World experience in legal support for the use of consumers-regulators in power systems
Bibliographic record
Abstract
The positive experience of using various technologies in power systems as consumers-regulators for energy management is shown, which helps to increase the reliability and stability of power systems, and also reduces the need for new generating capacities, including expensive peak ones. The world experience of legal support for the use of consumers-regulators in power systems at the international, national, and regional levels is considered, namely: directives and regulations of the 4th European EU Energy Package; laws of the USA about independence and national security, energy efficiency improvements, research and development for grid modernization, and the results of the programs developed based on them; laws and regulations of the U.S.A. states and Canada to promote energy savings and reduce greenhouse gas emissions through increased use of renewable energy sources (RES) and other new technologies; the effectiveness of the system of measures DSM (Demand Side Management). In these documents, an important role is assigned to both the use of technologies such as RES, energy storage, Smart Grid, etc., and the possibility to combine consumers of different categories and the rules for their participation in the electricity market in a non-discriminatory way. An increase step by step in support for the use of consumers-regulators in power systems in the legislative and other national documents of Ukraine, in particular, in the new law on the development of energy storage installations, the National Action Plan for Renewable Energy for the period up to 2030, the National Transport Strategy of Ukraine for the period up to 2030, etc., which meet the main provisions of European directives, and it is also shown the need to take into account the wide introduction of new technologies such as Smart Grid, building consumption regulation, the use of electric vehicle charging stations, etc. Keywords: consumers-regulators, legal support, power system.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".