overview and evaluation of models in use for m/a policy portfolios ...

PROMITHEAS-4
OVERVIEW AND EVALUATION OF
MODELS IN USE FOR M/A POLICY
PORTFOLIOS AND SELECTION THE
RELEVANT FOR PROMITHEAS-4
Dr. Todor Balabanov, IHS, Vienna, e-mail: balabano@ihs.ac.at
3/9/2011
Abstracts: After short overview of the models used in some integrated project financed by the FP
5,6 and 7 the main features of the 12 widely used energy-environmental models will be shortly
described then we will be presenting models subdivision into “Top-Down” and “Bottom-Up” as
well as the recent modelling advances, and on the basis of eight criteria we will be selecting the
integrated energy-environmental modelling tool best suited to the objectives of the PROMITHEAS 4
project

Table of Contents:
1. Climate Mitigation and Adaptation ________________________________________ 3
1.1. Mitigation Strategies _______________________________________________________ 3
1.2. Adaptation Strategies _______________________________________________________ 3
2. Research on Adaptation , Mitigation Policies in FP5/FP6/FP7 ___________________ 3
2.1. Overview of models used in the ADAM project __________________________________ 4
2.1.1. E3MG ________________________________________________________________________ 4
2.1.2. MERGE ________________________________________________________________________ 5
2.1.3. REMIND _______________________________________________________________________ 6
2.1.4. POLES _________________________________________________________________________ 6
2.1.5. TIMER _________________________________________________________________________ 7
2.2. Research on Adaptation , Mitigation and Policies in FP7 ___________________________ 7
2.2.1. Modelling Framework of the POEM project ___________________________________________ 8
2.2.2. Ensemble of models of the TOCSIN project ___________________________________________ 9
3. A review of widely used energy-environmental modelling tools________________ 10
3.1. LEAP ___________________________________________________________________ 10
3.2. EnergyPLAN _____________________________________________________________ 11
3.3. MARKAL/TIMES __________________________________________________________ 12
3.4. MESSAGE _______________________________________________________________ 13
3.5. IKARUS _________________________________________________________________ 13
3.6. INFORSE ________________________________________________________________ 14
3.7. Mesap PlaNet ____________________________________________________________ 14
3.8. PRIMES and GEM-E3 Models ________________________________________________ 14
3.9. BALMOREL ______________________________________________________________ 15
3.10. ENPEP-BALANCE ________________________________________________________ 16
3.11. MERCI-ATHDM E3: The Austrian Hybrid Dynamic Model E3 _____________________ 16
4. A brief classification of energy models ____________________________________ 17
4.1.1. ‘Top-Down’ Models _____________________________________________________________ 17
4.1.2. ‘Bottom-Up’ Models ____________________________________________________________ 18
4.1.3. Recent Modelling Advances ______________________________________________________ 19
5. Selection of an integrated modelling tool __________________________________ 21
6. References ___________________________________________________________ 23
6.1. References to the models used in FPs funded projects ___________________________ 27
2

1. Climate Mitigation and Adaptation
The terms ―adaptation‖ and ―mitigation‖ are two important terms that are fundamental in the climate change
debate. The IPCC defined adaptation as adjustment in natural or human systems in response to actual or expected
climatic stimuli or their effects, which moderate harm or exploits beneficial opportunities. Similarly, Mitchell
and Tanner (2006) defined adaptation as an understanding of how individuals, groups and natural systems can
prepare for and respond to changes in climate or their environment. According to them, it is crucial to reducing
vulnerability to climate change. While mitigation tackles the causes of climate change, adaptation tackles the
effects of the phenomenon. The potential to adjust in order to minimize negative impact and maximize any
benefits from changes in climate is known as adaptive capacity. A successful adaptation can reduce
vulnerability by building on and strengthening existing coping strategies.
In general the more mitigation there is, the less will be the impacts to which we will have to adjust, and the less
the risks for which we will have to try and prepare. Conversely, the greater the degree of preparatory adaptation,
the less may be the impacts associated with any given degree of climate change.
For people today, already feeling the impacts of past inaction in reducing greenhouse gas emissions, adaptation
is not altogether passive, rather it is an active adjustment in response to new stimuli. However, our present age
has proactive options (mitigation), and must also plan to live with the consequences (adaptation) of global
warming.
The idea that less mitigation means greater climatic change and consequently requiring more adaptation is the
basis for the urgency surrounding reductions in greenhouse gases. Climate mitigation and adaptation should not
be seen as alternatives to each other, as they are not discrete activities but rather a combined set of actions in an
overall strategy to reduce greenhouse gas emissions.
1.1. Mitigation Strategies
Climate change involves complex interactions between climatic, environmental, economic, political,
institutional, social, and technological processes. It cannot be addressed or comprehended in isolation of broader
societal goals (such as equity or sustainable development), or other existing or probable future sources of stress.
In the United Nations Framework Convention on Climate Change (UNFCCC) three conditions are made explicit
when working towards the goal of greenhouse gas stabilisation in the atmosphere:
1. That it should take place within a time-frame sufficient to allow ecosystems to adapt naturally to climate
change;
2. That food production is not threatened and;
3. That economic development should proceed in a sustainable manner
To eliminate or reduce the risk of climate change to human life and property, both policy instruments and
technology must be used in the context of sustainable development.
1.2. Adaptation Strategies
The United Nations Framework Convention on Climate Change refers to adaptation in several of its articles:
Article 4.1(f): All Parties shall ―Take climate change considerations into account, to the extent feasible, in their
relevant social, economic and environmental policies and actions, and employ appropriate methods, for example
impact assessments, formulated and determined nationally, with a view to minimizing adverse effects on the
economy, on public health and on the quality of the environment, of projects or measures undertaken by them to
mitigate or adapt to climate change.‖
2. Research on Adaptation , Mitigation Policies in FP5/FP6/FP7
According to Dr. Wolfram Schrimpf (2008) from the Climate Change and Environmental Risks Unit of the
Environment Directorate, DG Research, mitigation and adaptation are to be seen as a ‗co-exercise‘. Practical
adaptation actions and measures are to be based on sound, scientific, technical and socio-economic information
(ref. i.e. Green Paper, Third Pillar ‘ Reducing uncertainty by Expanding the knowledge base +through
integrated climate research)
3

Accordingly in FP5/FP6 a number of projects related to Adaptation and Mitigation Strategies and to prediction
of climatic change and its impacts have been financed. Examples of integrated projects are:
ADAM Adaptation and Mitigation Strategies: Supporting European climate Policy - started March
2006 and finished in July 2009, financed with € 18.2 million, involving 120 researchers in 26 research
institutions across Europe and worldwide; A comparison of models used in the ADAM project is
presented in below; http://www.adamproject.eu/
PRUDENCE Prediction of regional Scenarios and Uncertainties for Defining European Climate
Change Risks and Effects; http://prudence.dmi.dk/
ENSEMBLES Predictions of climate changes and their impacts; http://ensembles-eu.metoffice.com/,
among the partners are: National Observatory of Athens and Aristotle University of Thessaloniki,
CECILIA Central and Eastern Europe Climate Change Impact and Vulnerability Assessment;
http://www.cecilia-eu.org/
CIRCE Climate Change and Impact Research: the Mediterranean Environment,
http://www.circeproject.eu/index.php?option=com_frontpage&Itemid=1
2.1. Overview of models used in the ADAM project
ADAM supports the EU in the development of post-2012 global climate policies, the definition of European
mitigation policies to reach its 2020 goals, and the emergence of new adaptation policies for Europe with special
attention to the role of extreme weather events.
The ADAM project was addressing the following questions: How can the global energy system be transformed?
What are the possible mitigation pathways? What are the associated costs?
We report on the comparison of five energy-environment-economy models, namely: the macro-econometric
simulation model E3MG, the optimal growth models MERGE-ETL (abbreviated as MERGE in the following)
and REMIND-S (abbreviated as REMIND in the following) and the energy system models POLES and TIMER.
E3MG, MERGE and REMIND are top-down macroeconomic models with a more or less sophisticated energy
system. POLES and TIMER are bottom-up energy system models.
The policy scenarios are analysed for the period from 2000–2100 (POLES only until 2050), where the
optimisation models are run beyond 2100 to avoid end effects. Data are provided on a five year basis (10 year for
E3MG). The models provide different numbers of World regions. For this comparison exercise we agreed on
seven World regions: China (CHN), Eastern Europe and Russia (EERU), Europe (EU15), India (IND), Japan
(JPN), USA (USA), and the rest of the World (ROW). Additionally we investigate the aggregated WORLD
region.
Model details: For a better understanding of the models, a short description of each model is given below.
Moreover, we present a short overview concerning the way induced technological change (ITC) is modelled and
how an emissions trading system (ETS) is implemented in each model, as these are central points for modelling
the scenarios
2.1.1. E3MG
E3MG - E3 stands for energy-environment- economy, M for model and G for Global
The Cambridge Centre for Climate Change Mitigation Research has build an econometric simulation model of
the global energy-environment- economy (E3) system, estimated on annual data 1971-2002 and projecting
annually to 2030 and every 10 years to 2100. It is a disequilibrium model with an open structure such that labour,
foreign exchange and public financial markets are not necessarily closed. It is very disaggregated, with 20 world
regions (including the 13 nation states with the highest CO2 emissions in 2000), 12 energy carriers, 19 energy
users, 28 energy technologies, 14 atmospheric emissions and 42 industrial sectors, with comparable detail for the
rest of the economy. The methodology of the model can be described as post-Keynesian, following that of the
European model E3ME developed by Cambridge Econometrics, except that at the global level various markets
are closed, e.g. total exports equal total imports at a sectoral level allowing for imbalances in the data. It is
designed to address the issues of energy security and climate stabilisation both in the medium and long terms,
with particular emphasis on dynamics, uncertainty and the design and use of economic instruments, such as
emission allowance trading schemes.
Induced technological change (ITC) in E3MG incorporates endogenous technological change in three ways: (i)
Top-down macroeconomic effects: the sectoral energy and export demand equations include indicators of
technological progress in the form of accumulated gross investment and Research and Development (R&D), (ii)
bottom-up engineering effects: the energy technology submodel incorporates learning-by-doing through
reductions in costs of investment in energy-generation technologies according to global scale economies, and
(iii) amplifying effects: the extra investment in new technologies, in relation to baseline investment induces
4

further output and therefore investment, trade, income, consumption and output in the rest of the world economy
through a Keynesian multiplier effect. R&D is endogenously related to industrial gross investment, with an
exogenous component determined by government funding policy. It affects aggregate and disaggregate energy
demand. R&D is also part of gross investment which is a measure of technological progress impacting exports,
imports, industrial hours worked, employment, industrial prices, export and import prices. All these are part of
the macro-econometric model and the corresponding parameters are econometrically estimated. R&D is in turn
influenced by extra investments in new technologies, which emerge from mitigation measures that induce
change towards low carbon technologies. Furthermore, R&D is amongst the policy instruments in the model
promoting greenhouse gas abatement. Out of the 28 energy technologies available in E3MG, 14 have learning
modes included and are effectively used in the model. Learning is implemented in the energy technology
bottom-up submodel through learning curves with varying learning rates. Learning curves are modeled through
regional investment in energy generation technologies that depend on global scale economies. As investment is
made in new technologies, learning takes place and the cost of these new technologies decreases so that they
become competitive with the more ―established‖ technologies. The model is capable of explaining how lowcarbon
technologies are adopted as the real cost of carbon rises in the system, with learning-by-doing reducing
capital costs as the scale of adoption increases. A switch from one technology to another is modeled within the
energy technology bottom-up submodel in E3MG. This allows for a treatment of substitution between energy
technologies (for example between fossil and non-fossil fuel technologies), accounting for non-linearities (and
threshold effects) resulting from investment in new technology, learning-by-doing, and innovation. The
submodel is an annual dynamic technology model of energy supply based on the concept of a price effect on the
elasticity of substitution between competing technologies. Although the submodel is not estimated by formal
econometric techniques it does model in a simplified way the switch from carbon energy sources to non-carbon
energy sources over time. For each type of energy demanded there is usually a technology or fuel of choice – a
marker technology – against which the alternatives will have to compete. The total capital and operating costs of
using this fuel per unit of output are used as a basis or numeraire for expressing the relative costs of the
alternatives.
Emissions trading: The emissions trading scheme is implemented in E3MG globally only for the energy sector
through permit auctioning. The emissions trading is assumed to start from 2011 and the revenues raised are
recycled and spent within each of the corresponding region by reducing indirect taxes to ensure macroeconomic
inflation stability. For the non-energy sectors carbon taxes are globally applied with the tax rate given by the
ETS price, again assuming revenue recycling region-by-region.
2.1.2. MERGE
The Model for Evaluating Regional and Global Effects (MERGE) of Paul Scherrer Institute is an integrated
assessment model (IAM) that comprises a disaggregation of the energy system in electric and non-electric
sectors, a macro-economic production function and a simplified climate model. The world modelled in MERGE
is divided into nine geopolitical regions. An ETA-MACRO model describes each of these nine regions. The ETA
component is a ―bottom-up‖ engineering model that describes the energy-supply sector of a given region, and
captures price-dependent substitutions of energy forms and energy to achieve specified CO2 reduction targets.
The MACRO component is a ―top-down‖ Ramsey type macroeconomic growth model that balances the non
energy part of the economy of a given region using a nested constant-elasticity-of substitution (CES) production
function. The MACRO model also captures autonomous (e.g., price-independent) effects and macroeconomic
feedbacks between the energy sector and the rest of the economy, such as the impacts of higher energy prices
(e.g., resulting from CO2 control) on economic activities. MERGE also includes a simple climate and damage
model which considers market (through production losses), and non-market damages (through losses in global
welfare). Those derived non-market damages (i.e. disutility) are proportional to a quadratic temperature change
function, the global temperature increase being a function of the aggregate carbon emissions levels. Finally,
MERGE allows the modeling of multi-gas climate-change mitigation strategies and the assessment of the impact
of alternative policy instruments on stimulating technological change towards a low-carbon global energy
system.
All energy technologies include learning (ITC), with the exception of a few conventional power stations such as
existing fossil-based or hydro power plant. MERGE applies two-factor learning curves for the investment costs
of each learning technology. Experience and R&D expenditures are thus endogenously accounted for in
MERGE. In both electric and non-energy sectors, all technologies are perfect substitutes, i.e. all production
levels are summed up to satisfy energy needs. The least cost technology supplies the demand, up to some
maximum potential level, e.g. wind power capacity or biomass availability. The changes in each production level
are controlled according to bounds on expansion and decline rates (see Kypreos and Bahn, 2003).
Emissions trading: CO2 permits are traded on an international market and give rise to a price of carbon, along
with other traded commodities, i.e. the numeraire good, oil, gas, coal, biomass, the energy intensive sector good.
5

Global trade constraints applied in each period ensure that international net trade of CO2 permits (the difference
between export and imports) is balanced.
2.1.3. REMIND
REMIND-S - Potsdam Institute for Climate Impact Research: a multi-region endogenous economic growth
model based on the global integrated assessment model MIND. REMIND models the economic dynamics of
each world region by adopting an endogenous growth framework. It calculates time paths of investment and
consumption decisions that are inter temporally optimal. The objective is to maximize the social welfare of each
region, defined as the present value of utility, which is a function of per capita consumption exhibiting
diminishing marginal utility. Production takes place in an industrial sector and an energy sector. The industrial
sector is decomposed in consumption goods and service sector, and an investment goods sector. Both sectors are
represented by a CES-type production function with capital, labour and energy for production factors. The
energy sector distinguishes a fossil extraction sector, a fossil energy sector, a renewable energy sector and a
remaining energy sector. The latter includes mainly nuclear energy and traditional biomass. Its output is given
exogenously. The macroeconomic system and the energy system are linked by energy, which is demanded by
both industrial sectors and which is supplied by the energy sector. In turn, the energy sector demands
investments which are supplied by the industrial sector. Technological change has an endogenous formulation
with R&D investments in labour and energy productivity, learning by doing, and vintage capital in the different
energy sectors. REMIND extends this structure by essential elements of regional interactions, e.g. trade and
capital flows, and technological spillovers.
Induced technological change (ITC) represents changes in the technological characteristics of the economic and
energy system driven by exogenous policies (e.g. climate policies and trade policies). As part of endogenous
technological, induced technological change is modeled in REMIND in several ways. First, production factors
can be substituted by each other, in particular the production factor energy can be substituted by capital. Second,
R&D investments represent a channel of ITC. They can be redirected to increase both energy and labour
efficiency with different intensity. These efficiency parameters are additionally impacted by technological
spillovers – a third element of ITC. Fourth, ITC is represented by the energy mix, which is driven by investments
into energy technologies. Finally, REMIND models learning-by-doing by means of learning curves, in particular
for renewable energies. Investment costs of renewable energies decrease by 15% for each doubling of
cumulative installed capacity. There is no global learning, hence the different progress of regions in
technological learning is taken into account.
Emissions trading: Each region in REMIND is allocated an amount of emission permits for free, which
corresponds to the regional emission cap and follows a given allocation rule. The sum of the regional emission
permits equals the exogenously given global CO2 budget of the energy sector. For each unit of fossil resources
converted into final energy a permit is needed. Emissions trading provide the opportunity to buy and sell the
permits. At each time step the worldwide sum of the exports of emission permits equals the sum of imports. The
emissions trade is part of the whole trading system. All exports and imports of a region are subject to an
intertemporal trade balance which ensures that no deficits can be sustained in the long run. However, lending and
borrowing of permits is possible temporarily.
2.1.4. POLES
POLES - ENERDATA/ LEPII-EPE – IEPE, Université Pierre Mendes France: works in a year-by-year recursive
simulation and partial equilibrium framework from 2005 to 2050, with endogenous international energy prices
and lagged adjustments of supply and demand by world region. POLES provides a detailed description of a large
number of electricity technologies (large-scale, new and renewables) in each of the 47 POLES countries or
regions covering the world. Their regional development depends on different factors such as fuel prices, policy
choices (nuclear phase out, incentives for renewables through payback tariffs, etc...) and specific features like
seismic activity likely to increase the investment cost for some technologies like nuclear. The improvement in
the technology performances and costs can be projected in two different ways: either using exogenous data from
the TECHPOL database or through endogenous technical mechanisms through two-factor learning curves, which
combine learning by doing and learning by searching processes.
The impact of induced technological change (ITC) on mitigation costs strongly depends on the way
technological change is introduced. Technological change is the complex result of: exogenous events (scientific
discoveries), inducement factors (e.g. R&D investment, relative prices) and endogenous mechanisms (e.g.
learning by doing). The two-factor learning curves (TFLC) in the POLES model are meant to provide a
simplified but meaningful description of technological change. The incorporation in POLES of TFLC
relationships for the main technologies and technology clusters enable POLES to perform R&D policy
simulations in a dynamic environment where an increase in R&D effort produces improvements leading to
higher technology adoption and hence to further improvements through experience gained in a virtuous learning
circle. R&D spending is exogenous in the model.
6

Emissions trading: Emissions trading for each country is calculated as the difference between actual emissions
and allocations. In each scenario POLES determines the Carbon Value for each individual carbon market.
Relative abatement and trading costs are then calculated through the production for each country of the Marginal
Abatement Cost curves of the energy sector: these curves are confronted to the Carbon Value of the amount of
permits traded. The POLES model is then able to determine abatement and trading costs for all countries.
2.1.5. TIMER
TIMER – the Netherlands Environmental Assessment Agency: the global energy model describes long-term
trends in the world energy system, based on interplay of dynamics factors such as development of energy
demand, depletion and technology development of various energy sources and technologies, cost based
substitution and the development of climate policy. The TIMER model has been described in de Vries et al.
(2001) and van Vuuren (2007). The current version of the model cover 26 regions - and the TIMER model forms
a part of the Integrated Assessment Model IMAGE. Energy demand in TIMER is based on scenario assumptions
for economic and population development. The economic activity indicators are combined with assumptions on
technology development for end-use technologies, assumptions on autonomous energy efficiency improvement
and structural change. In a next step, energy demand is met based on selection of different energy carriers,
including coal, oil and natural gas, traditional and modern biomass, electricity, hydrogen and heat. These energy
carriers are selected on the basis of relative costs via a multinomial logit distribution function (van Vuuren,
2007). The final energy carriers are produced from a range of primary energy carriers that on their turn compete
for market share on the basis of costs (e.g. electricity can be produced from fossil fuels, biomass and nuclear,
solar, wind and hydropower). Throughout the model, inertia are introduced by an explicit treatment of vintages
of capital stock. The costs of the primary energy carriers are determined in the long-term by technology
development (mostly based on learning-by-doing i.e. technologies improve with their cumulative build-up of
installed capacity) and resource depletion (driving up costs for extraction of exhaustible energy resources with
their cumulative production; and of renewable resources with annual production). An important technology in
TIMER in the context of climate policy is carbon capture- and-storage. This technology can be applied in
combination with fossil-fuel and biomass fired power plants, in the industry end-use sector and in the production
of hydrogen. Its use is cost driven - where costs are function of capture costs (that decline over time) and storage
costs (that increase as a function of depletion of storage capacity). TIMER is mostly used in conjunction with the
IMAGE integrated assessment model and the FAIR climate policy scanner. The combination of the three models
allows development of multi-gas mitigation scenarios covering not only energy but also land-use related sources.
The FAIR model uses marginal abatement curves (based on TIMER and IMAGE) to select a leastcost emission
reduction pathways across all regions and sources (constrained by assumptions on emission trading). Final
strategies are evaluated in TIMER and IMAGE for energy, climate and land use consequences.
ITC: This description is adopted from van Vurren et al., 2006: An important aspect of the TIMER model is the
endogenous formulation of technological development on the basis of learning by- doing. In the TIMER model,
learning-by-doing influences the capital-output ratio of coal, oil and gas production, the specific investment cost
of renewable and nuclear energy, the cost of hydrogen technologies and the rate at which the energy conservation
cost curves decline. In TIMER, the existence of a single global learning curve is postulated. Regions are then
assumed to pool knowledge and ‗learn‘ together or to be (partly) blocked from this pool. In the latter case, only
the obviously smaller cumulated production within the region itself drives the learning process. Substitution
among energy carriers and technologies is described in the model with a multinomial logit formulation. The
multinomial logit model implies that the market share of a certain technology or fuel type depends on costs
relative to competing technologies. The option with the lowest costs obtains the largest market share, but in most
cases not the full market. The latter is interpreted as a representation of heterogeneity in the form of specific
market niches for every technology or fuel. The multinomial logit mechanism is used in TIMER to describe
substitution among end-use energy carriers, different forms of electricity generation (coal, oil, natural gas,
solar/wind and nuclear) and substitution between fossil fuels and bioenergy.
Emissions trading: A model of mitigation costs and emissions trade (FAIR, Den Elzen and Lucas, 2003) is
linked to the TIMER model of the energy system. This model calculates the tradable emission permits, the
international permit price and the total abatement costs, with or without emissions trading, according to the
regional emission allowances of a certain climate regime. The model uses aggregated permit demand and supply
curves derived from Marginal Abatement Cost (MAC) curves for the different regions, gases and sources. The
obtained international permit price is then implemented in TIMER. In TIMER, ETS is used in all scenarios.
2.2. Research on Adaptation , Mitigation and Policies in FP7
Research on Adaptation in FP7 recognises that the Adaptation should be considered within the ‗triangle‘
mitigation, socio-economic development and adaptation.
7

In the following are some selected projects on Climate Change Adaptation, Mitigation and Policies as listed in
the European Research Framework Program, Research on Climate Change (2009), including those with the
developing countries:
GILDED — Governance, Infrastructure, Lifestyle Dynamics and Energy Demand: European Post-Carbon
Communities, http://www.gildedeu.org/
PACT — Pathways for Carbon Transitions, http://www.pact-carbon-transition.org/
PLANETS — Probabilistic Long-Term Assessment of New Technology Scenarios, http://www.feemproject.net/planets/
2.2.1. Modelling Framework of the POEM project
POEM project - Policy Options to engage Emerging Asian economies in a post-Kyoto regime,
http://themasites.pbl.nl/en/themasites/image/projects/reports/poem.html , is using the IMAGE 2.4 modelling
Framework, developed by the IMAGE team from the Netherlands Environmental Assessment Agency (PBL),
consists of a number of sub models, structured as shown at the Figure 1.
Scenarios: The objective of the IMAGE 2.4 model is to explore the long-term dynamics of global environmental
change, following from human development activities. This requires a coherent image of how the world system
could evolve. Future greenhouse gas emissions, for instance, are the result of complex interacting demographic,
techno-economic, socio-cultural and political forces. Obviously, all of these key driving forces are inherently
uncertain and increasingly uncertain as the time horizon moves into the future. The use of scenarios is a widely
accepted approach for exploring the bandwidth of future developments and the sensitivity of the outcome to
alternative, yet internally consistent, sets of assumptions. Scenarios are thus images of how the future might
unfold, and thus an appropriate tool for analyzing how driving forces may influence future emissions and in
assessing the associated uncertainties.
Figure 1. IMAGE 2.4 modelling Framework
A short description of some of the sub models of IMAGE 2.4 modelling Framework:
Demography: the model PHOENIX is a tool to assess future changes (simulation period is 1950-2100) in the
population size and structure in relation to the socio-economic conditions and state of the environment for 25
world regions of IMAGE 2.4.
The Agricultural Economy Model (AEM) computes the regional demand for food and feed crops and timber.
Production required is determined by the sum of domestic regional demand and net trade.
8

Energy Supply and Demand: The IMAGE Energy Regional Model (TIMER) is a global energy model. Its
main objective is to analyze the long-term trends in energy demand and efficiency and the possible transition
towards renewable energy sources.
Land Use Emissions: The Land-Use Emissions Model (LUEM) computes the emissions of gaseous pollutants
(greenhouse gases, gas species involved in ozone chemistry, aerosol formation and acidifying compounds),
stemming from natural and land-use related sources
Carbon, Nitrogen and Water Cycle: In the carbon, nitrogen and water cycle component, IMAGE 2.4 includes
models for describing the global carbon cycle (Terrestrial Carbon Model) and the global nitrogen and phosphate
cycle.
Climate impacts: The impacts of global warming on the global sea level are calculated by the Sea-Level Rise
Model (SLRM), which has not been changed in IMAGE 2.4. In this model the total sea-level rise is influenced
by thermal expansion of the oceans and by changing the net mass balance of glaciers and ice sheets. The most
important ice sheets of Greenland and Antarctica are taken into account in SLRM
2.2.2. Ensemble of models of the TOCSIN project
TOCSIN — Technology-Oriented Cooperation and Strategies in India and China: Reinforcing the EU dialogue
with Developing Countries on Climate Change Mitigation, http://tocsin.ordecsys.com/
The FP6 TOCSIN project has evaluated climate change mitigation options in China and India and the conditions
for strategic cooperation on research, development and demonstration (RD&D) and technology transfer with the
European Union. In particular, the project investigated the strategic dimensions of RD&D cooperation and the
challenge of creating incentives to encourage the participation of developing countries in post-2012 GHG
emissions reduction strategies and technological cooperation.
The research is structured around the use of an ensemble of models that are coupled together via advanced large
scale mathematical programming techniques:
(1) World and regional (i.e. China and India) MARKAL/TIMES bottom-up techno-economic models permitting
a global assessment of technology options in different regions of the world. A full documentation on the TIMES
model‘s generic equations, variables, and parameters, as well as its economic significance, is available from
www.etsap.org
(2) GEMINI-E3 is the name of a top down Computable General Equilibrium Model developed jointly by the
French Ministry of Equipment and the French Atomic Energy Agency in collaboration with the Swiss Federal
Institute of Technology (Lausanne) 1 .
GEMINI-E3 is currently a family of general equilibrium models, all of them multi sector and dynamic, but some
multi-country and some purely domestic or aimed at domestic policy assessment purposes. The original version
of the multi-country model is fully described in Bernard (1998). Several successive versions have been
developed, with an increasing number of countries/regions (from 3 to 28) and an increasing number of sectors
(from 8 to 18). A more detailed representation of countries and sectors was required by new types of appraisal,
from very global ones such as the Kyoto Protocol to more precise ones such as the European Trading System
implemented from the start of 2005. GEMINI-E3 as used for this project includes a representation of developing
countries‘ economies (i.e. China and India) permitting an assessment of welfare, terms of trade and emissions
trading effects; As for numerical specification and resolution, the present version of GEMINI-E3 (and GEMINI-
EMU) is formulated as a mixed complementarity problem using GAMS with the PATH solver.
(3) The World Induced Technical Change Hybrid WITCH model 2 is an energy-economy-climate model
developed by the climate change group at FEEM. WITCH is representing the effect on economic growth of
technology competition in a global climate change mitigation context.
The model has been used extensively for the analysis of the economics of climate change policies.
WITCH is an economic model with a specific representation of the energy sector, thus belonging to the new
class of fully integrated (hard link) hybrid models. It is a global model, divided into 12 macro-regions. For the
present analysis the distinguishing features of the model are two.
The first one is the representation of endogenous technical change in the energy sector. Advancements in carbon
mitigation technologies are described by both diffusion and innovation processes. Learning by Doing and by
Researching allows devising the optimal investment strategies in technologies and R&D in response to given
1 GEMINI-E3 France (Bernard (1999a) ,Bernard (1999b)), GEMINI-E3 Switzerland (Bernard
et alii (2005)}, GEMINI-E3 Tunisia, (Besma (2006)).
2
See www.feem-web.it/witch for a list of applications and papers
9

climate policies. Moreover, knowledge in a country does not depend solely on R&D investments in that country
but it is partially affected by other countries‘ R&D investments, via an international spillovers mechanism. The
second relevant modeling feature is the game-theoretic set up. The model is able to produce two different
solutions, one assuming countries fully cooperate on global externalities, the so called globally optimal solution.
The second is a decentralized solution that is strategically optimal for each given region in response to all other
regions choice, the definition of Nash equilibrium. This modeling features allows to account for externalities due
to all global public goods (CO2, international knowledge spillovers, exhaustible resources etc.), making possible
to model free riding incentives.
3. A review of widely used energy-environmental modelling tools
Table 1 Computer Models to be presented in details
Model Organisation (link) Availability
LEAP
Stockholm Environment Institute
(http://www.energycommunity.org/)
Commercial/free for
developing countries
and students
EnergyPLAN Aalborg University (http://www.energyplan.eu/) Free to Download
MARKAL/TIMES
Energy Technology Systems Analysis Program, International
Energy Agency (http://www.etsap.org/)
Commercial
MESSAGE
International Institute for Applied Systems Analysis
(http://www.iiasa.ac.at/)
Free/Simulators must be
purchased
IKARUS
INFORSE
Mesap PlaNet
Research Centre Jülich, Institute of Energy Research
(http://www.fz-juelich.de/ief/ief-ste/index.php?index=3)
The International Network for Sustainable Energy
(http://www.inforse.org/europe/Vision2050.htm)
seven2one
(http://www.seven2one.de/de/technologie/mesap.html)
Commercial/Earlier
versions are free
Distributed to nongovernmental
organisations
Commercial
PRIMES and
GEM-E3
National Technical University of Athens
(http://www.e3mlab.ntua.gr/)
Projects completed for a
fee
BALMOREL
Project Driven with a users network and forum around it
(http://www.balmorel.com)
Free to Download (open
source)
ENPEP-
BALANCE
Argonne National Laboratory. Energy and Power Evaluation
Program.

Development) based users. Currently LEAP has over 5000 users in 169 countries and to use LEAP typically
requires three or four days of training (online training is available in English). www.energycommunity.org
LEAP is a comprehensive integrated scenario-based energy-environment modeling tool. Its scenarios account for
how energy is consumed, converted and produced in a given energy system under a range of alternative
assumptions on population, economic development, technology, price and so on. It is notable for its flexibility,
transparency and user-friendliness. LEAP is primarily an accounting system but users can also build
econometric and simulation-based models. The user can mix and match these methodologies as required in a
given analysis. For example, a user might create top-down projections of energy demand in one sector based on
a few macroeconomic indicators (price, GDP), while creating a detailed bottom-up forecast based on an end-use
analysis in other sectors. LEAP supports both final and useful energy demand analyses as well as detailed stockturnover
modelling for transportation and other analyses. On the supply side LEAP supports a range of
simulation methods for modelling both capacity expansion and plant dispatch. LEAP includes a built-in
Technology and Environmental Database (TED) containing data on the costs, performance and emission factors
for over 1000 energy technologies. LEAP can be used to calculate the emissions profiles and can also be used to
create scenarios of non-energy sector emissions and sinks (e.g. from cement production, land-use change, solid
waste, etc.).
LEAP includes features designed to make creating scenarios, managing and documenting data and assumptions
and viewing results reports as easy and flexible as possible. For example, LEAP's main data structure is
intuitively displayed as a hierarchical "tree" which be edited by dragging and dropping or copying and pasting
branches. Standard energy balance tables and Reference Energy System (RES) diagrams are automatically
generated and kept synchronized as the user edits the tree. The Results View is an extremely powerful report
generator capable of generating thousands of reports as charts or tables. LEAP is designed to work closely with
Microsoft Office products (Word, Excel, PowerPoint) making it easy to import, export and link to data and
models created elsewhere.
A list of 34 reports involving LEAP can be obtained from [2]. In addition, LEAP has been used for over 70 peerreviewed
journal papers including, an analysis of the potential reductions in energy demand and GHG emissions
within road transport in China [3], identifying the feasible penetration of sustainable energy on the Greek island
of Crete [4], and an investigation into the benefits of improved building energy-efficiencies in China [5]. A
recent EU relation publication is the study for the 27 EU countries named ―Europe‘s Share of the Climate
Challenge: Domestic Actions and International Obligations to Protect the Planet‖ 3
LEAP implementations Costs:
IHS will require a paid license - cost US$ 3.000 (EUR 2.200) for 2 years.
All of the other organizations (assuming they are not-for-profit organizations) qualify for free licenses.
The national "starter" data sets for LEAP for our partners will be provided cost free.
The costs involved in the backstopping technical support: an initial guess would be to budget for at least 5 days
of time of one of our more junior staff. This would cost 5 x US$500 = US $2.500 (EUR 1.820).
The costs involved in the one week training on LEAP, say in Vienna: Assuming this was undertaken by the
senior associate Charlie Heap the costs would be his time (5 x US$1100) = $5.500 (EUR 4.000) plus travel and
accommodation expenses. So roughly US$ 8.000 (EUR 5.850). If there were more than 10 trainees then we
would need a second trainer (not necessarily as expensive as me). This assumes that IHS would provide the
venue and all training equipment
3.2. EnergyPLAN
EnergyPLAN has been developed and expanded on a continuous basis since 1999 at Aalborg University,
Denmark [90]. Approximately ten versions of EnergyPLAN have been created and it has been downloaded by
more than 1200 people. The current version can be downloaded for free from [22] while the training period
required can take a few days up to a month, depending on the level of complexity required.
EnergyPLAN is a user-friendly tool designed in a series of tab sheets and programmed in Delphi Pascal. The
main purpose of the tool is to assist the design of national or regional energy planning strategies by simulating
the entire energy-system: this includes heat and electricity supplies as well as the transport and industrial sectors.
All thermal, renewable, storage/conversion, transport, and costs (with the option of additional costs) can be
modelled by EnergyPLAN. It is a deterministic input/output tool and general inputs are demands, renewable
energy sources, energy station capacities, costs, and a number of different regulation strategies for import/export
3
Heaps Ch., Erickson P., Kartha S., Kemp-Benedict E.,(2009), Europe‘s Share of the Climate Challenge: Domestic Actions
and International Obligations to Protect the Planet, Stockholm Environment Institute
11

and excess electricity production. Outputs are energy balances and resulting annual productions, fuel
consumption, import/export of electricity, and total costs including income from the exchange of electricity. In
the programming, any procedures which would increase the calculation time have been avoided, and the
computation of 1 year requires only a few seconds on a normal computer. Finally, EnergyPLAN optimises the
operation of a given system as opposed to tools which optimise investments in the system.
Previously, EnergyPLAN has been used to analyse the large-scale integration of wind [6] as well as optimal
combinations of renewable energy sources [7], management of surplus electricity [8], the integration of wind
power using Vehicle-to-Grid electric-vehicles [9], the implementation of small-scale CHP [10], integrated
systems and local energy markets [11], renewable energy strategies for sustainable development [12], the use of
waste for energy purposes [13], the potential of fuel cells and electrolysers in future energy-systems [14,15], the
potential of thermoelectric generation (TEG) in thermal energy-systems [16], and the effect of energy storage
[17], with specific work on compressed-air energy storage [18,19] and thermal energy storage [6,7, 25]. In
addition, EnergyPLAN was used to analyse the potential of CHP and renewable energy in Estonia, Germany,
Poland, Spain, and the UK [26]. Other publications can be seen on the EnergyPLAN website [19], while an
overview of the work completed using EnergyPLAN is available in [20]. Finally, EnergyPLAN has been used to
simulate a 100% renewable energy-system for the island of Mljet in Croatia [21] as well as the countries of
Ireland [106] and Denmark [22, 27].
3.3. MARKAL/TIMES
MARKAL (MARket ALlocation) is a technology-rich energy/economic/environmental model. It was developed
in a collaborative effort under the auspices of the International Energy Agency Energy Technology Systems
Analysis Programme (ETSAP). MARKAL is a generic model tailored by the input data to represent the
evolution over a period of usually 20 to 50 years of a specific energy-environment system at the national,
regional, state or province, or community level. The system is represented as a network, depicting all possible
flows of energy from resource extraction, through energy transformation and end-use devices, to demand for
useful energy services. Each link in the network is characterized by a set of technical coefficients (e.g., capacity,
efficiency), environmental emission coefficients (e.g., CO2, SOx, NOx), and economic coefficients (e.g., capital
costs, date of commercialization). Many such energy networks or Reference Energy Systems (RES) are feasible
for each time period. MARKAL finds the best RES for each time period by selecting the set of options that
minimizes total system cost over the entire planning horizon. TIMES (The Integrated MARKAL-EFOM
System) builds on the best features of MARKAL and the Energy Flow Optimization Model (EFOM). In order to
work with MARKAL, you need a number of software elements: MARKAL itself, a user-interface (two are
available for Windows: ANSWER and VEDA), GAMS (a high-level modeling system for mathematical
programming problems) and an optimizing solver such as MINOS, CPLEX or OSL.
Figure 2. ETSAP developed and coded two model generators - MARKAL and TIMES - and two data
management systems: ANSWER and VEDA
A number of variations of MARKAL are available including
MARKAL-MACRO: which links MARKAL with a macroeconomic model to provide demands that are
endogenous and responsive to price, and estimates of GDP impact and feedbacks.
STOCHASTIC, which associates probabilities with the occurrence of each scenario, allowing hedging
strategies to be determined that identify robust rather than purely optimal strategies.
12

GOAL PROGRAMMING: which solves MARKAL according to the weighted preferences of various
stakeholders with respect to cost versus environmental goals.
The MARKAL/TIMES (www.etsap.org) tools have been used for countless studies [36], which include an
investigation into the future prospects of hydrogen and fuel cells [37-39], as well as hydrogen vehicles [40,41],
examinations into the future role of nuclear power [42] and nuclear fusion [43-45], and the impacts of wind
power on the future use of fuels [46]. Also, MARKAL/TIMES has been used to simulate European Commission
integrated policies on the use of renewable sources, climate change mitigation and energy efficiency
improvement, the so called 20–20–20 targets, and far more stringent targets in the longer term at the national and
pan EU level [47].
At the moment, MARKAL/TIMES is used in 70 countries by 250 institutions (of which 75% are active users).
The source code is distributed free-of-charge by signing a Letter of Agreement. However, the code is written in
GAMS, which is a commercial language and therefore has to be purchased. In addition, both an interface and a
solver must also be purchased to use the source code effectively: as a result the total cost is approximately
US$1780–US$4420 (€1275– €3170) for an educational license and approximately US$13,700– US$21,200
(€9825–15,200) for a commercial license [35]. The most demanding part of MARKAL/TIMES is training which
takes some months.
MARKAL/TIMES applications by our consortium members:
a. In the Republic of Moldova for Energy Efficiency and RES Analysis; Presented at the ETSAP
Workshop 2010; The model was developed in the framework of the Synenergy Project Funded by
USAID- Hellenic Aid; Developers: Sergiu Robu, Institute of Power Engineering, Academy of Sciences
of Moldova; www.ie.asm.md; sergiu.robu@asm.md; Philip Siakkis CRES -Centre for Renewable
Energy Sources, Greece, fsiakkis@cres.gr ; Dr. George Giannakidis, CRES -Centre for Renewable
Energy Sources, Greece, www.cres.gr ggian@cres.gr ;
b. MARKAL applications in Turkey: (1) Establishing energy efficiency and mitigation strategies in
Turkey by Egemen Sulukan, Mustafa Saglam, (2) Analysing Alternative Scenarios on TURKISH
MARKAL Model by Tanay S. Uyar, Egemen Sulukan, Mustafa Salam, both teams from Uni-Marmara,
Istanbul
3.4. MESSAGE
MESSAGE (Model for Energy Supply Strategy Alternatives and their General Environmental Impact) has
been developed by the International Institute for Applied Systems Analysis (IIASA) in Austria since the 1980s
[48,49]. Depending on the scope and research question, various different versions of MESSAGE have been
created with several hundred users. It is free for academic purposes, and a special agreement between the IIASA
and IAEA (International Atomic Energy Agency) permits its use within the IAEA and its member states. The
latter has facilitated a number of in-depth training courses for energy experts in the IAEA member countries:
usually taking approximately 2 weeks of training to be able complete basic applications.
MESSAGE is a systems engineering optimisation tool used for the planning of medium to long-term energysystems,
analysing climate change policies, and developing scenarios for national or global regions. The tool
uses a 5 or 10 year time-step to simulate a maximum of 120 years. All thermal generation, renewable,
storage/conversion, transport technologies, and costs (including SO2 and NOX costs) can be simulated by
MESSAGE as well as carbon sequestration. The tool‘s principal results are the estimation of global and regional
multi-sector mitigation strategies instead of climate targets. MESSAGE determines cost-effective portfolios of
GHG emission limitation and reduction measures. It has recently been extended to cover the full suite of GHGs
and other radiative substances, for the development of multi-gas scenarios that try to stabilise future CO2-
equivalent concentrations [50].
MESSAGE has previously been used to develop global energy transition pathways for the World Energy
Council [51] and GHG emission scenarios for the Intergovernmental Panel on Climate Change [52]. Other
studies include scenario assessment with a focus on climate stabilization [53, 54], national studies of innovation
programs on the Iranian electricity sector [55], policy options for increasing the use of renewable energy [56],
energy supply options in the Baltic States [57], and designing a sustainable energy plan for Cuba [58].
MESSAGE has been used to simulate renewable-energy penetrations of 70% in the electricity sector, 60% in the
heat sector, and 55% in the transport sector, in the GGI B1 scenario of [54] (all the quantitative data for this
study is available at [59]).
3.5. IKARUS
IKARUS is a dynamic bottom-up linear cost-optimisation scenario tool for national energy-systems, which is
maintained by the Institute of Energy Research at Jülich Research Centre, Germany [60]. To date 20 versions
13

have been released, but the current version is not commercially available. However, earlier versions are sold for
approximately €250 and to use IKARUS requires at least three months of training.
A time-step of five years is used by IKARUS and each one is optimised by itself using the heritage from all
periods before. The tool can simulate a timeframe of approximately 40 years (usually up to 2050). Unlike
perfect-foresight tools, IKARUS does not take into account future changes in each time-step during the
optimisation to provide a realistic character of prognosis and projection. Therefore, aspects like reaction to
sudden changes (e.g. of energy prices), flexibility of technical scenarios, lost opportunities, etc., can be
examined. Interactions with macroeconomic input/output tools, dependencies on elasticities, and technological
learning are also possible. The objective is normally to reduce total system costs, but numerous other objectives
can be specified such as emissions reductions. IKARUS simulates all sectors of the energy-system and almost all
generation, storage/conversion, and transport technologies: the only technologies not considered are wave, tidal,
compressed-air energy storage, and intelligent battery-electric vehicles.
Some investigations that IKARUS has contributed to are an investigation into the role of carbon capture and
storage (CCS) in reducing carbon emissions [61], the effects of stochastic energy prices on long-term energy
scenarios [62], the introduction of fuzzy constraints to provide a better representation of political decisionmaking
processes in the energy economy and energy policy [63], and the implications of high energy prices [64].
3.6. INFORSE
INFORSE (International Network for Sustainable Energy) is an energy balancing tool for national energysystems
developed in 2002 by the network [64]. It is currently not for sale to external users, but instead is
distributed to non-governmental organisations (NGOs). To use INFORSE requires 2–4 weeks of training.
The tool consists of linked spreadsheets which are used to input the details of the energy-system being
modelled. These include details about energy production, energy demand, energy trends, and energy policies. All
thermal generation, renewable generation, and hydrogen-based storage/conversion devices except tidal power are
available. The transport technologies included are conventional, battery-electric, and hydrogen vehicles as well
as rail. The results from INFORSE give an overview for the possible energy development in a country or region,
by providing an energy balance for every decade simulated over a maximum timeframe of 100 years. This
illustrates the potential use of renewable energy and identifies the trends in energy efficiency, energy services,
and energy policies entered into INFORSE. The costs in INFORSE include an overall energy cost and CO2
costs.
INFORSE has been used to simulate the potential utilisation of renewable energy by 2050 for a number of
countries including Belarus, Bulgaria, Denmark, Latvia, Lithuania, Romania, Russia, Slovakia, Ukraine, and the
UK, as well as simulating a 100% renewable energy-system for Denmark by 2030. These studies can be
accessed via the INFORSE homepage [64].
3.7. Mesap PlaNet
Mesap (Modular Energy-System Analysis and Planning Environment) is an energy-system analysis toolbox,
and PlaNet (Planning Network) is a linear network module for Mesap. It was originally developed by the
Institute for Energy Economics and the Rational Use of Energy (IER) at the University of Stuttgart in 1997 [65–
67], but it is now maintained by the German company Seven2one Informationssysteme GmbH [68]. In total 15
versions of Mesap Pla-Net has been released and it has approximately 20 users. To purchase Mesap PlaNet costs
at least €11,500, but there is a discount for research groups. It takes 5 days of training to learn how to use Mesap
PlaNet.
Mesap PlaNet is designed to analyse and simulate energy supply, demand, costs, and environmental impacts
for local, regional, national, and global energy-systems. A detailed cost calculation determines the specific
production cost of all commodities in the reference energy-system, based on the investment, fixed O&M, and
variable O&M costs. The tool uses a technology-oriented modelling approach, where several competitive
technologies that supply energy services are represented by parallel processes. All thermal generation,
renewable, storage/conversion, and transport technologies are considered in the simulation. The simulation is
carried out in a user-specified time-step which ranges from 1 min to multiple years, while the total time-period is
unlimited.
Mesap PlaNet has previously been used to simulate global energy supply strategies [69,70] and to compare
energy-efficiency strategies in Slovenia [71]. It has also simulated a 100% renewable energy-system [70].
3.8. PRIMES and GEM-E3 Models
PRIMES 4 simulates a market equilibrium solution for energy supply and demand [71]. It has been developed by
4 http://www.e3mlab.ntua.gr/e3mlab/PRIMES%20Manual/The_PRIMES_MODEL_2010.pdf
14

the National Technical University of Athens (NTUA) since 1994, but it is not sold to third parties. Instead, the
tool is used within consultancy projects undertaken by NTUA and partners.
The equilibrium used in PRIMES is static (within each time period) but repeated in a time-forward path,
under dynamic relationships. All thermal, renewable, storage/conversion, and transport technologies can be
simulated except battery energy storage, compressed-air energy storage, intelligent battery-electric-vehicles, and
hybrid vehicles. PRIMES is organised in sub-tools, each one representing the behaviour of a specific ‗demander‘
and/or a ‗supplier‘ of energy. The tool can support policy analysis in the following fields: (1) standard energy
policy issues: security of supply, strategy, costs (includes all costs), etc., (2) environmental issues, (3) Pricing
policy and taxation, standards on technologies, (4) new technologies and renewable sources, (5) energy
efficiency in the demand-side, (6) alternative fuels, (7) conversion to decentralisation and electricity-market
liberalisation, (8) policy issues regarding electricity generation, gas distribution, and new energy forms. PRIMES
is organised by an energy production sub-system for supply consisting of oil products, natural gas, coal,
electricity and heat production, biomass supply, and others, and by end-use sectors for demand consisting of
residential, commercial, transport, and nine industrial sectors. Some demanders may also be suppliers, as for
example industrial co-generators of electricity and steam.
PRIMES has previously been used to create energy outlooks for the EU [71], develop a climate change action
and renewable energy policy package for the EU [73] and also, to analyse a number of different policies to
reduce GHG in the EU25 by 2030 [74, 75]. Finally, PRIMES has been used for several EU governments as well
as private companies.
NTUA has also developed the top down GEM-E3 Model 5 : The GEM-E3 (World and Europe versions) model is
an applied general equilibrium model, simultaneously representing 37 World regions/24 European countries,
which provides details on the macro-economy and its interaction with the environment and the energy system. It
covers all production sectors (aggregated to 26) and institutional agents of the economy. It is an empirical, largescale
model, written entirely in structural form. The model computes the equilibrium prices of goods, services,
labor and capital that simultaneously clear all markets under the Walras law and determines the optimum balance
for energy demand/supply and emission/abatement.
3.9. BALMOREL
BALMOREL is a partial-equilibrium model with an emphasis on the electricity sector and CHP. It is developed,
maintained, and distributed under open source ideals since 2000, and can be freely downloaded from [76]. The
tool is formulated in the GAMS modelling language [77] and approximately 10 different versions have been
created (the number of users is not monitored). In addition to providing 100% documentation at code level, any
user can modify the tool to suit specific requirements for a given application. The formulated model is solved in
standard software so no new optimisation code needs to be written. To run a typical analysis using BALMOREL,
one week of training is necessary.
Input data and calculation results are given in relation to a geographical subdivision. Time aspects are treated
flexibly in relation to how many years are represented, and how many subdivisions of time are within the each
year. Typical choices are 250 time segments per year over a 20 year time-horizon, or 8760 time segments per
year over 1 year, depending on the purpose of the study. BALMOREL can simulate the electricity sector and
some of the heat sector (district heating), but not the transport sector (transport technologies are not represented
as standard, but some projects [78] have developed transport sector modules). The different types of units
include electricity, district heating, CHP, short-term heat storages, hydro power, wind, and solar. Electricity
storage can also be represented by hydrogen storage or pumped hydroelectric. Electricity transmission is
described in relation to a number of nodes that are connected by transmission lines and allows for the
identification of bottlenecks in the transmission system. In relation to generation capacity, the tool may invest
optimally in electricity and CHP technologies. The investments respect specified restrictions e.g., in relation to
maximum investment addition per year, or maximum fuel available. Also, BALMOREL considers all costs
within the energy-system as well as SO2 and NOX penalties.
BALMOREL has been applied to projects in Denmark [79–81], Norway [82], Estonia [82], Latvia [83],
Lithuania [84], Germany [94], and countries outside of Europe [85]. It has been used to analyse security of
electricity supply [86,87], the role of demand response [79], wind power development [81,85], the role of natural
gas [80], development of international electricity markets [88], market power [89], investigate the expansion of
district heating in Copenhagen (an on-going project) [90], the expansion of electricity transmission [82],
international markets for green certificates and emission trading as well as environmental policy evaluation [91],
unit commitment [55–57], compressed-air energy storage [68], and learning curves [69]. To date the highest
renewable penetrations simulated by BALMOREL are 50% in the electricity sector [81] and 10% in the transport
sector [78].
5 http://www.e3mlab.ntua.gr/e3mlab/GEM%20-%20E3%20Manual/Manual%20of%20GEM-E3.pdf
15

3.10. ENPEP-BALANCE
The non-linear, equilibrium ENPEP-BALANCE tool matches the demand for energy with available resources
and technologies. It was developed by Argonne National Laboratory in the USA in 1999 and it is used in over 50
countries, but the exact number of users is not known.
ENPEP-BALANCE can be downloaded for free from [95], and it takes approximately one week of training for
basic applications or two weeks of training for advanced applications. ENPEP-BALANCE uses a market-based
simulation approach to determine the response of various segments of the energy-system to changes in energy
prices and demand levels. The analysis is carried out on an annual basis for up to a maximum of 75 years, and
typically on national energy-systems. The tool relies on a decentralized decision-making process in the energy
sector and basic input parameters include information on the entire energy-system structure.
All thermal and renewable generation can be simulated, but the only storage/conversion technology accounted
for is hydrogen production. Also, all financial aspects are considered as well as the option of adding additional
costs.
ENPEP-BALANCE simultaneously finds the intersection of supply and demand curves for all energy supply
forms and all energy uses included in the energy network. Equilibrium is reached when ENPEP-BALANCE
finds a set of market clearing prices and quantities that satisfy all relevant equations and inequalities. The tool
employs the Jacobi iterative technique to find the solution that is within a user-defined convergence tolerance.
Some of the case studies which ENPEP-BALANCE has been used for include analyzing Mexico‘s future energy
needs and estimating the associated environmental burdens [96], developing green- house-gas (GHG) emissions
projections for Turkey [97], and a GHG mitigation analysis for Bulgaria [98].
A full range of other publications that ENPEP-BALANCE participated in is available at [99].
Finally, ENPEP-BALANCE has been used to simulate nearly 20% of the electricity production from renewable
energy sources within an energy-system [100].
ENPEP Applications according to
The Energy and Power Evaluation Program (ENPEP) is used by staff members of the Center for Energy,
Environmental, and Economic Systems Analysis (CEEESA), energy and environmental ministries, lending
agencies, research institutes, and energy regulatory commissions around the world. Model applications cover the
entire spectrum of issues found in today‘s complex energy markets:
Energy policy analysis
Energy market projections
Natural gas market analysis
Carbon emissions projections
Projections of criteria pollutants (SO2, NOX, etc.)
Carbon mitigation studies
Increasingly, model applications focus on climate-change–related issues. ENPEP climate change study reports
can be downloaded at various web sites, including the United Nations Framework Convention on Climate
Change (UNFCCC) and the U.S. Environmental Protection Agency (EPA).
3.11. MERCI-ATHDM E3: The Austrian Hybrid Dynamic Model E3
For the MERCI or Austrian Hybrid Dynamic Model E3 ( ATHDM E3), where E3 stands for energy-economyecology
the Hybrid Dynamic Model for Top Down – Bottom Up Ramsey type dynamic general equilibrium
model (that is allowing for systematic trade-off analysis of environmental quality, economic performance and
welfare.
As to policy measures related also to mitigation of climate change impacts by promotion of renewable energies
there has been a shift - as more generally in environmental policy design - from command-and-control policies
to market-based instruments such as taxes, subsidies, and tradable quotas where a recent impact assessment by
the European Commission, 2008, has shown that feed-in tariffs are the preferred promotion measure, in addition,
to direct subsidies for renewable energy– typically differentiated by the type of green energy, i.e., wind, biomass,
solar cells, etc.
Methodologically the focus is set on the CGE (Computational General Equilibrium) modeling approach. The
usual approach in modeling environmental and climate related issues is the use of Top Down models in
analyzing the options of development with respect to the overall economy, as well as the use of Bottom Up
models for the analysis of technological processes, e.g. on the energy system level.
16

The combination of a Bottom Up and a Top Down part in the modeling framework for Austria, that deals with
short to medium term targets (EU 20-20-20 targets, Austrian Energy Strategy) ant the ambition to create long
term scenarios until 2050 connects newest results in applied CGE modeling and research in climate change
studies. The presented modeling approach is basically oriented at the works of Prof. Christoph Boehringer and
Prof. Thomas Rutherford 67 , who propose a combination of Bottom Up and Top Down models in a common
framework.
4. A brief classification of energy models
Many energy-environmental models are in current use around the world, each designed to emphasize a particular
facet of interest. Differences include: economic rationale, level of disaggregation of the variables, time horizon
over which decisions are made (and which is closely related to the type of decisions, i.e. only operational
planning or also investment decisions), and geographic scope. One of the most significant differentiating features
among energy-environmental models is the degree of detail with which commodities and technologies are
represented, which will guide our classification of models in two major classes.
4.1.1. ‘Top-Down’ Models
At one end of the spectrum are aggregated General Equilibrium (GE) models. In these each sector is represented
by a production function designed to simulate the potential substitutions between the main factors of production
(also highly aggregated into a few variables such as: energy, capital, and labour) in the production of each
sector‘s output. In this model category are found a number of models of national or global energy systems.
These models are usually called ―Top-Down‖, because they represent an entire economy via a relatively small
number of aggregate variables and equations. In these models, production function parameters are calculated for
each sector such that inputs and outputs reproduce a single base historical year 8 . In policy runs, the mix of
inputs 9 required to produce one unit of a sector‘s output is allowed to vary according to user-selected elasticities
of substitution. Sectoral production functions most typically have the following general form:
where
X S is the output of sector S,
K S , L S , and E S are the inputs of capital, labour and energy needed to produce one unit of output in sector
S,
ρ is the elasticity of substitution parameter,
A 0 and the B‘s are scaling coefficients.
The choice of ρ determines the ease or difficulty with which one production factor may be substituted for
another: the smaller ρ is (but still greater than or equal to 1), the easier it is to substitute the factors to produce
the same amount of output from sector S. Also note that the degree of factor substitutability does not vary among
the factors of production — the ease with which capital can be substituted for labour is equal to the ease with
which capital can be substituted for energy, while maintaining the same level of output. GE models may also use
alternate forms of production function (3-1), but retain the basic idea of an explicit substitutability of production
factors.
Some of the top-down models mentioned in this review:
1. The Austrian Hybrid Dynamic Model E3 ( ATHDM E3) is a combination of a ―top-down‖ Ramsey type
dynamic general equilibrium model (where E3 stands for energy-economy-ecology) representing the macroeconomy
through a Social Accounting Matrix with 12 producing sectors and where Hybrid means allowing
several aggregated energy technologies (bottom-up) within the same model.
6 Böhringer, C., Rutherford, T.,F.:Combining bottom-up and top-down, Energy Economics volume 30, March 2008, Pages
574-596
7 Rutherford, T. F.: Constant Elasticity of Substitution Functions: Some Hints and Useful Formulae, manuscript, 1995,
University of Colorado
8 These models assume that the relationships (as defined by the form of the production functions as well as the calculated parameters)
between sector level inputs and outputs are in equilibrium in the base year.
9 Most models use inputs such as labor, energy, and capital, but other input factors may conceivably be added, such as arable land, water, or
even technical know-how. Similarly, labour may be further subdivided into several categories.
17

Hence, the hybrid approach permits an energy-economy-ecology model to combine technological details of an
energy system (bottom-up) with a characterization of the overall economy market equilibrium (top-down).
Mathematically ATHDM E3 is formulated as a Mixed Complementarity Problem.
2. GEMINI-E3 is a ―top-down‖ Computable General Equilibrium Model that is multi sector and dynamic, and
can be either multi-country or purely domestic aimed at domestic policy assessment purposes.
3. The MACRO component of the MERGE model is a ―top-down‖ Ramsey type macroeconomic growth model
that balances the non energy part of the economy of a given region using a nested constant-elasticity-of
substitution (CES) production function. The MACRO model also captures autonomous (e.g., price-independent)
effects and macroeconomic feedbacks between the energy sector and the rest of the economy, such as the
impacts of higher energy prices (e.g., resulting from CO2 control) on economic activities.
4.1.2. ‘Bottom-Up’ Models
At the other end of the spectrum are the very detailed, technology explicit models that focus primarily on the
energy sector of an economy. In these models, each important energy-using technology is identified by a detailed
description of its inputs, outputs, unit costs, and several other technical and economic characteristics. In these socalled
‗Bottom-Up‘ models, a sector is constituted by a (usually large) number of logically arranged
technologies, linked together by their inputs and outputs (commodities, which may be energy forms or carriers,
materials, emissions and/or demand services). Some bottom-up models compute a partial equilibrium via
maximization of the total net (consumer and producer) surplus, while others simulate other types of behaviour by
economic agents, as will be discussed below. In bottom-up models, one unit of sectoral output (e.g., a billion
vehicle kilometres, one billion tonnes transported by heavy trucks or one Petajoule of residential cooling service)
is produced using a mix of individual technologies‘ outputs. Thus the production function of a sector is implicitly
constructed, rather than explicitly specified as in more aggregated models. Such implicit production functions
may be quite complex, depending on the complexity of the reference energy system of each sector (sub-RES).
Some examples of ‗Bottom-Up‘ Models:
1. MARKAL/ TIMES bottom-up techno-economic model permitting a global assessment of technology options
in different regions of the world. A full documentation on the TIMES model‘s generic equations, variables, and
parameters, as well as its economic significance, is available from www.etsap.org
2. The TIMER Energy Regional Model as used in the IMAGE modeling system is a global energy model. Its
main objective is to analyze the long-term trends in energy demand and efficiency and the possible transition
towards renewable energy sources.
3. EnergyPLAN is assisting the design of national or regional energy planning strategies by simulating the
entire energy-system: this includes heat and electricity supplies as well as the transport and industrial sectors. All
thermal, renewable, storage/conversion, transport, and costs (with the option of additional costs) can be modelled
by it. It is a deterministic input/output tool and general inputs are demands, renewable energy sources, energy
station capacities, costs, and a number of different regulation strategies for import/export and excess electricity
production. Outputs are energy balances and resulting annual productions, fuel consumption, import/export of
electricity, and total costs including income from the exchange of electricity
4. MESSAGE is a systems engineering optimisation tool used for the planning of medium to long-term energysystems,
analysing climate change policies, and developing scenarios for national or global regions. The tool
uses a 5 or 10 year time-step to simulate a maximum of 120 years. All thermal generation, renewable,
storage/conversion, transport technologies, and costs (including SO2 and NOX costs) can be simulated by
MESSAGE as well as carbon sequestration. The tool‘s principal results are the estimation of global and regional
multi-sector mitigation strategies instead of climate targets. MESSAGE determines cost-effective portfolios of
GHG emission limitation and reduction measures. It has recently been extended to cover the full suite of GHGs
and other radiative substances, for the development of multi-gas scenarios that try to stabilise future CO2-
equivalent concentrations
5. IKARUS is a dynamic bottom-up linear cost-optimisation scenario tool for national energy-systems, which is
maintained by the Institute of Energy Research at Jülich Research Centre, Germany. A time-step of 5 years is
used by IKARUS and each one is optimised by itself using the heritage from all periods before. The tool can
simulate a timeframe of approximately 40 years (usually up to 2050).
6. INFORSE (International Network for Sustainable Energy) is an energy balancing tool for national energysystems
developed in 2002 by the network. The tool consists of linked spreadsheets which are used to input the
details of the energy-system being modelled. These include details about energy production, energy demand,
energy trends, and energy policies. All thermal generation, renewable generation, and hydrogen-based
18

storage/conversion devices except tidal power are available. The transport technologies included are
conventional, battery-electric, and hydrogen vehicles as well as rail. The results from INFORSE give an
overview for the possible energy development in a country or region, by providing an energy balance for every
decade simulated over a maximum timeframe of 100 years. This illustrates the potential use of renewable energy
and identifies the trends in energy efficiency, energy services, and energy policies entered into INFORSE.
7. MESAP (Modular Energy-System Analysis and Planning Environment) is an energy-system analysis toolbox,
and PlaNet (Planning Network) is a linear network module for Mesap. Mesap PlaNet is designed to analyse and
simulate energy supply, demand, costs, and environmental impacts for local, regional, national, and global
energy-systems. A detailed cost calculation determines the specific production cost of all commodities in the
reference energy-system, based on the investment, fixed O&M, and variable O&M costs. The tool uses a
technology-oriented modelling approach, where several competitive technologies that supply energy services are
represented by parallel processes. All thermal generation, renewable, storage/conversion, and transport
technologies are considered in the simulation. The simulation is carried out in a user-specified time-step which
ranges from 1 min to multiple years, while the total time-period is unlimited.
8. BALMOREL is a partial-equilibrium model with an emphasis on the electricity sector and CHP.
BALMOREL can simulate the electricity sector and some of the heat sector (district heating), but not the
transport sector (transport technologies are not represented as standard, but some projects have developed
transport sector modules). The different types of units include electricity, district heating, CHP, short-term heat
storages, hydro power, wind, and solar. Electricity storage can also be represented by hydrogen storage or
pumped hydroelectric. Electricity transmission is described in relation to a number of nodes that are connected
by transmission lines and allows for the identification of bottlenecks in the transmission system.
9. ENPEP-BALANCE tool matches the demand for energy with available resources and technologies. ENPEP-
BALANCE uses a market-based simulation approach to determine the response of various segments of the
energy-system to changes in energy prices and demand levels. The analysis is carried out on an annual basis for
up to a maximum of 75 years, and typically on national energy-systems. The tool relies on a decentralized
decision-making process in the energy sector and basic input parameters include information on the entire
energy-system structure. All thermal and renewable generation can be simulated and all financial aspects are
considered as well as the option of adding additional costs. ENPEP-BALANCE simultaneously finds the
intersection of supply and demand curves for all energy supply forms and all energy uses included in the energy
network. Equilibrium is reached when ENPEP-BALANCE finds a set of market clearing prices and quantities
that satisfy all relevant equations and inequalities. The tool employs the Jacobi iterative technique to find the
solution that is within a user-defined convergence tolerance.
4.1.3. Recent Modelling Advances
While the above dichotomy applied fairly well to earlier models, these distinctions now tend to be somewhat
blurred by recent advances in both categories of model. In the case of aggregate top-down models, several
general equilibrium models now include a fair amount of fuel and technology disaggregation in the key energy
producing sectors (for instance: electricity production, oil and gas supply). This is the case with MERCI-
ATHDM E3 and MERGE, for instance. In the other direction, the more advanced bottom-up models are
‗reaching up‘ to capture some of the effects of the entire economy on the energy system. For instance, the
TIMES model has end-use demands (including demands for industrial output) that are sensitive to their own
prices, and thus capture the impact of rising energy prices on economic output and vice versa. Recent
incarnations of technology-rich models are multi-regional, and thus are able to consider the impacts of energyrelated
decisions on trade. It is worth noting that while the multi-regional top-down models have always
represented trade, they have done so with a very limited set of traded commodities – typically one or two,
whereas there may be quite a number of traded energy forms and materials in multi-regional bottom-up models.
Examples for currently developed top down environmental models of the research community are GEM-E3
(General Equilibrium Model for Economy – Energy - Environment) 10 , MERGE (Model for Estimating the
Regional and Global Effects of Greenhouse Gas Reductions) 11 , as well as WITCH (World Induced Technical
Change Hybrid) 12 .
10 See http://www.gem-e3.net/
11 See http://www.stanford.edu/group/MERGE/
12 See http://www.feem.it/getpage.aspx?id=2461&sez=Research&padre=18&sub=75&idsub=102
19

A list of detailed bottom up models for the technological analysis includes amongst others the models
MESSAGE (Model for Energy Supply Strategy Alternatives and their General Environmental Impact) 13 ,
PRIMES Energy System Model 14 and MARKAL (Market Allocation) Model 15 .
An additional example for a complex climate- and environmental model is the GAINS model, developed by
IIASA (International Institute for Applied Systems Analysis), that can be categorized as an „Integrated
Assessment Model―(IAM) 16 . These kinds of models are slightly different, they integrate information on air
emissions as well as their diffusion, effects and mitigation options and costs as well as synergies with other
external parts of the environment.
Many research works on greenhouse gas emissions have been carried out and numerical model scenarios on the
topic have been made on global and European scale IPCC (2007). As examples one could name the project
RECIPE (Report on Energy and Climate Policy in Europe) 17 that focused on the targets of reaching an increase
of only 2°C until 2020 using amongst others the REMIND-R model developed by the Potsdam Institute for
Climate Impact Research.
The TIMES model introduces further enhancements over and above those of MARKAL. In TIMES, the horizon
may be divided into periods of unequal lengths, thus permitting a more flexible modelling of long horizons:
typically, one may adopt short periods in the near-term (the initial period often consists of a single base year),
and longer ones in the out years; TIMES includes both technology related variables (as in MARKAL) and flow
related variables (as in the EFOM model, (van der Voort et. al., 1984), thus allowing the easy creation of more
flexible processes and constraints; the expression of the TIMES objective function (total system cost) tracks the
payments of investments and other costs much more precisely that in other bottom-up models.
In spite of these advances in both classes of models, there remain important differences.
Specifically:
Top-down models encompass macroeconomic variables beyond the energy sector proper, such as
wages, consumption, and interest rates, and
Bottom-up models have a rich representation of the variety of technologies (existing and/or future)
available to meet energy needs, and, they often have the capability to track a wide variety of traded
commodities.
The Top-down vs. Bottom-up approach is not the only relevant difference among energy models. Among Topdown
models, the so-called Computable General Equilibrium models (CGE) described above differ markedly
from the macro econometric models. The latter do not compute equilibrium solutions, but rather simulate the
flows of capital and other monetized quantities between sectors (see e.g. E3MG of the Cambridge Centre for
Climate Change Mitigation Research). They use econometrically derived input-output coefficients to compute
the impacts of these flows on the main sectoral indicators, including economic output (GDP) and other variables
(labour, investments). The sectoral variables are then aggregated into national indicators of consumption, interest
rate, GDP, labour, and wages.
Among technology explicit models also, two main classes are usually distinguished: the first class is that of the
partial equilibrium models such as MARKAL and TIMES, that use optimization techniques to compute a least
cost (or maximum surplus) path for the energy system. The SAGE incarnation of the MARKAL model possesses
a market sharing mechanism that allows it to reproduce certain behavioural characteristics of observed markets.
The second class is that of simulation models, where the emphasis is on representing a system not governed
purely by financial costs and profits. One of these simulation models is LEAP the Long-range Energy
Alternatives Planning system.
LEAP is a comprehensive integrated scenario-based energy-environment modelling tool. Its scenarios account
for how energy is consumed, converted and produced in a given energy system under a range of alternative
assumptions on population, economic development, technology, price and so on. LEAP is primarily an
accounting system but users can also build econometric and simulation-based models as well. The user can mix
and match these methodologies as required in a given analysis. For example, a user might create top-down
projections of energy demand in one sector based on a few macroeconomic indicators (price, GDP), while
13 See http://www.iiasa.ac.at/Research/ECS/docs/models.html#MESSAGE
14 Prof. P. Capros, The PRIMES Energy System Model, Institute of Communication and Computer Systems of the National
Technical University of Athens (ICCS-NTUA), E3M-Lab, 1995
15 See http://www.etsap.org/Tools/MARKAL.htm
16 Forinformation on GAINS model see GAINS EUROPE, Greenhouse Gas - Air Pollution Interactions and Synergies,
http://gains.iiasa.ac.at/gains/docu.EUR/index.menu, 2010
17 Luderer, G. et.al.: Report on Energy and Climate Policy in Europe (RECIPE), the economics of decarbonization, Potsdam,
2009. http://www.pik-potsdam.de/recipe
20

creating a detailed bottom-up forecast based on an end-use analysis in other sectors. LEAP supports both final
and useful energy demand analyses as well as detailed stock-turnover modelling for transportation and other
analyses. On the supply side LEAP supports a range of simulation and optimisation methods for modelling both
capacity expansion and plant dispatch. LEAP includes a built-in Technology and Environmental Database
(TED) containing data on the costs, performance and emission factors for over 1000 energy technologies. LEAP
can be used to calculate the emissions profiles and can also be used to create scenarios of non-energy sector
emissions and sinks (e.g. from cement production, land-use change, solid waste, etc.).
5. Selection of an integrated modelling tool
After a preliminary screening of the model presented in the Table 1, namely, LEAP , EnergyPLAN,
MARKAL/TIMES, MESSAGE, IKARUS, INFORSE, Mesap PlaNet, PRIMES and GEM-E3, BALMORE, L
ENPEP-BALANCE and MERCI-AUHDM E3 with intention of identifying the integrated modeling tools
allowing for analysis of Adaptation and Mitigation scenarios and identification of the respective portfolios two
models seems to deserve more carefully comparisons on the basis of the eight criteria defined by the
PROMITHEAS-4 working group on 10 th of March 2011.
These are the LEAP and the MARKA/TIMES integrated energy-environmental modelling tools.
In Table 2 the detailed reflection on the selected criteria is presented and, to my mind, the ranking is quite clear
– the LEAP model seems to be the only affordable one in terms of suitability to our goals, availability of data,
and the compliance with PROMITHEAS 4 objective s, as well as financing and training.
Certainly we should keep in mind that this is the first attempt for models evaluation and it may be well possible
that some models have been left out of our attention.
To that end comments and suggestions are more than welcome.
21

Table 2. Comparison oft he LEAP and MARKAL/TIME according the criteria defined by the PROMITHEAS-4 working group 10 th of March 2011
Model Organisation (link) Covering A/M
LEAP
MARKAL
/
TIMES
Stockholm Environment Institute
(http://www.energycommunity.org/)
ETSAP, IEA
(http://www.etsap.org/)
Yes
For Adaptation
macroeconomic
indicators
(price, GDP,
etc.)
Energy and
GHG
mitigation
study for the
EU27
Yes
MARKAL-
MACRO:
provides for
endogenous
and price
responsive
demands e, and
estimates of
GDP impact
and feedbacks;
Allows certain
behavioural
characteristics
of observed
markets to be
reproduced
Transparency, complexity, easy
to use
Notable for its flexibility,
transparency and userfriendliness
technology-rich
energy/economic/environmental
model that requires long
preparatory work
Availability of the model
and the Data
Paid license for
EU27/free for
developing countries;
Total cost to the project
€ 5.500
Provides national
"starter" data sets
Includes a built-in
Technology and
Environmental Database
(TED) for over
1000 energy
technologies.
cost per user for
educational license:
€1.300– €3.200
Economic/Energy/Enviro
nmental Data base
correspond to the EU
statistical standards
Compliance of
outputs with projects
objectives
Final and useful
energy demand
analyses; Stockturnover
for
transport; Scenarios
of energy and nonenergy
sector
emissions and sinks
used to simulate
European
Commission
integrated policies on
the use of renewable
sources, climate
change mitigation
and energy efficiency
improvement, the so
called 20–20–20
targets, and far more
stringent A/M targets
in the longer term at
the national and pan
EU level
International
recognition
Currently
LEAP has
over 5000
users in 169
countries
used in 70
countries by
250
institutions
Training and
technical support
Online training is
available and
sufficient for us
Technical support
provided against
fee
The most
demanding part of
MARKAL/TIMES
is training which
takes some months

6. References
[1] Stockholm Environment Institute. Community for energy, environment and development.
[accessed 02.02.2011].
[2] COMMEND, the community for energy, environment & development, IHS registered to access LEAP
applications. [accessed 02.02.2011].
[3] Yan X, Crookes RJ. Reduction potentials of energy demand and GHG emissions in China‘s road transport
sector. Energy Policy 2009;37(2):658–68.
[4] Giatrakos GP, Tsoutsos TD, Zografakis N., Sustainable power planning for the island of Crete. Energy Policy
2009;37(4):1222–38.
[5] Li J. Towards a low-carbon future in China‘s building sector – a review of energy and climate models
forecast. Energy Policy 2008;36(5):1736–47.
[6] Lund H, Munster E. Modelling of energy systems with a high percentage of CHP and wind power. Renew
Energy 2003;28(14):2179–93.
[7] Lund H. Large-scale integration of wind power into different energy systems. Energy 2005;30(13):2402–12.
[8] Lund H. Large-scale integration of optimal combinations of PV, wind and wave power into the electricity
supply. Renew Energy 2006;31(4): 503–15.
[9] Lund H, Munster E. Management of surplus electricity-production from a fluctuating renewable-energy
source. Appl Energy 2003;76(1–3):65–74.
[10] Lund H, Kempton W. Integration of renewable energy into the transport and electricity sectors through
V2G. Energy Policy 2008;36(9):3578–87.
[11] Lund H, Andersen AN. Optimal designs of small CHP plants in a market with fluctuating electricity prices.
Energy Convers Manage 2005;46(6): 893–904.
[12] Lund H, Munster E. Integrated energy systems and local energy markets. Energy Policy 2006;34(10):1152–
60.
[13] Lund H. Renewable energy strategies for sustainable development. Energy 2007;32(6):912–9.
[14] Münster M, Lund H. Use of waste for heat, electricity and transport – challenges when performing energy
system analysis. Energy 2009;34(5): 636–44.
[15] Mathiesen BV. Fuel cells and electrolysers in future energy systems. PhD thesis, Department of
Development and Planning, Aalborg University, Aalborg, Denmark, 2008.
.
[16] Mathiesen BV, Lund H. Comparative analyses of seven technologies to facilitate the integration of
fluctuating renewable energy sources. IET Renew Power Generat 2009;3(2):190–204.
[17] Chen M, Lund H, Rosendahl LA, Condra TJ. Energy efficiency analysis and impact evaluation of the
applications of thermoelectric power cycle to today‘s CHP systems. Appl Energy, in press
doi:10.1016/j.apenergy.2009.
[18] Blarke MB, Lund H. The effectiveness of storage and relocation options in renewable energy systems.
Renew Energy 2008;33(7):1499–507.
[19] Aalborg University. EnergyPLAN: advanced energy system analysis computer model.
[accessed 02.02.2011].
[20] Lund H. Renewable energy systems: the choice and modeling of 100% renewable solutions. MA, USA:
Academic Press, ISBN: 978-0-12-375028-0.
[21] Lund H, Duic N, Krajacic G, Graça Carvalho Md. Two energy system analysis models: a comparison of
methodologies and results. Energy 2007;32(6): 948–54.
[22] Lund H, Mathiesen BV. Energy system analysis of 100% renewable energy systems – the case of Denmark
in years 2030 and 2050. Energy 2009;34(5):524–31.
[23] Lund H, Salgi G. The role of compressed air energy storage (CAES) in future sustainable energy systems.
Energy Convers Manage 2009;50(5): 1172–9.
[24] Lund H, Salgi G, Elmegaard B, Andersen AN. Optimal operation strategies of compressed air energy

storage (CAES) on electricity spot markets with fluctuating prices. Appl Therm Eng 2009;29(5–6):799–806.
[25] Lund H, Clark WW. Management of fluctuations in wind power and CHP comparing two possible Danish
strategies. Energy 2002;27(5):471–83.
[26] DESIRE. Dissemination strategy on electricity balancing for large scale integration of renewable energy.
[accessed 03.02.11].
[27] Mathiesen BV. 100% Renewable energy systems in project future climate – the case of Denmark. In:
Proceedings of the 5th Dubrovnik conference for sustainable development of energy, water and environment
systems. Dubrovnik, Croatia, 29th September–3rd October, 2009
[28]. Vienna University of Technology. Invert. [accessed 02.02.11].
[29] Ragwitz M, Brakhage A, Kranzl L, Stadler M, Huber H, Haas R, et al. Final report of work phase 6 of the
project invert. Vienna University of Technology; 2005. .
[30] Stadler M, Kranzl L, Huber C, Haas R, Tsioliaridou E. Policy strategies and paths to promote sustainable
energy systems – the dynamic invert simulation tool. Energy Policy 2007;35(1):597–608.
[31] Kranzl L, Stadler M, Huber C, Haas R, Ragwitz M, Brakhage A, et al. Deriving efficient policy portfolios
promoting sustainable energy systems – case studies applying Invert simulation tool. Renewable Energy
2006;31(15): 2393–410.
[32] Bürger V, Klinski S, Lehr U, Leprich U, Nast M, Ragwitz M. Policies to support renewable energies in the
heat market. Energy Policy 2008;36(8):3150–9.
[33] Tsioliaridou E, Bakos GC, Stadler M. A new energy planning methodology for the penetration of renewable
energy technologies in electricity sector – application for the island of Crete. Energy Policy
2006;34(18):3757–64.
[34] Tosato GC. Introduction to ETSAP and the MARKAL–TIMES models generators. International Energy
Agency: NEET Workshop on Energy Technology Collaboration; 2008.
.
[35] International Energy Agency. TOOLS. < http://www.etsap.org/Tools.asp > [accessed 02.02.11].
[36] Goldstein GA, Tosato GC. Global energy systems and common analyses: final report of Annex X (2005–
2008). International Energy Agency; 2008. .
[37] Contaldi M, Gracceva F, Mattucci A. Hydrogen perspectives in Italy: analysis of possible deployment
scenarios. Int J Hydrogen Energy 2008;33(6): 1630–42.
[38] Tseng P, Lee J, Friley P. A hydrogen economy: opportunities and challenges. Energy 2005;30(14):2703–20.
[39] Gielen D, Simbolotti G. Prospects for hydrogen and fuel cells. International Energy Agency; 2005.
.
[40] Contreras A, Guervós E, Posso F. Market penetration analysis of the use of hydrogen in the road transport
sector of the Madrid region, using MARKAL. Int J Hydrogen Energy 2009;34(1):13–20.
[41] Endo E. Market penetration analysis of fuel cell vehicles in Japan by using the energy system model
MARKAL. Int J Hydrogen Energy 2007;32(10–11): 1347–54.
[421] Vaillancourt K, Labriet M, Loulou R, Waaub J-P. The role of nuclear energy in long-term climate
scenarios: an analysis with the World-TIMES model. Energy Policy 2008;36(7):2296–307.
[43] Hamacher T, Lako P, Ybema JR, Korhonen R, Aquilonius K, Cabal H, et al. Can fusion help to mitigate
greenhouse gas emissions? Fusion Eng Des 2001;58– 59:1087–90.
[44] Lechon Y, Cabal H, Varela M, Saez R, Eherer C, Baumann M, et al. A global energy model with fusion.
Fusion Eng Des 2005;75–79:1141–4.
[45] Biberacher M. Fusion in the global energy system – GIS and TIMES. International Energy Agency; 2006.
.
[46] Nguyen KQ. Impacts of wind power generation and CO2 emission constraints on the future choice of fuels
and technologies in the power sector of Vietnam. Energy Policy 2007;35(4):2305–12.
[47] Centre for Renewable Energy Sources (Greece). Monitoring and evaluation of the RES directives
24

implementation in EU27 and policy recommendations for 2020. [accessed
03.02.2011].
[48] International Institute for Applied Systems Analysis. Energy modeling framework: model for energy supply
strategy alternatives and their general environmental impact (MESSAGE). <
http://www.iiasa.ac.at/Research/ENE/model/message.html > [accessed 02.02.2011].
[49] Messner S, Strubegger M. User‘s Guide for MESSAGE III. International Institute for Applied Systems
Analysis; 1995. .
[50] Rao S, Riahi K. The role of non-CO2 greenhouse gases in climate change mitigation: long-term scenarios
for the 21st century. Energy J 2006;27: 177–200 [multi-greenhouse gas mitigation].
[51] Nakicenovic N, Gruebler A, McDonald A, editors. Global energy perspectives. Cambridge: Cambridge
University Press; 1998.
[52] Nakicenovic N, Alcamo J, Davis G, de Vries B, Fenhann J, Gaffin S, et al. Special report on emissions
scenarios. Intergovernmental panel on climate change; 2000.
.
[53] Grübler A, Nakicenovic N, Riahi K, Wagner F, Fischer G, Keppo I, et al. Integrated assessment of
uncertainties in greenhouse gas emissions and their mitigation: introduction and overview. Technol Forecast
Soc Change 2007; 74(7):873–86.
[54] Riahi K, Grübler A, Nakicenovic N. Scenarios of long-term socio-economic and environmental
development under climate stabilization. Technol Forecast Soc Change 2007;74(7):887–935.
[55] Shafiei E, Saboohi Y, Ghofrani MB. Impact of innovation programs on development of energy system: case
of Iranian electricity-supply system. Energy Policy 2009;37(6):2221–30.
[56] International Atomic Energy Agency. Assessing policy options for increasing the use of renewable energy
for sustainable development: Modelling energy scenarios for Sichuan, China. International Atomic Energy
Agency; 2007. .
[57] International Atomic Energy Agency. analyses of energy supply options and security of energy supply in
the Baltic states. International Atomic Energy Agency; 2007.
.
[58] International Atomic Energy Agency. Cuba: a country profile on sustainable energy development.
International Atomic Energy Agency; 2008. .
[59] International Institute for Applied System Analysis. Greenhouse gas initiative: scenario data base.
[accessed 03.02.11].
[60] Forschungszentrum Jülich Institute for Energy Research. Systems Analysis and Technology Evaluation.
[accessed 02.02.11].
[61] Martinsen D, Linssen J, Markewitz P, Vögele S. CCS: a future CO2 mitigation option for Germany? – a
bottom-up approach. Energy Policy 2007;35(4): 2110–20.
[62] Krey V, Martinsen D, Wagner H-J. Effects of stochastic energy prices on long-term energy–economic
scenarios. Energy 2007;32(12):2340–9.
[63] Martinsen D, Krey V. Compromises in energy policy – using fuzzy optimization in an energy systems
model. Energy Policy 2008;36(8): 2983–94.
[64] Martinsen D, Krey V, Markewitz P. Implications of high energy prices for energy system and emissions –
the response from an energy model for Germany. Energy Policy 2007;35(9):4504–15.
[64] INFORSE-Europe. Vision 2050: visions for a renewable energy world. [accessed 25.04.09].
[65] Schlenzig C. PlaNet – Ein entscheidungsunterstützendes System für die Energie-und Umweltplanung
(PlaNet – a decision support system for energy and environmental planning). PhD thesis, Institut für
Energiewirtschaft und Rationelle Energieanwendung, Universität Stuttgart, Stuttgart, Germany; 1997.
.
[66] Baumhögger F, Baumhögger J, Baur J, Kühner R, Schellmann U, Schlenzig C, et al. Mesap Manual:
Version 3.1. Institut für Energiewirtschaft und Rationelle Energieanwendung; 1998.
25

[67] Schlenzig C. Energy planning and environmental management with the information and decision support
system MESAP. Int J Global Energy Issues 1999;12(1–6):81–91
[68] Schlenzig C. Seven2one modelling: Erstellung integrierter Ener-giekonzepte mit Mesap/PlaNet (Seven2one
modelling: building integrated energy concepts with Mesap/PlaNet). seven2one; 2009. Error! Hyperlink
reference not valid.
[69] Krewitt W, Simon S, Graus W, Teske S, Zervos A, Schäfer O. The 2 C scenario – a sustainable world
energy perspective. Energy Policy 2007;35(10):4969–80.
[70] Greenpeace. Energy [r]evolution – a sustainable global energy outlook. Greenpeace; 2008.
.
[71] Al-Mansour F, Merse S, Tomsic M. Comparison of energy efficiency strategies in the industrial sector of
Slovenia. Energy 2003;28(5):421–40.
[71 a ] National Technical University of Athens. Energy–economics–environment modelling laboratory research
and policy analysis. < http://www.e3mlab.ntua.gr/e3mlab/ > [accessed 02.02.11].
[72] Capros P, Mantzos L, Papandreou V, Tasios N. Energy and transport outlook to 2030 – update 2007.
European Communities; 2008. .
[73] Commission of European Communities. Impact assessment: package of implementation measures for the
EU‘s objectives on climate change and renewable energy for 2020. Commission of European Communities;
2008. .
[74] Bulteel P, Belmans R, Dolben G, Garcia Madruga M, Kallstrand B, Lace I, et al. The role of electricity: a
new path to secure, competitive energy in a carbon-constrained world. Eurelectric; 2007.
.
[75] Capros P, Kouvaritakis N, Mantzos L. Economic evaluation of sectoral emission reduction objectives for
climate change: top-down analysis of greenhouse gas emission reduction possibilities in the EU. National
Technical University of Athens; 2001.

[86] Morthorst PE, Jensen SG, Meibom P. Investering og prisdannelse på et liberaliseret elmarked (Investment
and pricing in a liberalised electricity market). Risø National Laboratory; 2005.
.
[87] Jensen SG, Meibom P. Investments in liberalised power markets: gas turbine investment opportunities in the
Nordic power system. Int J Electr Power Energy Syst 2008;30(2):113–24.
[88] Ea Energy Analyses, Hagman Energy, COWI. Congestion management in the Nordic market – evaluation of
different market models. Nordic Council of Ministers; 2008. .
[89] Danish Energy Research Program 2003. Modelling imperfect competition on the Nordic electricity market
with Balmorel. Danish Energy Research Program 2003, 2005.
.
[90] VEKS, København E (KE), Metropolitan Copenhagen Heating Transmission Company (CTR). Long-term
heat plan for Greater Copenhagen. < http://www.varmeplanhovedstaden.dk/ > [accessed 11.07.09].
[91] Lindboe HH, Werling J, Kofoed-Wiuff A, Bregnbmk L. Impact of CO2 quota allocation to new entrants in
the electricity market. Ea Energy Analyses, Energy Modelling; 2007.
.
[92] Rud J. Systemanalyse af Compressed Air Energy Storage: Optimering, drift og implementering i det danske
energimarked (System analysis of a potential compressed air energy storage: optimization, operation and
implementation in the Danish energy market). Masters thesis, Institut for Mekanisk Teknologi, Danmarks
Tekniske Universitet, Copenhagen, Denmark; 2008. .
[93] Dittmar L. Integration of energy savings technologies and learning curves into the BALtic MOdel of
Regional Electricity Liberalisation – BALMOREL. Masters thesis, University of Flensburg, Flensburg,
Germany; 2006. .
[94] Ball M, Wietschel M, Rentz O. Integration of a hydrogen economy into the German energy system: an
optimising modelling approach. Int J Hydrogen Energy 2007;32(10–11):1355–68.
[95] Argonne National Laboratory. Energy and Power Evaluation Program (ENPEP- BALANCE).
[accessed 8.03.11]
[96] International Atomic Energy Agency. Comparative assessment of energy options and strategies in Mexico
until 2025, International Atomic Energy Agency; 2005, http://www.dis.anl.gov/news/MexicoEnergy.html,
[accessed 8.03.11]
[97] Conzelmann G, Koritarov V. Turkey energy and environmental review. Argonne National Laboratory; 2002,
http://www.dis.anl.gov/news/TurkeyUndp.html
[98] The United Nations (1996), Republic of Bulgaria: the first national communication on climate change,
[99] Argonne National Laboratory. ENPEP applications.
[accessed 8.03.11]
[100] Mirsagedis S, Conzelmann G, Georgopoulou E, Koritarov V, Sarafidis Y. Long-term GHG emissions
outlook for Greece. In: Proceedings of the 6th IAEE European conference on modeling in energy economics and
policy, Zurich, Switzerland, 2–3 September; 2004.
[101] Connolly D., Lund H., Mathiesen B.V., Leahy M. (2009), A review of computer tools for analyzing the
integration of renewable energy into various energy systems, Applied Energy
[102] Berger, C., R. Dubois, A. Haurie, E. Lessard, R. Loulou, and J.-Ph. Waaub, "Canadian MARKAL: an
Advanced Linear Programming System for Energy and Environment Modelling", INFOR, Vol. 20, 114-125,
1992.
6.1. References to the models used in FPs funded projects
Global-Greenhouse-Warming , 2011, Climate Mitigation and Adaptation, http://www.global-greenhousewarming.com/climate-mitigation-and-adaptation.html,
status 31 January 2011
Dr. Wolfram Schrimpf and Dr. Elisabeth Lipiatou , Research on Adaptation to Climate Change: Needs for
Future Research, Climate Change & Environmental Risks Unit, Research Directorate General, European
27

Commission, Presentation at the Bridging the Gap Conference, 14 – 16 May 2008, Portorož, Slovenia;
http://www.bridgingthegap.si/pdf/Adapation%20to%20climate%20change/Wolfram%20Schrimpf%20and%20El
isabeth%20Lipiatou%20NEEDS%20FOR%20FUTURE%20RESEARCH.pdf
European Research Framework Programme, Research on Climate Change, Prepared for the Third World
Climate Conference (WCC-3), and the UNFCCC Conference of the Parties (COP-15), Directorate-General for
Research, Environment, 2009, http://ec.europa.eu/research/environment/pdf/cop-
15.pdf#view=fit&pagemode=none
Drouet L., Vielle M., Labriet M. and R. Loulou, 2008, A master program that will drive the coupling of GEMINI-
E3 and MARKAL/TIMES models, Working paper, http://geminie3.epfl.ch/webdav/site/geminie3/shared/A%20master%20program%20that%20will%20drive%20the%20couplin
g%20of%20GEMINIE3%20and%20MARKAL%20TIMES%20models
PRIMES MODEL; Prof. P. Capros, E3Mlab of ICCS/NTUA; As Used for the 2007 EC Scenarios,
http://www.e3mlab.ntua.gr/e3mlab/PRIMES%20Manual/The_PRIMES_MODEL_2008.pdf
Richard Loulou et al., 2005 , Documentation for the TIMES Model, PART I,., Energy Technology Systems
Analysis Programme, April 2005, http://www.etsap.org/tools.htm
ADAM project: Full title: Adaptation and Mitigation Strategies: Supporting European Climate Policy,
http://www.adamproject.eu/
D. Connolly, H. Lund b, B.V. Mathiesen, M. Leahy (2010), A review of computer tools for analysing the
integration of renewable energy into various energy systems, Applied Energy, journal homepage: www.elsevier
.com/ locate/apenergy
Fishbone, L.G., and H. Abilock, (1981), "MARKAL, A Linear Programming Model for Energy Systems
Analysis: Technical Description of the BNL Version", International Journal of Energy Research, Vol. 5, 353-
375.
Manne, A.S., and Wene, (1992), C-O., "MARKAL-MACRO: A Linked Model for Energy- Economy Analysis",
BNL-47161 report, Brookhaven National Laboratory, Upton, New-York, February 1992.
Meade, D. (1996) ―LIFT User‘s Guide‖, Technical report, INFORUM, IERF, College Park, Md (July 1996).
Jaccard, M., J. Nyboer, C. Bataille, and B. Sadownik (2003), "Modeling the Cost of Climate Policy:
Distinguishing between alternative cost definitions and long-run cost dynamics." Energy Journal 24(1): 49-73.
28