Optimal allocation of STATCOM with energy storage to improve power system performance
Bibliographic record
Abstract
The production cost of electrical energy has created a need to find reliable, cheap and accessible sources for energy generation. As a result, the use of alternative sources such as wind and solar energy is in rapid growth world-wide. Nowadays these renewable energies are always combined with energy storage systems (ESSs) to save extra energy production and keep the production level below a specific limit. An ESS could also be combined with a STATCOM in a power system. This combination could add the benefits of an ESS to the advantages of a FACTS device such as reduced power flows on overloaded lines, resulting in increased system loadability, lower transmission line losses, improved power system stability and security, lower power production costs, and more secure bus voltage levels. This paper presents a genetic algorithm-based optimization process, for seeking optimal locations and parameters for a STATCOM combined with an ESS in power systems. The optimization process is designed to minimize transmission line losses and maximize the power transmitted by the network. The simulation results show the effectiveness of the proposed optimization process in determining optimal locations for the device in several test networks.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".