A Japanese Utility Renewable Energy Management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it