1-s2.0-

Energy Strategy Reviews 26 (2019) 100396
Contents lists available at ScienceDirect
Energy Strategy Reviews
journal homepage: www.elsevier.com/locate/esr
Are open access models able to assess today's energy scenarios?
a,*
Stella Oberle , Rainer Elsland
a
b
T
b
Fraunhofer Institute for Systems and Innovation Research ISI, Breslauer Straße 48, 76139 Karlsruhe, Germany
Wilhelm Büchner University of Applied Sciences, Ostendstraße 3, 64319 Pfungstadt, Germany
A R T I C LE I N FO
A B S T R A C T
Keywords:
Open access modelling
Energy systems analysis
Energy scenarios
The current transition towards a low-carbon energy system requires an increasingly complex energy system
framework. This is accompanied by the demand for high result reproducibility in order to provide transparency
to decision-makers in terms of assumptions and methodological issues. Given this background, Open Access
Models (OAMs) are increasingly entering the market that already have a high degree of diversity. This study
analyses and compares the methodological framework of different OAMs to assess long-term energy scenarios. In
a first step, selected OAMs are typified and characterised based on predefined criteria. In general, the analysis
reveals that OAMs with a high level of accessibility appear to have a rather low level of complexity and often
focus on the analysis of a single target year. In a second step, we underline our findings of the model overview by
applying a well-established OAM (DESSTinEE - Demand for Energy Services, Supply and Transmission in
EuropE) to a current energy scenario for Germany. Overall, we conclude that current OAMs can already be
applied to a large variety of research questions. However, comparing OAMs to conventional models applied in
the field of energy system analysis reveals that there is still a significant performance gap in terms of the degree
of methodological sophistication.
1. Introduction
To address the threat of climate change, the international community is aiming to keep the rise in global average temperature below 2 °C
compared to pre–industrialisation levels [1]. In Germany, the government's target is to decrease greenhouse gas emissions by 80–95% by
2050 compared to 1990 levels [2]. To achieve this target, a disruptive
transformation of the energy sector towards more sustainability based
on low-carbon technologies is necessary. The management of this
transformation is a subject of much debate, so that there is a great need
for insights based on quantitative analysis that is able to capture uncertainties [3,4].
Energy scenarios quantified by energy system models are the main
methodology applied to analyse alternative future developments of
entire energy systems. These models map essential techno-economic
parameters like the installed capacity of power plants or the required
expansion of electricity grids [5,6]. Since the first oil crisis in 1973,
energy system modelling combined with an energy scenario framework
has frequently been used to answer research questions for policy makers and institutions in the field of energy economics (Fig. 1) [7]. As a
consequence of the oil crises, the International Energy Agency (IEA)
launched its Energy Technology System Analysis Program (ETSAP) in
*
1976, with the aim to develop the first energy system model in a detailed techno-economic manner, applying it mainly to analyse oil use
options and more efficient use of final energies [8]. In subsequent years,
models such as PRIMES (Price-Induced Market Equilibrium System) and
LEAP (Long-range Energy Alternatives Planning system) were developed in European countries, which are now frequently applied for high
level consultancy also outside Europe. Due to the high demand for
energy system modelling expertise, a large variety of models have been
developed in academic institutions over time, which are usually applied
in a commercial context to evaluate energy scenarios for industrial and
political decision-makers [6,9].
This development has been accompanied by issues of limited
transparency and reproducibility, which have often been criticized in
recent years [7]. To address this criticism, an Open Access movement
was launched [10,11], which led to the establishment of the Open
Energy Modelling Initiative in 2014 aiming to accelerate the development and use of Open Access Models (OAMs) to support high-level
decision-making in industry and politics [12]. The term OAM is not to
be confused with the term Open Source Models (OSM), whose source
code is always publicly available as well. Despite the dynamic development of OAMs in recent years, this research field is still rather young
when compared to the models currently applied in a commercial
Corresponding author.
E-mail address: Stella.Oberle@isi.fraunhofer.de (S. Oberle).
https://doi.org/10.1016/j.esr.2019.100396
Received 22 December 2018; Received in revised form 16 April 2019; Accepted 25 July 2019
Available online 02 August 2019
2211-467X/ © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Energy Strategy Reviews 26 (2019) 100396
S. Oberle and R. Elsland
Fig. 1. Cornerstones in the development of energy system modelling (own illustration based on [7,8,12–15]).
developing countries.
Furthermore, the analysis distinguishes modelling approaches,
meaning the way in which the reality is broken down to a model. There
are different ways of approaching the modelling process of a system,
namely top-down, bottom-up or hybrid. The top-down approach views
the system from an aggregated level ‘from the top’ and disaggregates
from there. Typical top-down models include input-output models,
general-equilibrium models and macro-econometric models. Bottom-up
models, on the other hand, describe a system from the bottom to the
top, using a technological perspective with a process-technological
structure. This means that they start with disaggregated data and aggregate them, finally arriving at the overall system at the end. Models
using the bottom-up approach can be classified into accounting models,
simulation models and optimisation models. With increasing system
complexity, these two modelling approaches are often combined to
obtain a more realistic outcome. These combined models are called
‘hybrid models’ [19,26–28,46].
As indicated in Fig. 2, the analysis revealed that models with the
highest level of accessibility tend to be simulation models. In contrast,
all of the OA frameworks apply the optimisation method. OAMs
needing additional software are also mainly optimisation models, while
capturing some elements of the simulation method. It should be noted
that the results are determined by the model selection. The conclusions
may vary when analysing a different set of models.
In addition, the models listed in Fig. 2 can be characterised using
different criteria, which are mainly identified in the ‘transparency
checklist’ of Cao et al. [29]. For instance, temporal granularity has
strong impacts on modelling results. There is a general distinction between the analyses of transformation paths (where technological trajectories can be traced back to specific drivers) versus the analysis of a
single target year (when a certain configuration is analysed with a
limited consideration of path dependencies). Furthermore, temporal
granularity can vary within the analysed years from time slices on an
hourly basis to a single value for the entire year. Based on these distinctions, the OAMs can be systemised and compared as illustrated in
Fig. 3. The comparison shows that there are three OAMs analysing
transformation paths, even with hourly time slices. When taking into
account the level of accessibility as well, only one model has implemented transformation paths, namely RETScreen [30], which
models a number of years to illustrate a development over time. Most of
the OAMs with the highest level of openness analyse one year with
hourly time slices.
OAMs can also be distinguished by their geographical coverage,
ranging from low (local/cities), through medium (one country), to high
(world/continents), and their sectoral coverage, which can be only one
sector or multiple sectors. This comparison is illustrated in Fig. 4 and
shows high diversity even within the different groups of accessibility.
When looking only at the highest level of accessibility, it becomes clear
context.
The objective of this study is to analyse whether current OAMs have
the analytical depth to assess today's energy scenarios. To do so, we
provide an overview of existing OAMs used for energy system analysis.
In a first step, the models will be typified to show that they have different degrees of accessibility. Subsequently, these models are characterised according to predefined criteria like their level of endogenisation or their temporal boundaries (chapter 2). In a second step,
an exemplary application of a selected OAM is conducted, in which an
energy scenario for Germany with high political relevance is reproduced. Selected parameters of the scenario results generated by the
OAM are compared to the original study using quality criteria (chapter
3). Finally, conclusions are drawn regarding the status quo of OAMs
compared to conventional energy system models currently applied in
the field of energy system analysis (chapter 4).
2. Analysis and evaluation of open access models
This chapter presents the fundamental findings of an analysis of 40
OAMs. Besides the model manuals, we also considered existing review
studies like Connolly et al. [16] or Hall et al. [17]. First, the models
investigated were grouped according to their level of accessibility (typification) [18]. Second, the methodologies of the models were analysed in more detail to derive their capability to investigate current
energy scenarios (characterisation) [19].
For the OAM typification, we distinguished four groups, which
differ in their degree of accessibility (Fig. 2): The highest level of accessibility is achieved by models that are free of charge and directly
downloadable without the need for additional software, such as EnergyPlan [20] or DESSTinEE [15]. The second group includes Open
Access (OA) Frameworks, which act as toolboxes from which single
code parts can be selected and implemented to create a model, which is
then used to analyse a system. One well-known OA framework is oemof
(Open Energy Modelling Framework) [21,44]. This OA framework is
intuitive to use and flexible, to the extent that even sector coupling can
be investigated. The third group consists of OAMs requiring additional
Open Source (OS) software, such as solvers or programming languages,
so that a certain amount of time is needed to download this software
and check its compatibility. This is the case for OSeMOSYS (Open
Source Energy Modelling System) [22,45], for example, which needs
the GLPK (GNU Linear Programming Kit) OS solver [23]. Finally, the
fourth and least accessible group includes models that require additional commercial software. These models are often only published with
their source code and additional programming environments need to be
purchased, such as Balmorel [24], which only operates with GAMS
(General Algebraic Modelling System) [25]. Also included in this group
are models that require a licence. LEAP [14], for example, offers free
access to the licence for selected user groups like universities and
2
Energy Strategy Reviews 26 (2019) 100396
S. Oberle and R. Elsland
Fig. 2. Typification by level of accessibility (own illustration).
North African countries on a national basis.
Other criteria include the time resolution, ranging from high
(hourly) to low (annually), and the spatial granularity, illustrating
whether a country is analysed (low) or a city (high). Fig. 5 shows that
many OAMs include high time resolution, while the level of spatial
that these models tend to have medium level sectoral and geographical
coverage. EnergyPlan [20] has the highest sectoral coverage compared
to the other OAMs and covers nearly every sector such as electricity,
heat, transportation and industry. DESSTinEE [15] appears to have the
highest geographical coverage and analyses nearly all the European and
Fig. 3. Model comparison by temporal granularity (own illustration).
3
Energy Strategy Reviews 26 (2019) 100396
S. Oberle and R. Elsland
Fig. 4. Model comparison by geographical and sectoral coverage (own illustration).
Fig. 5. Model comparison by spatial granularity (own illustration).
3. Exemplary application of a selected open access model
granularity varies. Models with lower time resolution tend to analyse a
system with lower spatial granularity. Looking at highly accessible
models reveals that these tend to have rather low spatial but high time
resolution.
Finally, OAMs are compared by the level of technological granularity and the level of endogenisation. These criteria represent the
magnitude of detail with which technologies are included in the model
and to what extent decision-making is represented by a certain set of
rules or simple exogenous parameter determinations. Both criteria are
compared in Fig. 6. The models with the highest degree of accessibility
tend to have medium to low technological detail and medium to low
level of endogenisation.
To further investigate the discussed OAMs regarding their capability
to analyse energy scenarios from an energy system perspective, we
applied and tested many of the models discussed in chapter 2. To demonstrate our procedure and make our findings transparent, we illustrate the insights of one OAM modelling run reproducing an energy
scenario for Germany, which had also been analysed using a conventional energy system model. Although this is only an example, the
following discussion illustrates the type of analysis we conducted on a
model-by-model basis.
4
Energy Strategy Reviews 26 (2019) 100396
S. Oberle and R. Elsland
Fig. 6. Model comparison by technological granularity and endogenisation (own illustration).
Table 1
Overview of model specifications of DESSTinEE and PRIMES [13,15].
Analytical approach
Methodology
Programming technique
Geographical coverage
Modelling scope
Sectoral coverage
Time resolution
Time horizon
Level of technology detail
DESSTinEE
Bottom-up
Simulation
Linear
Europe
Energy system
Nearly all sectors (heating sector not in detail)
Mixture of hourly (electricity) and annual (fuel) resolution
2010 and 2050
Low to medium
PRIMES
Hybrid
Simulation, among others (optimisation, price-driven partial-equilibrium)
Dynamic
Europe
Energy economic system
All sectors
5-year steps by time slices
2010 until 2050
High
The models SAM (System Advisor Model) [33] and RETScreen [30]
focus on different technology projects, such as a PV power plant, and do
not analyse an energy system of a country. Furthermore, WILMAR [34]
an acronym for Wind Power Integration in Liberalised Electricity
Markets, analyses the integration of wind power and not the overall
system, and the model SIREN (SEN Integrated Renewable Energy Network Toolkit) [35] only analyses the electricity sector, so that its sectoral scope is too low. STREAM [36] is mainly implemented for the use
of analysing Denmark, so that its geographical scope is too focused.
Lastly, the ETM (Energy Transition Model) [37] is only available online
with a restricted capability of including data, so that mainly the predefined databases have to be used and EnergyPlan [20] can only simulate one country integrating the Smart Grid approach.
Based on this process of exclusion the OAM named DESSTinEE can
be identified as the most appropriate [15]. When comparing DESSTinEE
with PRIMES, there are obvious similarities between the two (Table 1).
Both models can simulate the energy system in 2050 and cover a broad
range of sectors, such as electricity, industry, households, commercial
and transport. However, there are also significant differences, for instance, DESSTinEE is a bottom-up model simulating an equilibrium
across 40 countries based on a multiregional merit-order stack in hourly
time steps for 2010 and 2050. In contrast, PRIMES is a hybrid model
calculating a price-driven partial equilibrium across 35 countries in 5year time steps from 2010 until 2050 based on reference type days
representing the different periods throughout the year [13].
3.1. Scenario and model selection
The main application field of energy system analyses is the investigation of strategic questions such as the contribution alternative
political frameworks can make to achieving energy and climate policy
goals. The energy scenarios for the European Commission, such as the
‘EU Reference Scenario 2016′ selected here, represent the European
reference framework to quantify the energy and climate goals in the EU
[31,32]. The modelling platform PRIMES from the University of Athens
is used to analyse this scenario for the European Commission [32].
Consequently, the OAM selected for the comparison of modelling
insights should match the characteristics of the PRIMES model as closely as possible, meaning that the model should examine the European
energy system including all sectors in 2050. In addition, we aim to
select an OAM with a high level of accessibility, i.e. a model that is
directly downloadable and useable (the rectangular group from the
previous analysis). This results in eight possible models:
• EnergyPlan
• STREAM
• WILMAR
• DESSTinEE
• SIREN
• SAM
• RETScreen
• ETM
5
Energy Strategy Reviews 26 (2019) 100396
S. Oberle and R. Elsland
Final Energy Consumption [TWh]
3000
2500
2000
1500
0.02 %
192
0
131
0
158
131
528
532
677
657
0
158
131
0.20 %
0.00 %
0.00 %
0.00 %
9.4 %
10
231
118
532
0.00 %
580
657
1000
500
0
929
0.00 %
967
109
PRIMES
Oil
0
152
99
-16.1 %
673
567
24.6 %
609
8.2 %
109
DESSTinEE
44
PRIMES
-0.3 %
2010
Solids
34.3 %
16.0 %
428
967
0.11 %
129
Energy balance
100 %
559
45
DESSTinEE
2050
Gas
Electricity
Heat
Renewable
Others
Fig. 7. Final energy consumption of DESSTinEE and PRIMES by energy carrier in 2010 and 2050 (own illustration based on [15,31,38]).
for the electricity production in 2010 is especially noticeable. Whereas
PRIMES has a high ability to align calibration to the energy balance, the
generation values of DESSTinEE show a lower calibration ability. While
the electricity production from nuclear power plants calculated by
DESSTinEE with 145 TWh is quite close to the historic energy balance
with an electricity production from nuclear power plants of 141 TWh,
the production by solids fired power plants is far lower (−94 TWh)
determined by DESSTinEE than the historical data. This high deviation
does not seem plausible. In general, electricity production can be calculated based on the final energy consumption, the installed capacity
and the full load hours of installed power plants. It seems that the
calculation procedure of DESSTinEE for the year 2010 does not include
the full load hours, so that further development is needed. Further, the
lack of documentation in DESSTinEE means a concise explanation for
this large deviation cannot be given in more detail (e.g. the impact of
electricity imports while calibrating the model). This also indicates a
low traceability of the decision logic in DESSTinEE.
Focussing on the scenario results for 2050, the deviation between
PRIMES and DESSTinEE is essentially driven by electricity produced by
gas power plants. As can be seen in Fig. 7, the electricity consumption
of DESSTinEE is around 673 TWh in 2050 while Fig. 8 illustrates an
electricity production of slightly more than 700 TWh. The marginally
higher electricity production seems to be due to transmission losses,
which are taken into account by DESSTinEE. Consequently, for the
2050 scenario results, DESSTinEE shows a good dynamic in mapping
the interactions of system components, such as the electricity production matches the electricity demand.
Overall, DESSTinEE has the potential to provide interesting insights
for energy system analysis on a rather aggregated level. However, the
parameters differ in terms of the previous defined quality criteria. In
terms of final energy consumption, the analysis shows a lower granularity in mapping system components in DESSTinEE than in PRIMES.
The scenario results for electricity generation calculated by DESSTinEE
reveal difficulties with tracing its decision logic for the 2010 values. It
seems that the calculation of the electricity production needs to be more
accurate, for example by taking into account the full load hours of the
installed power plants. However, DESSTinEE does seem to have a valid
dynamic when mapping interactions between system components for
the electricity demand and production in 2050. In the end, different
results lead to different recommendations for decision-makers, but this
is a general phenomenon and not explicitly related to OAMs.
3.2. Evaluation of the EU Reference Scenario for Germany
In order to be able to compare the results of DESSTinEE and
PRIMES, we first had to define quality criteria which help to describe
the quality of the results produced by the models. The following four
criteria were used for the comparison: Ability to align the model calibration close to the energy balance, level of granularity when mapping
system components, consideration of dynamics when mapping interactions between system components, and traceability of the model's
decision logic. We discuss these quality criteria below, focussing on
final energy consumption and electricity production.
Comparing the final energy consumption for the base year (2010) in
both models indicates that DESSTinEE matches the PRIMES data quite
well on an aggregated level (Fig. 7). However, on a technology level, it
is more difficult to compare the two because PRIMES differentiates
between 150 technologies, whereas DESSTinEE only considers 15 distinct technologies. Consequently, the level of granularity when mapping system components is lower for DESSTinEE than for PRIMES.
However, both models show a high ability to align model calibration
close to the historical data of the energy balance.
In terms of the target year 2050, parameters like the population and
the Gross Domestic Product are taken directly from PRIMES, so that the
main activity parameters can be set to the same level (Fig. 7). However,
the breakdown of the activity drivers, for instance, the development of
the sectoral Gross Value Added, cannot be implemented in such a detailed manner in DESSTinEE. In addition, there are differences in terms
of methodology, system component interactions and boundary conditions between the models, which cannot be quantified in detail. Typical
examples for differences regarding boundary conditions are usage requirements for renewable energy carriers, accessibility of end consumers to the gas grid or restrictions in terms of fuel switching. Overall,
this leads to a deviation of 9.4% in the total final energy consumption in
2050 between the two models.
To calculate the electricity generation of the power plant sector,
generation capacities must be determined as a first step. For this purpose, the endogenously determined values of PRIMES are used as input
into DESSTinEE, which has no endogenous decision logic to derive
generation capacities. Hence, only minor deviations occur for the installed capacity, which are essentially attributed to different levels of
granularity regarding the technologies implemented. In terms of the
endogenously calculated electricity generation (Fig. 8), there is a significant difference between the scenario results of PRIMES and those of
DESSTinEE. The deviation of 16.5% between PRIMES and DESSTinEE
6
Energy Strategy Reviews 26 (2019) 100396
S. Oberle and R. Elsland
Fig. 8. Electricity production of DESSTinEE and PRIMES by energy carrier for Germany in 2010 and 2050 (own illustration based on [15,31,38]).
4. Conclusions and outlook
model parameters are only disclosed to a limited extent, the decision
logic is hard to follow, which raises the question whether non-intuitive
results are findings or model artefacts and there are limitations in the
level of modelling depth, which are reflected by a limited number of
parameters and a limited representation of dynamic interactions between technologies. With regard to the model manuals and a hard to
follow decision logic a comparison to non-OAMs cannot be given directly, because a model that is made publicly available requires a higher
standard in documentation.
Despite these limitations, however, the analysed OAMs are able to
assess today's energy scenarios but often in a more simplified way than
the state-of-the-art models applied commercially by academic institutions and consultancy firms. Taking into account the time frame of the
OS movement and how long commercial models have been around
shows that the current OAMs are just at the beginning of their development stage, while commercial models are already well established.
Therefore, it is obvious that these OAMs are usually not as sophisticated
as the available commercial models. Just like the commercial models,
the OAMs are developed in a simplified way as a first step and in a
group effort the complexity and add-ons will increase over time.
Consequently, OAMs are on the right road to achieving a competing
level of accuracy, while also providing a much higher level of transparency.
As the open access movement is characterised by a very dynamic
development, we emphasise that these findings should be interpreted as
a snapshot. The demand for more sophisticated OAMs as well as the
discussion of certain standards for documentation are already visible
today, for example in Refs. [11,18,39], and might lead to a broader
application of OAMs in the future. We would also like to stress that an
energy system analysis requires fundamental knowledge of energy
economics as well as energy technology and is not simply a matter of
applying a model. Easy access to powerful OAMs will therefore always
carry the risk of fundamental misinterpretations of the results.
In conclusion, our comparison of 40 OAMs indicates that the energy
system models examined are very diverse. The typification according to
the level of accessibility shows that it is not possible to draw specific
methodological conclusions for each type of model. However, the
analysis also shows that the analytical approach chosen is essentially
the bottom-up perspective. Furthermore, the characterisation of OAMs
reveals that the models are restricted in terms of their level of endogeneity as well as technology detail. This implies that the complexity
of OAMs is more limited than state-of-the-art commercial models. In
addition, many OAMs simply focus on single target years in their analysis and only consider the development of transformation paths to a
limited extent. Overall, our analysis shows that today's OAMs are able
to address a large number of research questions. Nevertheless, due to
the methodological and technological design of some models, their
findings should be interpreted as indicative results and not equated
with the results of highly sophisticated energy system models, which
often are implemented in more detailed manner, for example with a
highly detailed level of regional resolution.
The exemplary application of DESSTinEE and subsequent comparison to PRIMES′ results reveals that this model has considerable potential to provide insights into the country-specific development of the
European energy system. However, it also revealed that DESSTinEE
relies on a large amount of exogenous data, such as the generation
capacity, whereas the optimal generation capacity mix is a core result of
PRIMES′ decision logic. Looking for explanations of DESSTinEE's implausible modelling results (e.g. electricity production in 2010) revealed the partially low traceability of the applied decision logic.
Although the exemplary application here is based on only one OAM,
similar problems appeared when analysing other OAMs with the
highest level of accessibility: Model manuals are sometimes incomplete
and incomprehensible in parts, assumptions about uninfluenceable
A. Appendix
A.1
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Energy Strategy Reviews 26 (2019) 100396
S. Oberle and R. Elsland
Overview of open access models by accessibility and origin.
Energy System
Models
Free accessible
Community
(Enhancements)
Origin
Source
EnergyPlan
Yes
Online Forum
http://www.energyplan.eu/getstarted/
STREAM
WILMAR
Yes
Yes
No
No
DESSTinEE
Yes
No
SIREN
Yes
Sustainable Energy Planning Research
Group at Aalborg University, Denmark
(Henrik Lund)
Danish Board of Technology
Risø supported by the European
Commission under the Fifth Framework
Programme (Helge V. Larsen)
Imperial College London (ICL), London,
United Kingdom (Iain Staffell and
Richard Green)
Sustainable Energy Now Inc. (Angus
King et al.)
NREL a laboratory of the U.S.
Department of Energy, Office of Energy
Efficiency and Renewable Energy, operated by the Alliance for Sustainable
Energy, LLC
Minister of Natural Resources Canada
Quintel Intelligence, Amsterdam, the
Netherlands
ORDECSYS
SAM
Yes, volunteering
Yes, but needs registra- Support Forum
tion
RETScreen
ETM
Yes
Yes, online
Mailing list
No
ETEM
Yes, with additional
open source software
Yes, with additional
open source software
No
OSeMOSYS
GCAM
renpass
Yes, with additional
open source software
Yes, with additional
open source software
renpassG!S
Yes, with additional
open source software
OnSSET
Yes, with additional
open source software
pandapower
Yes, with additional
open source software
PowerMatcher
Yes, with additional
open source software
Yes, with additional
open source software
PyPSA
SWITCH
Yes, with additional
open source software
EnergyPATHWAYS
Yes, with additional
open source software
ficus
Yes, with additional
open source software
SciGRID
Yes, with additional
open source software
NEMO
Yes, with additional
open source software
Yes, with additional
open source software
Yes, with additional
open source software
EMLab-Generation
DINGO
Online Forum
Energy Systems Analysis Group (dESA),
KTH Royal Institute of Technology,
Stockholm, Sweden
(M. Howells, H. Rogner, N. Strachan, C.
Heaps, H. Huntington, S. Kypreos, A.
Hughes, S. Silveira, J. DeCarolis, and M.
Bazillian)
GCAM
The Joint Global Change Research
Community
Institute (JGCRI)
No
Dissertation Frauke Wiese at Centre for
Sustainable Energy Systems (CSES or
ZNES), University of Flensburg,
Germany; Reiner Lemoine-Stiftung
Yes, with GitHub Based on Renpass and oemof;
Sustainable Energy Systems (Zentrum
für nachhaltige Energiesysteme (ZNES))
in Flensburg
Yes, with GitHub Energy Systems Analysis Group (dESA),
KTH Royal Institute of Technology,
Stockholm, Sweden
No
Energy Management and Power System
Operation research group, University of
Kassel and the Department for
Distribution System Operation,
Fraunhofer Institute for Wind Energy
and Power Systems Technology (IWES),
both of Kassel, Germany
Community,
Flexiblepower Alliance Network (FAN)
with GitHub
in Amsterdam, the Netherlands
only Google
Frankfurt Institute of Advanced Studies
Group
(FIAS), Goethe University Frankfurt,
Frankfurt Germany
No
Department of Electrical Engineering,
University of Hawai'i, Mānoa, Hawaii,
USA
No
Energy and climate protection consultancy, Evolved Energy Research, San
Francisco, USA
No
Institute for Energy Economy and
Application Technology, Technical
University of Munich, Munich, Germany
Only newsletter NEXT ENERGY - EWE Research Centre
for Energy Technology, an independent
non-profit institute at the University of
Oldenburg, Germany
Yes, via mailing University of New South Wales
list
No
Delft University of Technology
Yes, with GitHub open_eGo-Team from Reiner Lemoine
Institut and LEW Verteilnetz GmbH
http://www.streammodel.org/downloads.html
http://www.wilmar.risoe.dk/
https://sites.google.com/site/2050destinee/
http://www.sen.asn.au/modelling_overview
https://sam.nrel.gov/
http://www.nrcan.gc.ca/energy/software-tools/7465
https://pro.energytransitionmodel.com/
http://ordecsys.com/en/etem
http://www.osemosys.org/get-started.html
http://www.globalchange.umd.edu/gcam/
https://github.com/fraukewiese/renpass
https://github.com/znes/renpass_gis
http://www.onsset.org/
https://www.uni-kassel.de/eecs/en/fachgebiete/e2n/software/
pandapower.html
https://flexiblepower.github.io/
www.pypsa.org/
http://switch-model.org/
https://github.com/energyPATHWAYS/energyPATHWAYS
https://github.com/yabata/ficus
http://www.scigrid.de/pages/downloads.html
https://nemo.ozlabs.org/
http://emlab.tudelft.nl/
http://dingo.readthedocs.io/en/dev/index.html
(continued on next page)
8
Energy Strategy Reviews 26 (2019) 100396
S. Oberle and R. Elsland
A.1 (continued)
Energy System
Models
Free accessible
Community
(Enhancements)
OEMOF
Yes, with additional
open source software
https://oemof.org/
URBS
Yes, at GitHub
Calliope
Yes, at GitHub
TEMOA
Yes, at GitHub
Balmorel
Yes, with additional
commercial software
Yes, with additional
commercial software
only source code, needs
additional commercial
software
only source code, needs
additional commercial
software
Yes, with additional
commercial software
Yes, with additional
commercial software
Yes, with additional
commercial software
Yes, with additional
commercial software
Yes, with additional
commercial software
Yes, with additional
commercial software
Needs licence, which is
free for developing
country government,
NGO, academic organization or students
HOMER QuickStart is
free
Yes, with GitHub Reiner Lemoine Institut,
Centre for Sustainable Energy Systems
at the University of Flensburg and the
Flensburg University of Applied
Sciences
No
Institute for Renewable and Sustainable
Energy Systems, Technical University of
Munich, Germany
Chat on Gitter
Department of Environmental Systems
Science, ETH Zurich, Zürich,
Switzerland
Yes, google
Department of Civil, Construction, and
group
Environmental Engineering, North
Carolina State University, Raleigh,
North Carolina, USA (Joe DeCarolis)
No
The Balmorel Open Source Project
Yes, Forum
http://energyinteractive.net/
COMPOSE
MARKAL
TIMES
NEMS
DIETER
Dispa-SET
EMMA
StELMOD
REMIND
LEAP
HOMER
Origin
Source
Aalborg University in Denmark
https://github.com/tum-ens/urbs
https://www.callio.pe/
http://temoaproject.org/
http://www.balmorel.com/index.php
Energy Technology Systems Analysis
Programme (ETSAP) of the
International Energy Agency
Yes, IEA-ETSAP
Energy Technology Systems Analysis
Community
Programme (ETSAP) of the
International Energy Agency
No
Energy Information Administration
(EIA) of the U.S. Department of Energy
No
DIW (German Institute for economy
research in Berlin)
Yes, with GitHub Joint Research Centre, EU Commission
and University of Liège, Belgium
No
Neon Neue Energieökonomik GmbH
https://iea-etsap.org/index.php/etsap-tools/model-generators/markal
Yes, with GitHub DIW (German Institute for economy
research in Berlin)
No
Potsdam Institute for Climate Impact
Research
Yes, LEAP
Stockholm Environment Institute
Community
(Charlie Heaps)
http://www.diw.de/de/diw_01.c.528493.de/forschung_beratung/
nachhaltigkeit/umwelt/verkehr/energie/modelle/elmod.html
https://www.pik-potsdam.de/research/sustainable-solutions/models/
remind
https://www.energycommunity.org/default.asp?action=introduction
Yes, Forum
https://www.homerenergy.com/
Yes, IEA-ETSAP
Community
HOMER Energy LLC
https://iea-etsap.org/index.php/etsap-tools/model-generators/times
https://www.eia.gov/outlooks/aeo/info_nems_archive.php
http://www.diw.de/de/diw_01.c.508843.de/forschung_beratung/
nachhaltigkeit/umwelt/verkehr/energie/modelle/dieter/dieter.html
http://www.dispaset.eu/en/latest/
https://neon-energie.de/en/emma/
A.2
Overview of open access models by methodological approach.
Energy System
Models
Free accessible
Analytical approach
Methodology
Topdown
Top-down
Bottomup
Input/
Output
EnergyPlan
STREAM
WILMAR
DESSTinEE
SIREN
SAM
RETScreen
ETM
ETEM
OSeMOSYS
GCAM
renpass
Yes
Yes
Yes
Yes
Yes
Yes, but needs registration
Yes
Yes, online
Yes, with additional open source
software
Yes, with additional open source
software
Yes, with additional open source
software
Bottom-up
Economic
equilibrium
x
x
x
x
x
x
x
x
x
Econometric
Accounting
x
Other
Simulation
Optimisation
x
x
x
x
x
x
x
x
x
Spreadsheet
x
x
x
x
Spreadsheet
x
x
x
partial
x
x
x
x
(continued on next page)
9
Energy Strategy Reviews 26 (2019) 100396
S. Oberle and R. Elsland
A.2 (continued)
Energy System
Models
Free accessible
Analytical approach
Methodology
Topdown
Top-down
Bottomup
Input/
Output
renpassG!S
OnSSET
pandapower
PowerMatcher
PyPSA
SWITCH
EnergyPATHWAYS
ficus
SciGRID
NEMO
EMLab-Generation
DINGO
OEMOF
URBS
Calliope
TEMOA
Balmorel
COMPOSE
MARKAL
TIMES
NEMS
DIETER
Dispa-SET
EMMA
StELMOD
REMIND
LEAP
HOMER
Yes, with additional open source
software
Yes, with additional open source
software
Yes, with additional open source
software
Yes, with additional open source
software
Yes, with additional open source
software
Yes, with additional open source
software
Yes, with additional open source
software
Yes, with additional open source
software
Yes, with additional open source
software
Yes, with additional open source
software
Yes, with additional open source
software
Yes, with additional open source
software
Yes, with additional open source
software
Yes, with additional open source
software
Yes, at GitHub
Yes, at GitHub
Yes, at GitHub
Yes, with additional commercial
software
Yes, with additional commercial
software
only source code, needs additional
commercial software
only source code, needs additional
commercial software
Yes, with additional commercial
software
Yes, with additional commercial
software
Yes, with additional commercial
software
Yes, with additional commercial
software
Yes, with additional commercial
software
Yes, with additional commercial
software
Needs licence, which is free for
developing country government,
NGO, academic organization or students
HOMER QuickStart is free
x
Bottom-up
Economic
equilibrium
Econometric
Accounting
Other
Simulation
Optimisation
partial
x
x
x
x
x
x
agent-based
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
agent-based
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
partial
x
Partial
x
partial
x
Toolbox
Partial
x
partial
x
Toolbox
x
x
x
x
agent-based
x
x
x
x
x
x
x
partial
x
x
x
x
x
x
x
x
x
x
x
x
x
x
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