Read on Twitter
1/ Our new Commentary, out today in @Joule_CP, focuses on the benefits of collaborative macro-energy modeling efforts: https://www.sciencedirect.com/science/article/pii/S2542435120305109
A thread.
A thread.
2/ We use the term "macro-energy systems" introduced by @pjlevi et al. to describe models with broad energetic, spatial, and temporal scales, which are used to examine technology and policy pathways across the whole energy system: https://www.sciencedirect.com/science/article/pii/S2542435119303617
3/ These models provide a systematic way to examine decarbonization pathways, evaluate technology choices, test the effects and consequences of proposed policies, and explore decisions under future uncertainty.
4/ There are big challenges when modeling deep decarbonization, including rapidly changing costs, sparse data for novel technologies, and operational issues associated with high renewables penetration.
5/ Not to mention the challenge of accounting for the human element - technology uptake, behavioral change, public acceptance of technology, as well as social justice and equity issues.
6/ Addressing these challenges in model-based analysis suggests the need for teams with diverse expertise and disciplinary backgrounds.
7/ Many of these macro-energy modeling efforts are conducted by government agencies or intergovernmental organizations, where institutional and governance structures can limit participation by outsiders.
8/ We argue that distributed teams offer the flexibility to bring a diversity of perspectives to bear on the myriad challenges we face in model-based analysis. And we have all the tools at our disposal to make this happen.
9/ First, open source models, tools, and datasets serve as an important foundation for distributed modeling efforts because they enable transparency, accessibility, and replicability among team members.
10/ Second, modern software development tools like @github allow for distributed control of model code and data.
11/ Third, there are a number of ways for distributed team members to communicate - traditional email, cloud-based collaboration platforms (e.g., Slack), and videoconferencing software. We're all used to Zoom by now, right?
12/ To be fair, there already are several collaborative European modeling efforts. But they involve large teams with generous EU funding. We think it is possible to form distributed teams from the bottom-up with more limited resources.
13/ We're taking this approach in our Sloan-funded project to develop an Open Energy Outlook for the United States. Though the project is still in the early stages, we're already starting to see the benefits.
14/ One modest example: working with electric sector experts, we were able to increase the model's temporal resolution by moving to a rolling horizon approach, which reduced the computational burden of a single solve.
15/ Moving forward, we're thinking about ways to democratize the team building process, which can ensure a greater diversity of perspectives and make the effort adaptable to new challenges.
16/ We always welcome feedback and suggestions on our project. We have a website ( https://openenergyoutlook.org/ ), GitHub repo ( https://github.com/TemoaProject/oeo), and public mailing list ( https://oeo.groups.io/g/main ).
17/ I want to thank my co-authors (all 37 of them!) for their contributions to this piece. The Commentary itself benefited tremendously from their diverse opinions.
18/ Finally, thanks to the @SloanFoundation and @MichelsonEvan for making this work possible and to @Joule_CP for making the article open access.
