. Hence, an ellipsoidal robust set of wind power output was initially constructed in accordance with the predicted value and predicted error of wind power. Second, a power system optimization dispatch model of automatic generation control (AGC) was established on the basis of the robust set. This model aimed to minimize the cost of power generation and maximize the use of wind power according to the following constraint conditions: power system power balance, upper and lower limit of wind and thermal power unit outputs, climbing power, and spinning reserve. Finally, the internal point method was employed to solve the example. Results show that on the premise of safe operation, the total operating cost of the robust optimization dispatch method is decreased by 8.64% compared with that of the traditional dispatch method, and the economic efficiency is improved. The robust optimal dispatch considers the uncertainty of wind power output, and load shedding scene seldom occurs, thereby enhancing the operational reliability. This study can improve the reliability and economics of power system operation and provide a basis for optimization dispatch of power systems.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".