Comparison of environmental assessment methodology in hybrid energy system simulation software
Bibliographic record
Abstract
The advent of renewable energy systems has led to an increase in decentralised energy systems. Consequently, the last 10 years have seen development of specialised software such as HOMER, iHOGA, EnergyPro, RETScreen and TRNSYS to analyse these systems. This study compares these software in detail especially in terms of the environmental assessment. It is concluded that these software do not adequately include environmental analysis since only 1 out of 5 software considers more than one life cycle stage, neglecting other upstream/downstream emissions. Furthermore, the emphasis is on emissions such as NOx and SO2 that are usually associated with fossil fuel utilization. As the energy systems are becoming increasingly complex, especially with storage technologies such as hydrogen and batteries, emissions ‘shift’ away from the operating stage. Moreover, it becomes essential to look further than global warming potential and take into account other impacts such as depletion of critical materials, acidification, eco-toxicity, etc. Hence, it becomes essential to take into account entire life cycle stages and provide comprehensive environmental impacts along with the already available techno-economic capabilities to the designers and decision-makers. Finally, this study provides recommendations on the methodology to include environmental analysis in the investigated software.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| 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".