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The modelling of energy systems is a vital part of understanding the effectiveness and flaws in our energy distribution and consumption systems. Energy system modelling is the process of creating computer models of energy systems so they may be analyzed. These often incorporate scenario analysis to test different assumptions of technical and economic conditions. Energy system modelling is a crucial component in advising future policy decisions, and so it follows that giving energy system modellers the best possible tools to improve their modelling is an important component in determining our collective future. Currently the most prominent energy modelling systems take into account the following dynamics:
Given the stakes involved and the significance, there has been a movement within the scientific community to request for data and insights related to modelling systems to be open source, in order to help them tackle some of the trickiest problems related to decarbonization together. Having a collaborative-analysis based modelling approach is not currently the case, particularly in the U.S, although Europe has been making moves toward a paradigm shift in the process of conducting model-based analysis from single-institution modelling teams to distributed, collaborative teams. The potential that this method of conducting research within this field are yet to be realized and could unlock global gains in terms of decarbonization efforts and energy efficiency.
As it stands in the U.S, energy modelling is not as easy as it could be. Most research in this field is being carried out by single-institution teams, government agencies or commercial modelling efforts. Each of these come with their own caveats as to why they are not as efficient as they could be.
A number of the challenges the modelling community faces are from the restrictive nature of the research systems. Commercial modelling efforts usually rely on proprietary models and data which is not available to the broader scientific community or interested stakeholders. Therefore, it is difficult for these findings to be replicated or be verified by the broader scientific community, or for the scientific community to utilize this research and build on the findings. Government agencies conducting research will often be guided by biases toward the usage of certain technologies or be influenced by policy preferences. As a result, a lot of variables or alternate approaches are not considered as much as they ought to be. Not to mention the considerable access to data which may not be granted to other members within the field. Finally, single institution will often suffer from a limited breadth of expertise. Energy system modelling involves solving problems which draw from a number of different disciplines. While a data analyst expert may be able to find patterns within a source to find results, a policy expert may be required to translate those results into actionable and applicable policies.
On top of this, many supply-and demand side technologies at different stages of development will work toward decarbonizing energy systems. A lot of these technologies are relatively new (such as direct air capture and hydrogen-based steel production), have fluctuating costs (such as solar photovoltaics, lithium-ion batteries, and electrolysers), or have location-specific variables (heat pumps and wind farms). These aspects make the projection of technology cost and performance characteristics over multiple decades related to deep decarbonization problematic. This is not made easier by the fact that many decision makers across the energy system, each with their own objectives and preferences, make it difficult to model technology uptake, behavioural change, and public acceptance. Making more of the efforts toward system modelling open source would address some of these problems in a better way.
Having greater access to decades worth of weather data for example would be a hugely valuable tool to properly represent high penetrations of renewables with energy storage, and other options for flexibility, since modeled spatial variation in resource availability and temporal variation in supply and demand can significantly impact results.
By opening up to a more collaborative community, many of the biases held by commercial or government agency researchers could be mitigated with the masses tending to focus more on what the data reveals than what preconceptions or opinions would dictate. Having this data available would allow discourse on what appropriate action may look like or how best to implement it. Flexible arrangement of teams can allow for experts in a variety of fields to input their knowledge and advise on niches within a problem which may not be considered by a more limited team.
Open-source efforts in the macro-energy space have proliferated over the last decade. Resulting models, tools, and datasets serve as a solid foundation for distributed modelling efforts as they encourage transparency, accessibility, and replicability among the broader community. If insights and revelations can be shared more effectively within the community, then ideas and solutions can be generated at a quicker pace.
Nations across the globe have committed to tackling climate change by designing and implementing policy solutions that enable deep decarbonization of energy systems. Given the current reliance on fossil fuels, fundamental and coordinated changes in the way societies generate and use energy. Policy makers are tasked with the massive challenge of crafting effective energy and climate policy in the face of a highly uncertain future. Energy infrastructure changes will always involve large, up-front investments in long-lived assets. Given the magnitude of the challenge and what is at stake, everything should be done to ensure that these decisions are as well advised as possible. Shifting to an open-source energy system modelling approach will be a step in the right direction.
