An Optimal Control Approach for Enhancing Transients Stability and Resilience in Super Smart Grids
Bibliographic record
Abstract
Super smart grids (SSGs) are a wide area transmission network that mainly uses renewable energy resources (RERs), contributing to the reduction of greenhouse gas (GHGs) emissions and supporting the power infrastructure of multiple countries. The SSGs comprise two-way communication between the loads and sources of different countries, and these loads can be mostly served with numerous types of RERs tied with the grids. The RERs will play a pivotal role in the development of future grids and the generation of electricity. However, the main challenge to tackle in these RERs is that they are intermittent in nature. Due to intermittency in these RERs, transient stability issues have become one of the critical research challenges in SSGs. These stability issues are escalated and become more difficult to handle if a network is vulnerable to an arising of different kinds of faults. To address these problems, multiple approaches to enhance transient stability already exist in the current literature. After reviewing the literature, flexible alternating current transmission systems (FACTS) devices proved more promising in improving transient stability. Among FACTSdevices, UPFC is a versatile FACTS device, which provides complete stability to power system networks in the form of series and shunt compensations. Considering this scenario, a hypothetical network for SSGs is designed in this research work based on the interconnection between two countries, i.e., Denmark and Norway, to address the transient stability issues in SSGs. The complete probabilistic model of the system is also designed to enhance the stability of the system. The results clearly showed that the insertion of UPFC is an effective technique to enhance the transient stability and resilience of the power system networks as compared to other purposed techniques in the literature. The main contribution of this paper is that extensive simulation studies employing accurate RERs models are used to analyze and investigate various problems arising due to the integration of many clusters of RERs in SSGs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".