From Smart Grids to Super Smart Grids: A Roadmap for Strategic Demand Management for Next Generation SAARC and European Power Infrastructure
Bibliographic record
Abstract
Due to an increasing of demands of electricity in a world on regular basis, different continents will initiate a step towards transforming their smart grids infrastructure into super smart grids (SSGs), in which various countries in a continent will take a step towards an interconnection of their power system networks with one another to manage their futuristic demands conditions. The concept of SSGs system is predicated due to extensive use of modern technology, digital communication, machine learning and modern information techniques for the present power generating system to be more accurate and feature on balancing demand and supply. The SSGs uses renewable energy resources to support power system of multiple countries by reducing the greenhouse gases emissions. The main purpose of transforming smart grids into SSGs is to balance the demands and supply between multiple countries, if each country is not able to manage their own demands profiles. The environmental conditions, lack of energy management, intermittent nature of renewable energy resources and line losses are the major hurdles to provide regular supply. This research work focused about the hurdles in the form of technical challenges that will be arises in case of developing a futuristic SSGs for European and SAARC continents, and thus provide a valuable solution for it along with discussion about the future research directions. Moreover, although SSGs ideas have received positive reviews from many technical experts, but there development in future is still a challenging research issue due to lack of simulation based models of SSGs in the current literature. To deal with this issue, finally a fuzzy logic using hybrid cluster model of SSGs consisting of two clusters and a renewable wind energy system is successfully presented in this research paper. This model can be utilized in prospective for transforming any smart grids power infrastructure based on one country power network to futuristic SSGs power infrastructure based on multiple countries power networks for SAARC and European continents. The simulations of clusters and wind system are performed by the MATLAB. The suggested model of SSGs consisting of eighteen bus networks provides regular supply of energy between two countries interconnecting in the form of two clusters, whenever one or both countries lies in the region of SAARC and European continents faced some kind of fault.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".