MISO’s Regional Long Range Transmission Planning: A Proactive and Holistic Planning Approach
Bibliographic record
Abstract
The energy ecosystem is evolving, resulting in a paradigm shift in the Market, Planning and Operations of the electric power grid. Retirement of conventional generation, increased penetration of renewable resources, and growing electrification will continue with policy support for decarbonization of the grid. The power industry is reacting and adapting to the evolving resource mix. As part of its response to these drivers, Midcontinent Independent System Operator (MISO) is leading major efforts, redefining its Planning, Operations and Market Systems. Long Range Transmission Planning (LRTP), one of the workstreams under MISO’s Reliability Imperative, recognizes the need for proactive and holistic planning and assesses reliability risks 10-20 years into the future. The intensity, frequency and severity of extreme weather events further highlight need for Long Range Transmission planning, to facilitate regional delivery of energy to serve generation deficient areas. LRTP is informed by the Renewable Integration Impact Assessment (RIIA) and MISO’s Futures (forward-looking planning scenarios). RIIA, a technically rigorous and systematic analysis, evaluated increasing levels of wind and solar resources penetration on the MISO footprint. The “Futures” address uncertainty over a long-term horizon. The first tranche of the LRTP effort identified a ${\$}$10.3 billion portfolio comprised of eighteen projects with over 2,000 miles of new transmission lines for the MISO Midwest subregion. The business case for LRTP identified a Benefit-to-Cost ratio of 2.6 overall.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".