Improvement of Microgrids Operation Considering Demand Response Using Imperialist Competitive Algorithm
Bibliographic record
Abstract
In recent years, with the restructuring and privatization of power networks, special attention has been paid to the role of customers in network operation. Therefore, demand response programs have played an important role in power network studies and many researchers have been working in this area. Microgrids are a collection of loads and generating units that can supply their own power independently. In this article, the problem of demand response of loads in a microgrid has been studied. For this purpose, the customers in a microgrid announce their proposed prices for participating in the demand response program to the network operator for different hours of the day. This demand response program will be executed when the network operation constraints are violated. Therefore, there is no need to run it in the hours when the network is operating properly. The network operator will run the demand response program with the aim of removing the constraints imposed on the network. For this purpose, the imperialist competitive algorithm (lCA) has been used to find the best possible solution. After running the proposed method, it will be determined how much power each load should reduce in each hour. The proposed method has been implemented on a sample network and its results have been evaluated and analyzed for different scenarios. The problem modeling has been done using MATLAB software. The results proved the effectiveness of the proposed model.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".