Energy modeling for policy support (EMoPS)


This topic provides some orientation for the concept of energy modeling for policy support (EMoPS).

The idea of building numerical models of energy systems to support public‑interest analysis stretches back to the oil crisis of the 1970s. Those early efforts built on developments in operations research, mainframe computing, and advances in numerical optimization. Arguably, the idea is firmly back on the table now as countries, regions, and the world collectively starts to confront the intertwined challenges of rapid decarbonization, sector coupling, decentral architectures, smart grids and systems, and energy sufficiency.

A key theme this time around is transparency. And the key enablers of transparency are open source models, genuinely open data, and open access content. While the key disrupters for transparency include the democratization of variously software development, data collection, and knowledge management more generally. Indeed, transparency and openness go hand in hand.

The U4RIA project attempts to advance these current tends by providing both a context for and venues in which to nurture a community to support transparent and open EMoPS. The energy sector is the initial focus but neighboring sectors like water management and land‑use provide natural extensions — with both being closely allied to the multiple trends highlighted earlier. Moreover, the energy sector will doubtless need to become carbon‑negative to offset hard‑to‑abate emissions in agriculture, air travel (with its cloudiness impacts), and elsewhere.

The U4RIA project offers advice on the open licensing of data, software, and content.

Energy system modeling

It is not possible to build a “digital twin” of an entire energy system and have it be remotely feasible, both computationally and in terms of data requirements. It is therefore necessary to break down the problem by temporal and spatial scope and resolution. Which then introduces the issue of how best to “divide and combine” these various “sub‑analyses”. Navigating this space while simultaneously looking for unseen opportunities is essentially the art of numerical modeling. Data availability, quality, and legal status represent further issues to be traversed. Useful reviews of energy system modeling include Chang et al (2021) and Subramanian et al (2018).

Interaction with public policy

The relationship between energy system analysis and public policy development was recently investigated by Süsser et al (2021). The authors identify substantial influence in both directions, point out that models can be instrumentalized to justify already cast policies, and that openness can provide an antidote to this and related effects (page 1):

Our study implies that greater transparency, including open-source code and open data, and transdisciplinary elements in modelling could increase model legitimacy and impact in policymaking.


The challenges of validation and robustness have yet to be tackled comprehensively by energy system modelers. These indeed being key drivers behind the U4RIA project. One promising approach is the use of cross‑model comparisons using the same scenario specifications. Another is current work on data integrity, underpinned by the development of more consistent semantics, metadata, and collection.

Notwithstanding, one needs be clear about what represents an input assumption, what represents an analytical limitation, what represents an output, and what represents a legitimate but ultimately qualified conclusion. These various facets of modeling are ofttimes easily misinterpreted, particularly given insufficient experience, attention, or transparency.


The degree of openness is a key criteria for EMoPS analysis (Morrison 2018). The following diagram shows how the overarching application software, termed a “framework”, can vary in terms of economic classification. The U4RIA project strongly prefers the use of open source frameworks.

The data used to create specific scenarios and models should likewise be genuinely open. More on data.


There is no question that energy system modeling is complicated. Understanding the system is complicated, assembling the skills required is complicated, and accessing and curating the necessary data is complicated.

On the other hand, the tools are increasingly available from code repositories, the programming and data management skills more readily acquired through online support, and the data requirements en‑masse are increasingly being tackled as a community endeavor. Moreover science funding bodies and development agencies are increasingly recognizing the benefits of genuinely open science and genuinely transparent analysis.

Useful links


Chang, Miguel, Jakob Zink Thellufsen, Behnam Zakeri, Bryn Pickering, Stefan Pfenninger, Henrik Lund, and Poul Alberg Østergaard (15 May 2021). “Trends in tools and approaches for modelling the energy transition”. Applied Energy. 290: 116731. ISSN 0306-2619. doi:10.1016/j.apenergy.2021.116731. Creative Commons CC‑BY‑NC‑ND‑4.0 license.

European Environment Agency (EEA), European Aviation Safety Agency (EASA), and European Organisation for the Safety of Air Navigation (EUROCONTROL) (2019). European Aviation Environmental Report 2019 — TO‑01‑18‑673‑EN‑N. Europe: EEA, EASA, EUROCONTROL. ISBN 978‑92‑9210‑214‑2. doi:10.2822/309946. High resolution version. Open access.

Morrison, Robbie (22 December 2020). Energy system models explained: Dr Berit Erlach explains energy system modeling in everyday terms. Berlin, Germany: Löschwasser Productions. Video 00:13:17. Filmed 9 June 2019 in Berlin, Germany. Reference LP‑001‑01. Creative Commons CC‑BY‑4.0 license.

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. Creative Commons CC‑BY‑4.0 license.

Subramanian, Avinash Shankar Rammohan, Truls Gundersen, and Thomas Alan Adams II (2018). “Modeling and simulation of energy systems: a review”. Processes. 6: 238. doi:10.3390/pr6120238. Creative Commons CC‑BY‑4.0 license.

Süsser, Diana, Andrzej Ceglarz, Hannes Gaschnig, Vassilis Stavrakas, Alexandros Flamos, George Giannakidis, and Johan Lilliestam (1 May 2021). “Model-based policymaking or policy-based modelling? How energy models and energy policy interact”. Energy Research and Social Science. 75: 101984. ISSN 2214-6296. doi:10.1016/j.erss.2021.101984. Creative Commons CC‑BY‑4.0 license.

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