A Zonal Capacity Market Model With Energy Storage for Transmission and Distribution
Bibliographic record
Abstract
Traditional generation and transmission expansion planning has served electric utilities well for several decades to procure the least costing set of assets to meet forecasted demand. Unfortunately, it does not consider a demand curve, in which case it procures generation and transmission assets that do not ensure maximum societal value. An Incremental Capacity Auction (ICA) enables a power system to competitively procure additional generation capacity that maximizes social welfare while satisfying numerous constraints. However, typical ICA designs, zonal or otherwise, do not consider new inter-zonal transmission lines and distributed energy resources (DERs) embedded in distribution systems, promoting suboptimal solutions. To address these shortcomings, this work presents a new comprehensive ICA model that considers intra-zonal and inter-zonal constraints with provision to add new inter-zonal transmission lines and distribution system embedded DERs, while accommodating non-monotonically increasing generator capacity price bids. The proposed zonal ICA model is applied to two systems: (1) a synthetic test system with two zones; and (2) Ontario, Canada’s provincial power system with six zones. The Ontario system study considers a realistic demand growth and demonstrates that the proposed zonal ICA model achieves 5.7% higher social welfare considering new inter-zonal transmission enhancements and DERs over existing single-zone methods.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".