Transmission Network Planning in Super Smart Grids: A Survey
Bibliographic record
Abstract
For a utility company to reduceCO2emissions along with managing load demands, it must strive for a 100 percent renewable electrical power generation. Europe takes initiatives to achieve this goal by developing a super smart grid (SSG) based on renewable energy resources (RERs) by 2050. The SSG is based on two exclusive alternatives: wide area and decentralized power generation using large number of RERs. Before developing such SSG, there is a need to address the critical issues associated with RERs, i.e., load flow balancing and transients stability. Considering a reliability issue involved with RERs and the random deviations between demand response and generation response patterns, load flow balancing and transient stability become challenging research issues in SSGs. These technical issues are also considered to be more challenging, if an unexpected outage in the form of an occurrence of three phase (L-L-L) faults (TPF) arises in SSGs, due to power quality disturbances. To address this problem, load flow balancing probabilistic modeling is performed in this research paper in order to formulate the complexity of randomness between generation and demand response patterns through transmission network planning (TNP) in the form of a super smart node (SSN) transmission network infrastructure. Moreover, a further optimization in SSN transmission network has been done with the addition of a cooperative control strategies in terms of an integrating vehicle to grid (V2G) technology in SSN transmission network in order to achieve further enhancement in load flow balancing and transient stability in SSGs. Furthermore, as SSGs power infrastructure is based on different clusters, therefore in order to accommodate various clusters for load flow balancing, a continuous spinning reserve (CSRs) probabilistic modeling has also been performed in this paper in terms of its integration in SSN transmission network. Considering above probabilistic analysis, future contingencies are easily predictable, before any kind of disruptive changes arises in a SSGs due an occurrence of a TPF. Moreover, from simulation results as performed in this paper, we can easily verified that our proposed probabilistic algorithm of load flow balancing and transients stability outperforms existing literature work and can also achieved near optimal performance, even for a broad range of variations in load and also in case of an arising of significant power quality disturbances in SSGs due to an occurrence of TPF.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".