Impacts of Smart Grid Concept on Energy Industry
Bibliographic record
Abstract
Smart grid (SG) is a term that has recently become widely discussed along with the boom of renewable resources (RES-E) and with brand new approach to energy industry. Such phenomena are results from CO2 emissions mitigation and fight against global climate change, as it is discussed e.g. in [1]. Most of the RES-Es work on principles that do not enable the control of their generation. This fact impacts massively on the electricity grid. It is publicly known that the relatively massive development of non-manageable resources, along with the long-term increasing of energy demand, puts higher and higher requirements on the transmission system’s transport capacity. This problem becomes more visible e.g., with future plug-in electric vehicles (PEV) or local renewables (RES-E) expansion. Task for today’s engineers is to solve the sustainability of energy industry. The smart grid concept provides one possible way. Our paper therefore discusses main aspects of SG implementation, which are not often publicly discussed. Our paper describes SG concept that compiles with approach to the decentralized power industry, together with nodal prices occurrence. The local congestions in the grid as well as growing amount of consumption (connected with electric vehicles expansion) and local micro-generation can result in the price nodality. Therefore electricity price can differ according to local conditions from price in global grid. The mathematical description of conditions influences grid nodality follows. In the end of the manuscript, the new way of electricity pricing is proposed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".