MARKAL: Frequently Asked Questions (FAQ's)
In order to work with MARKAL, you need a number of software elements:
Information regarding the acquisition of software can be obtained here.
MARKAL can be provided at no cost, but an ETSAP R&D contribution is expected
from commercial and government institutions. In addition, you must buy a shell,
an optimiser (e.g. OSL) and GAMS, the language in which the standard MARKAL model
The MARKAL databases are owned by the institutes which have developed the databases. Please contact the MARKAL user in your country if they are willing to let you share their database, for more details, see ETSAP members. MARKAL is distributed with a pair of demo databases.
The model runs on a personal computer with a Pentium processor (Windows95 and Office95 or higher for ANSWER).
The construction of a small MARKAL model for a country can be finished in 1 month,
if you are a MARKAL expert and the statistics are available. However, the bulk of
the work is in the collection of the development of the process database.
For example, the building of the MATTER MARKAL
model took about 1 man-year of modelling, but 10 man-years of data collection.
Building such a model is a time-consuming exercise that should not be underestimated.
A MARKAL model produces output that is determined by the input parameters and by the model algorithm. In this sense, the model works like a pocket calculator. The model is not an oracle, but a method to produce a techno-economic scenario for the future in a logical, traceable manner. Key paradigms are an ideal market (competitive partial equilibrium) and the predictability of technological development over a period of several decades.
Typical MARKAL models run in 3-10 minutes depending upon size and assuming
a state-of-the-art LP (Linear Programming) optimizer.
MARKAL-MACRO (NLP, Non-Linear Programming) runs in 0.25-4 hours depending
on model size, although advancements in computer capability tend to decrease
these running times. In addition, ETSAP is currently investigating the possibility
the NLP formulation into a Mixed Complementarity Programming (MCP) formulation.
For a certain class of MCP problems, the use of the PATH solver for MCP leads to
significantly lower solution times compared to the analogous NLP problem.
The model consists of a database of processes, a demand vector, and import and export data. Processes are characterised by their inputs and outputs of energy and materials, by their costs and by their emissions. Examples of process data that serve as MARKAL input can be found in the list of publications for MATTER .
The MATTER model consists of approximately 50 types of energy carriers,
150 materials, 100 demand categories and a database of several hundred processes.
A full list of processes is available for the MATTER 1.0 model version
(see the list of publications or the
PDF report format(248 kbytes).
The most recent MATTER 4.2 MARKAL model consists of a matrix of 25,000 rows and 50,000
columns. The .DD file (the matrix file) is approximately 5 Mb in size.
The results are mainly used by governments, international bodies such as IEA, OECD, IPCC, and by organisations that fund R&D for energy technology. The results are used for development of environmental strategies (mainly greenhouse gas emission reduction and reduction of NOx and SO2 emissions), energy policy making, industrial policy making, and for evaluation of policy instruments. Fields where the model has not yet been applied extensively, but where further applications can be visualised are in the development of sustainability strategies, industry strategy development or the assessment of waste policies.
The MARKAL-MACRO model features in addition to the MARKAL model:
See MARKAL-MACRO for more information.
Besides the MACRO module, MARKAL has also formulations for other flexible demand formulations, viz. the elastic MARKAL-ED and MARKAL-MICRO:
A novel feature of the MARKAL family of models is endogenous
technology learning (ETL).
In 1998, the Swiss ETSAP Partner, the Paul Scherrer Institute (PSI),
and the Dutch ETSAP Partner, ECN, carried out the first MARKAL
experiments with endogenous technology learning. PSI experimented with
a small scale model for the global electricity demand. ECN used its
large scale MARKAL application for the Western European energy system.
The first PSI and ECN experiments were presented at the
ETSAP Workshop in October 1998 .