Any thoughts? Please comment below or message me offline. Preferably by 22 September.
10 September 2023
A proposal to develop a decision‑support application mockup using input from relevant developer and potential user communities. The purpose being to streamline the process of selecting an open‑source energy system modeling framework,
The German government is sponsoring a Prototype Fund: We fund Public Interest Tech scheme to assist the development of open‑source software that additionally contributes some public good. The project described here would seek six months of half‑time funding under that support program. For background:
The application deadline is 30 September 2023 and the project itself would commence in February 2024.
The application process is two pass. Those originally selected will be asked to provide further details related to project structure, deliverables, and governance in a second step.
Streamlining the process of selecting open‑source energy system modeling frameworks
briefly describe your project
This project aims to provide a semi‑structured approach to the selection of energy system modeling frameworks. Briefly, these frameworks can be used to determine carbon‑neutral futures for the energy sector (more later). The current frameworks on offer can vary markedly in terms of their technical capabilities, supported technologies, areas of application, regional usage, and so forth. Also on legal context, package management, usability, documentation, development community, types of support, official backing, and so on.
Developer community input will be sought and will be key to the success of this project. The framework projects themselves are the parties best placed to describe their characteristics and strengths.
Potential users will be sought and interviewed.
The project will leverage on existing resources within the wider energy modeling community (described later), including the Open Energy Platform Model Factsheets database (for background see, Reder et al 2019).
This field of energy system analysis is not widely understood, so some additional comments are probably warranted.
An energy system framework is software that, when populated with detailed system data, can be used to simulate present and future energy systems and investigate a range of hypothetical development trajectories (known as scenarios) against some reference case. These days, net‑zero by 2045 or 2050 is normally binding. Issues of interest can include the future roles of nuclear power, renewables generation, green hydrogen, demand‑side flexibility, network expansion, sufficiency options, decentral versus global architectures, the management of intermittency, conventional and novel policy measures, new market designs, and so forth.
It is hard to say if open‑source frameworks make up the majority of analysis these days, but they are certainly on the ascendancy. There is very clear trend towards open‑sourcing, with some key legacy models recently being released under OSI‑approved open licenses and some established independent open‑source frameworks now being taken up by significant public agencies. Improved policy transparency and stakeholder engagement are among the drivers for this process.
Many development teams are also fully committed to open data, open science, and genuine replicability. Wikipedia provides examples of such frameworks.
what social problem do you want to solve with your project?
Open‑source energy systems models (meaning populated frameworks) are contributing to the identification of rapid decarbonization pathways within the energy sector and also beyond — because many frameworks also account for wider contexts, such as water resources, land availability, the built environment, e‑mobility, and even planning bottlenecks, technological learning, and climate justice.
For those seeking a modeling framework suited to their particular systems questions, there is a huge amount of information available — but locating, assembling, processing, and filtering this mountain of usually disparate information represents a major and ofttimes insurmountable barrier. Conversely, the downsides of making poor software choices in this regard can be significant.
Well‑designed decision support tooling can therefore contribute.
The potential userbase spans the global north and the global south. Indeed the project will include a list of studies that have been applied in various countries and regions — as well as details of the accompanying frameworks and study details. More here on that regional context.
how do you want to implement your project technically?
At this juncture, the details of technical implementation are intentionally sketchy. Questions covering data structures, database design, platform choice, backend functionality, and user interactions need traversing, but these should be relatively easy to resolve.
Of greater importance and difficulty are matters of user need and appropriate information provision. The project in the longer term will also need to be responsive and evolving. Indeed, the development of this kind of service must necessarily rely in part on learning‑by‑doing.
Project maintenance going forward is also another important consideration. In this case, ongoing community interest will be essential — as this will be the primary avenue for ensuring ongoing uptake, development, and currency.
what similar solutions already exist and what will your project do differently or better?
To date, the main venue for processing these kinds of queries are sporadic discussions on channels provided by the various projects or by the broader modeling community — often via chatrooms, mailing lists, or discussion servers. Some of that traffic is public and may remain accessible, but much of it is semi‑private and soon lost. Moreover, those discussion need not be very comprehensive.
There is some academic work directed towards model comparisons (models in the sense of populated frameworks) (see Gils et al 2022). This work is limited to the participating projects. Nor are the results designed to be accessible to non‑specialists.
There is a considerable computer science literature on decision support related to software selection and choice processes more generally.
Down track, AI‑based analysis may have a role. But at this juncture, orthodox database methodologies should prove sufficient.
The earlier funded Tauritron project originates from the same general community.
who is the target group and how will your tool reach them?
The target audience is the clearly growing number of organizations and, in some cases, individuals taking up this kind of systematic analysis. The list includes undergraduate students, early‑stage researchers, engineering consultancies, companies and corporations, public bodies tasked with policy analysis and development, and NGOs including the new round of climate change think‑tanks.
The assisted uptake of open‑source frameworks in the global south is a key motivation for this proposal. Not only are software license fees absent, but most projects bundle helpful and engaged communities — in essence, a spontaneous form of soft technology transfer.
Reach is not really an issue in the context of this project. The open energy modeling community has sufficient visibility and established channels for this kind of tooling to be readily found and evaluated by potential users.
have you already worked on the idea? if so, briefly describe the current status and explain the innovation
About ten years back, the applicant began and later wrote about 90% of the Wikipedia page on open energy system models, that also contains circa 250 citations. That page covers about half the framework projects that it doubtless could (see here).
But none of this kind of information provision would class as offering decision support in any real sense.
How many hours in total do you (or the team) plan to work on the implementation during the 6-month funding period?
The resourcing sought would cover the proposer for 20 hours/week × 26 weeks × appropriate employee rate.
No specialist hardware or software is required. All selected application software would be open‑source. All outputs will be released under an MIT AND CC0‑1.0 dual license unless upstream licensing constraints dictate otherwise. A CC‑BY‑4.0 license, at the strictest, would be necessary for all inbound content.
It should be understood that this proposal is practical and not academic.
briefly outline the most important milestones that you (or the team) want to achieve during the funding period.
The following outputs are proposed:
- a requirements document
- semi‑structured interviews with potential users and accompanying analysis
- software design proposals
- a mockup interactive HTML interface
Robbie Morrison began working with high‑resolution energy systems modeling frameworks in 1995 and with open‑source variants since 2003 (Morrison 2018). He has been active in the Open Energy Modelling Initiative (openmod) community since 2016, about two years after its formation in Berlin.
Robbie is the current lead admin on the openmod forum with about 1200 registered users. I think it fair to say that Robbie is well known within that wider community. Robbie is not affiliated with any individual modeling framework project and no associated conflicts of interest should arise.
The applicant has programmed in a number of languages, including Fortran, C++, R, Python, Lisp, Bash, and SQL
Provided for background information.
Gils, Hans Christian, Jochen Linßen, Dominik Möst, and Christoph Weber (1 October 2022). “Improvement of model-based energy systems analysis through systematic model experiments — Editorial”. Renewable and Sustainable Energy Reviews. 167: 112804. ISSN 1364-0321. doi:10.1016/j.rser.2022.112804.
Knops, Mathias (August 2023). Software selection process and criteria. Software Alliances.
Morrison, Robbie (April 2018). “Energy system modeling: public transparency, scientific reproducibility, and open development”. Energy Strategy Reviews. 20: 49–63. ISSN 2211-467X. doi:10.1016/j.esr.2017.12.010. Open access.
Reder, Klara, Carsten Pape, Mirjam Stappel, Hannah Förster, Lukas Emele, Christian Winger, Ludwig Hülk, Christian Hofmann, Editha Kötter, Martin Glauer, and Till Mossakowski (2019). Scenario data on the Open Energy Platform (SzenarienDB on the OEP): a web-platform to improve transparency and reproducibility of energy system analyses — Poster. Kassel, Germany: Fraunhofer Institute for Energy Economics and Energy System Technology (IEE).