Collaborative operation and supply/demand management of virtual power plant system integrating complex network theory
Bibliographic record
Abstract
Virtual power plant can effectively solve the impact brought by distributed power supply access to the grid system and optimize the operational efficiency of the grid.In this paper, a virtual power plant operation system based on complex network is designed.Under the premise of taking into account the security and economy, the complex network is used to realize the automatic access of distributed energy sources and adjustable load access, put forward the topology of autonomous learning virtual power plant network, and construct the corresponding load calculation mathematical model.In addition, a day-ahead robust bidding model for multi-energy virtual power plants to participate in the peaking market is established, taking into account demand response, and the model is solved using the column and constraint generation algorithm.After the simulation experiments, a virtual power plant project containing PV, MT and wind power is analyzed as an example, and it is found that the price of electricity before 9:30 and after 16:30 is low and the purchase price is lower than the cost of VPP power generation, so in this paper, the system purchases electricity from the grid, which reduces the generation of power by generating units and reduces the cost of power generation by the system.Between 11 and 16 o'clock, the market price of electricity is very high, higher than the cost of VPP power generation, so the VPP sells electricity to the grid to gain revenue.In this paper, the operation of virtual power plant under system operation is in line with the market law, optimizing the cooperative operation and supply and demand management of virtual power plant system.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".