A Japanese Utility Renewable Energy Management
Bibliographic record
Abstract
HOKKAIDO a northern island of Japan has high potentials of Solar and Wind energies. However, HOKKAIDO Electric Power Company (HEPCO) declares that by increasing Renewable Energy (RE) power such as Photovoltaic and Wind generation (hereafter PV and Wind), they cannot interconnect to the grid because of interconnection limitation and having surplus power in the grid. In this paper, for RE surplus power management, we suggest two solutions. The first solution is to convert RE surplus power to another type of energy which divided into two different methods. First, convert RE surplus power to 100% heat. Second, convert RE surplus power to 50% heat, 40% hydrogen and 10% electric cars. For this purpose, we use the Advanced Energy System Analysis Computer tool called "EnergyPLAN" to estimate RE surplus power in HOKKAIDO future energy system. Then, we calculate and compare the conversation economic and environmental performances. The second solution is to transfer RE surplus power to connected multi-area networks. For this reason, we design load frequency control (LFC) in smart grid model of IEEE 30 bus test system in MATLAB/SIMULINK to give such ability to transfer power from one area to another. Finally, we compare both solutions economical and environment performances.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".