Value‐stack aggregator optimal planning considering disparate DERs technologies
Bibliographic record
Abstract
Abstract Federal energy regulatory commission (FERC) Order #2222 prescribes that distributed energy resources (DERs) with 100 kW or more capacity in aggregate should be allowed to participate in organized electricity markets. Most aggregation is via a combination of disparate DER technologies such as solar, wind, storage, electric vehicles, and smart load units. Another stumbling block to enabling participation of DERs in organized electricity markets is the energy limitation. However, there is a lack of aggregator models in the literature that gainfully allow aggregation of disparate DER technologies that are energy limited. To address this shortcoming, we proposed a disparate DER aggregator (DDA) planning model here, that overcomes energy limitation of DERs. The DDA planning model considers multiple revenue streams of (1) capacity credits; (2) energy revenues; and (3) ancillary services revenues. The proposed DDA planning model enables disparate DER technologies to collate and provide a firm power capacity and participate in the market capacity auction and receive capacity credits. This comprehensive DDA planning model considers the dynamic/temporary aggregations with other facilities through peer‐to‐peer (P2P) trade, and maximization of the net present value (NPV) revenues over the planning horizon. The developed model is tested on sample and practical large‐scale case studies. Additional sensitivity analyses are performed, demonstrating the favourable performance and the business potential of the developed DDA planning model.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".