Energy Storage as a Non-Wires Alternative for Deferring Distribution Capacity Investments
Bibliographic record
Abstract
There is growing interest in Energy Storage Systems (ESS) and other distributed energy resources (DER) as non-wires alternatives to resolve distribution issues while also providing valuable services to the grid and to energy customers. One such use is to defer distribution capacity investments required due to load and/or DER growth. Selecting ESS power and energy ratings for capacity deferral requires time-series load profile data. This paper is a part of a longer-term objective of making it easy for utilities to consider energy storage systems within distribution planning. This paper proposes a practical planning-based approach to screen ESS (both power and energy ratings) to defer distribution capacity investments. More specifically, an approach is presented here that leverages linear power flow approximation to quickly perform many time-series ESS dispatch simulations enabling rapid ESS project screening for a large number of distribution feeders. This paper also illustrates intuitive ways to visualize optimal ESS power and energy ratings for different peak clipping objectives. The proposed ESS project screening approach is demonstrated on a real Hydro One distribution feeder.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".