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Title:
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Endogenous technological learning: experiments with MARKAL: contribution to Task 2.3 of the EU-TEEM Project
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Author(s):
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Published by:
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Publication date:
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ECN
Policy Studies
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1-12-1998
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ECN report number:
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Document type:
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ECN-C--98-064
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ECN publication
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Number of pages:
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Full text:
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87
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Download PDF
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Abstract:
The experiences gained with including endogenous technology learning inthe energy optimisation model MARKAL are reported. The objective of the TEEM
project is to provide new insights for the European Union (EU) energy
Research and Technology Development (RTD) strategy, focusing on policy or
market induced progress of energy technologies. Activity 2.3 provides
methodological recommendations, numerical results and sensitivity analyses
for use in other activities of the TEEM project, based on experience with the
MARKAL model with endogenous learning. First, the formulation of learning
curves in MARKAL is described. The basic implementation was carried out by
the Paul Scherrer Institute (PSI). Some conceptual issues for characterising
energy technologies development and introducing technology learning in an
energy system model are discussed and used to select technologies with
learning from the MARKAL-Europe database. For these selected technologies
learning parameters are estimated and introduced in several model cases to
test the new endogenous technology learning feature of MARKAL. The main
finding is that a full-scale MARKAL model with learning is able to generate
globally optimal solutions efficiently. For an optimal use of the benefits of
including technology learning, some model database improvements are
recommended, such as re-evaluation of the reference energy system and a
well-targeted use of bounds and growth parameters. Sensitivity cases show the
large impact of assumed learning parameters on model outcomes. Furthermore,
model cases are analysed simulating the impact of R, D and D activities and
the introduction of a CO2 tax on technology learning and thus on the market
penetration of technologies. 55 p.
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