OSeMOSYS step - a python package for modelling energy systems under limited foresight and with decision trees
Bibliographic record
Abstract
Abstract.A common challenge in energy system optimisation models is the representationof investment decision patterns. Such capacity expansion models often assume perfectforesight. Thereby they assume certainty for the entire modelling horizon. Thisproduces investment patterns that are cost optimal on the long-term but not necessarilyfor single assets on the short-term. Therefore, models with limited foresight can providevaluable insights when trying to anticipate investment patterns in the power sector.This article presents a Python package that expands the capabilities of the OpenSource energy Modelling System (OSeMOSYS) to facilitate modelling with limitedforesight. Furthermore, it applies the package to investigate the impact of foresighthorizon length on the accumulated CO2 emissions in a decarbonisation scenario andinvestigates the possibilities to model disruptive events.To expand the functionality of OSeMOSYS, a fully open Python package isdeveloped that allows the user to run a provided model with limited foresight. Themodel time horizon is divided into multiple independently cost-optimised time stepswith the option to vary parameters between time steps to represent decisions ordisruptive events. The package allows to run decision trees by providing multipledata options for model parameters. The decision tree then incorporates all possiblecombinations of the provided options.The article shows for the case of a decarbonisation scenario that a shortening of theforesight horizon leads to an increase in CO2 emissions accumulated over the modellinghorizon. This illustrates the importance of analysing and considering foresight horizonlengths at different levels of decision making in policy design.The analysis of model results when applying the decision tree feature illustratethat OSeMOSYS step facilitates to model sudden and disruptive events while keepinglimited foresight. This allows for analysis of the robustness of the energy system todeal with sudden changes in the boundary conditions.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".