An EROI-based analysis of renewable energy farms with storage
Bibliographic record
Abstract
Large renewable energy (RE) farms, such as wind or solar farms are usually sited in remote areas, far from the transmission grid which typically interconnects population centers. Thus, they need to be connected on expensive access lines (distribution feeders) with limited capacity. The excess of RE generation over line capacity is wasted; this is called curtailment. We study curtailment using the metric of energy return on investment (EROI), defined as the ratio of useful energy extracted from each unit of energy invested in creating the renewable energy generation system. Curtailment reduces EROI. It may appear that we can extract more energy from an RE farm and increase EROI by adding storage to the system, where this storage is charged during generation peaks and discharged during off-peak times. However, manufacturing the storage requires an energy investment, and, after a certain number of cycles of usage, the storage becomes non-functional. Thus, adding storage may actually decrease the EROI. In this work, we study the EROI for RE farms when used with several types of storage technologies. Unlike prior work that makes numerous simplifying assumptions, our work accounts for storage size and storage imperfections and uses actual traces of renewable power generation. We find that lithium-ion batteries increase the EROI of both wind and solar farms, unlike lead-acid batteries which generally decrease their EROI. We also show that increasing access line capacity to achieve a target EROI is much more expensive for solar farms than for wind farms.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".