Increasing the reliability of energy system scenarios with integrated modelling: a review
Bibliographic record
Abstract
Abstract Systems models are an important tool for policy and energy planning decisions. These models generally fall into one of three modelling paradigms: energy economy, capacity expansion or power sector planning. Recent work seeks to combine these paradigms into an integrated framework to leverage the benefits of different model types. There is also interest and research in representing more system interactions to expand the modelling nexus. However, this increases model complexity and risks creating more black box models that are not well understood or trusted by users or policymakers. To understand the trade-offs and best practices of using combined models, we review current modelling practices, including an overview of the different modelling paradigms in the literature, how combined modelling has been applied to date and how the nexus has been represented in different modelling applications. Building on the literature review, we held a series of expert elicitation workshops to gain insight from energy modelling domain experts who use combined models. Finally, we encapsulate these findings and best practices into a modelling evaluation framework. We find that while there is interest and research being done in these areas, there are no set standards for how to build these types of models, resulting in a wide range of practices. Increasing model complexity to develop fully hard-linked coupled models that are also trustworthy and transparent generally requires more time and resources than is worthwhile. Instead, the focus should be on avoiding black box models by having a clear modelling purpose and developing best practices that allow for clarity and transparency. Expanding the nexus to include attributes such as biodiversity and cultural security presents a challenge and representing them as a cost is not congruent to equitable policy. These aspects could be better incorporated into analysis using stakeholder debate and citizens’ assemblies.
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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 | 0.009 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".