Uncertainty and simulation‐based cost analyses for energy storage systems used in green buildings
Bibliographic record
Abstract
Summary Energy storage systems (ESS) provide buildings with the ability to store electricity generated from renewable energy sources (eg, solar photovoltaics), increasing building operational flexibility and reliability. Previous studies on ESS in buildings often make assumptions about key input parameters (eg, installation factor, roundtrip efficiency, etc.) being fixed, overlooking potential performance risks due to uncertainty or variations in these parameters. The goal of this paper is to quantify the impact of uncertainty in technical and financial parameters on the economic performance of Li‐ion batteries (LIB), proton‐exchange membranes reversible fuel cells (PEM RFC), and reversible solid oxide cells (RSOC). The analysis is conducted on three medium‐sized office buildings located in three different climate zones of the United States: 2B (Phoenix, AZ), 3B‐Coast (Los Angeles, CA), and 4C (Seattle, WA). Two metrics are used to evaluate the economic viability of the selected technologies, namely the levelized cost of electricity (LCOE) and the levelized cost of energy storage (LCOS). While the results of the LCOE and LCOS differed in value between those cities, the cost breakdown for LCOS in all locations shows that capital cost is the biggest cost contributor, followed by electricity cost. A Monte‐Carlo simulation was also conducted to assess the impact of uncertainty in input parameters on the LCOS model. The simulation results allowed us to generate probable ranges and probability tables for the LCOS.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".