A Novel Solution For California's Energy Crisis: Wind Power Transmission From Energy Rich North Dakota To California Through Hvdc Lines
Bibliographic record
Abstract
This paper first investigates feasibility of establishing a 7,000 MW power capacity wind farm, and the conversion of the total AC electrical power of 4,000 MW to the DC in a large converter station in Olga, North Dakota.Then it includes transmission of this bulk power to Northern California through a 1,700 miles, two bipolar +500 kV, 2,000A high voltage DC (HVDC) lines.The study assumes that there exists an average AC electrical power of 4,000 MW generated through two wind farms located in Olga, ND with 10,000 MW capacity.An existing wind capacity factor (CF) of 40%, which shows actual or predicted output as a % of installed capacity, is considered for this study.Two wind farms are considered to be established at Olga 3 and Olga 5 locations with average wind data available by North Dakota Department of Commerce -Division of Community Services.The commercially available North Dakota wind resource alone is estimated at over 1,000 TWh (billion kWh) per year.Dakotas wind energy potential is very stranded allover the land.Manitoba HVDC Research Center's PSCAD /EMTDC power system software is used for the system modeling and simulation studies of the proposed HVDC scheme.Overall, the researchers determined that it is feasible and economical to establish a total power capacity of 10,000 MW from two new wind farms including 5,000MW at Olga 3, and other 5,000MW at Olga 5 wind sites, both are located in the north east corner of North Dakota, and one large 4,000 MW AC to DC converter station in Olga 5, and to transfer this DC power to the Northern California by HVDC lines.I.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".