Characterizing the Effect of Conservation Voltage Reduction on the Hosting Capacity of Inverter-Based Distributed Energy Resources
Bibliographic record
Abstract
The increased adoption of inverter-based distributed energy resources in the form of the mass deployment of renewable generation systems has been a focal goal of many jurisdictions worldwide. Actions taken by local governments have included adapted regulations, financial subsidies, and a variety of grants. This has spurred the proliferation of solar generation among residential customers in virtually all provinces of the United States and Canada. Needless to say, these small generators are interfaced with DC–AC inverters, which have evolved tremendously since the formation of working groups targeting the impact of inverter-based generation on the grid. Among the first rules and standards are California’s ISO Rule 21 and the UL1741 SA, which were published in September 2017. IEEE followed suit in 2018 with the revision of IEEE 1547, inspiring virtually all jurisdictions to either adopt these standards or adapt them as their own variants. Among many features, these standards mandate inverters to be fitted with autonomous performance functions, including the constant power factor, voltage-reactive power (Volt-VAR), voltage-active power (Volt-Watt), and grid support functions, as well as provisions for compatibility with control centers. These functions have been demonstrated to increase the nameplate hosting capacity. At the same time, grid modernization strategies have become more prevalent, one of which is the use of conservation voltage reduction. This grid modernization initiative has a great impact on the hosting capacity. Conversely, the increased penetration of distributed energy resources has a negative effect on the conservation voltage reduction, but surprisingly to only a limited extent. The characterization of these impacts is addressed in this paper, with a focus on a case with very high DER penetration and with very long daily sunlight hours.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".