I considered starting a new forum topic but decided to tack this on to the end of the current thread. In some senses, standardized reporting leads on relatively naturally from data structures. Perhaps it would be better if this discussion was indeed forked? But let’s see what others think first?
I recently talked to people active in Scientists for Future who see a need for standardized reporting from technically comparable models (I’ll start with the more usual term “model” here and later switch to “scenario + method” which I think is a better characterization). Standardized reporting would then allow, for instance, civil society organizations (CSO) advocating for rapid decarbonization to more easily develop and defend their policy positions by citing suitable scientific studies. These studies should at least be consistent in the sense of compatible definitions, sufficiently similar numerical paradigms, and standardized reporting — but need not necessarily cover the same set of scenarios. Moreover, representative metrics should be provided in an easily interpreted format to facilitate interpretation across these various studies and scenarios.
The theme of this posting is therefore to add the interests of downstream users to this discussion. While noting that such users — including NGO advocates — may well not be analysts or possess much specific domain knowledge.
With respect to the energy sector, key study attributes should be readily available on first inspection, including but not limited to:
- year of carbon neutrality — typically 2050 but earlier target years are increasingly being proposed
- scope — whether local, national, supra‑national, or global
- sector coverage — are the heat and mobility sectors included, for instance
- role of international trade in fuels — biofuels, natural gas, green (electrolysis) and non‑green hydrogen, and other e‑fuels
- treatment of cross‑boarder electricity
- technical definition of final demand (see this thread on the mailing list)
- role of energy efficiency and assumptions on future developments and uptake
- assumptions regarding future demand, behavioral change, and energy sufficiency
- degree of spatial and topological disaggregation and temporal resolution
- representation of power flow
- consideration or otherwise of novel technologies like thermal storage, CCUS, BECCS, and direct air capture
- rate constraints, if any, on uptake, the treatment of fleet vintage, and the role of early retirement and capital stranding more generally
The underpinning methodology needs communicating as well because the modeling paradigm adopted can have a profound influence on the nature of the results and hence conclusions. And thereby limit the comparison exercises under discussion. Methodological questions include but are not limited to:
- use of perfect foresight
- strict versus near optimality (typically under intertemporal optimization)
- full horizon modeling versus time‑slicing
- evolution from present versus greenfield design (with or without pre‑existing transmission corridors)
- recursive dynamic (stepping though time) methods, including hybrid agent‑based
- system dynamics
- projected accounting methods
The final aspect is standardized aggregate metrics. Common definitions are necessary in order that each study performs the same arithmetic. Such metrics would include:
- final energy
- externally traded energy
- installed capacity by technology type
- generation or production by fuel type
- breakdown of above by sector using consistent definitions for coverage
- carbon dioxide equivalent and cost formation information for all the above
In addition, a set of standardized derived metrics should also be considered. These are often produced by dividing two aggregate metrics to produce intensive quantities. Examples include CDM‑style financial and carbon additionalities for specific projects. Or system energy efficiency metrics calculated by dividing final and primary aggregate energy fluxes for selected years.
The three broad strands above — scenario attributes, underpinning methodology, and aggregate (and perhaps also derived) metrics — leads to the question of a standardized worksheet. Whether such a worksheet could be usefully realized remains an open question, at least in my mind.
A related idea would be a recommendation to provide Sankey diagrams as described on wikipedia. Discussion elsewhere in this community included forming a recommended set of colors for for energy commodities and technologies (although I am not aware of the outcome).
The uses of standardized reporting should not be seen as a substitute for detailed comparative analysis in which researchers drill down to identify and understand the differences between several studies and/or sets of scenarios. One example of this exercise is the ESYS, BDI, dena (2019) report (listed below) which government policy analysts apparently found particularly useful.
In summary, the two coupled issues — technically comparable scenarios + methods and standardized reporting — introduced in the first paragraph, might well appear to fall outside the boundary of the original do‑a‑thon concept. But I would argue that the idea of semantic consistency is implied in the concept of data interchange and that standardized reporting is likewise implied in the idea of “aggregate” metrics. I would also guess that the integrated assessment modeling community is further along this path than the energy system modeling community and that the IAM experiences could prove valuable in this regard.
The organizers may need to decide, I would suggest, whether these ideas fall within the scope of the proposed do‑a‑thon or would be better traversed elsewhere. Equally, if the do‑a‑thon discussion is limited to a particular modeling paradigm (such as graph‑dynamical systems representations), then that paradigm should be explicitly stated at the outset.
It is also worth reinforcing — for those outside our community who are reading this posting — that the process of converging on agreed common practices is greatly facilitated when the projects involved are genuinely open. Once your project is open‑licensed and easily downloadable, you have every incentive to press for beneficial intra‑community practices and every reason to cooperate on the development of enabling protocols.
ESYS, BDI, dena (20 February 2019). Expertise bündeln, Politik gestalten — Energiewende jetzt!: Essenz der drei Grundsatzstudien zur Machbarkeit der Energiewende bis 2050 in Deutschland [Pooling expertise, shaping policy — Energiewende now: essence of the three fundamental studies on the feasibility of energy system transformation in Germany by 2050] (in German). Presented at Auditorium Friedrichstraße, Friedrichstraße 180, 10117 Berlin, Germany.