workshop-summary — Macro-Energy Systems

Macro-Energy Systems Workshop Summary

September 17 and 18, 2020

Research directions have moved from technical feasibility and energy transition pathways to understanding other non-economic and non-technical bottlenecks, with a focus on distributional impacts
Decision support (for policy makers, investors and non academic users) provided by models is crucial, and maintaining model transparency, open access and replicability are critical for achieving this goal
Systems thinking is challenging and we need to be deliberative about analyses boundaries and how models can be expanded or combined meaningfully with other models (“the model of everything”); a ‘theory of models’ will help
As we incorporate additional objectives (employment effects, energy poverty, pollution), it becomes important to think about how tradeoffs can be formalized; groups affected the most by these tradeoffs need to have a seat at the modeling table
We also need to expand our research focus to include non-electricity and non-developed world centered analyses, and start to understand how to include the demand side in a better way

The anonymized panelist poll identified the most important critiques of MES as (1) economic impacts, (2) policy realism, (3) tail risks, and (4) equity
- Economic Impacts: direct and physical effects of climate change are often considered, but indirect impacts are often underrepresented and speculative, because they depend more on response strategy
- Policy Realism: there is a disconnect between policy simplicity used in research and the piecemeal/ heterogeneous policies implemented in reality. The multitude of objectives (emissions, energy security, poverty, equity) of real-world policy makers may also be excluded from more traditional cost-based analyses
- Tail Risks: these can have significant impacts of modelling outcomes, yet computational, data-sourcing, or other restrictions often limit their presence in MES modeling
- Equity: this is critical to include in models, and strides have been made since their inclusion in the SDG, though including access to energy remains a gap in many large models currently

Exercises and real world examples (i.e., learning-by-doing) can help students gain insights
Teachers and students will benefit from a shared repository, where teaching materials, exercises and games, models and codes can be shared; cross-institutional approach can help facilitation collaboration in educating researchers
More training on open-source collaborative scientific software development and coding can be important in educating macro-energy systems researchers
Data-driven ways of teaching dynamics and complexity of macro-energy systems can help provide insights in parallel with traditional optimization approach
The modeling and analysis tasks require multiple steps and skills from both classroom and workplace
Better instruction on coding practice, problem formulation and fundamental interdisciplinary skills/knowledge, among others, are three main areas for education to improve upon
Political science/political economics and non-economic behaviors are the two most neglected topics in educating energy modelers
Optimization, economics and uncertainty analysis are among the most important skills that people are glad to be trained on

Discussed in breakout rooms
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