Providing
adequate energy services while complying with increasingly stringent
criteria of high quality, flexibility, low
environmental impact(s), economic soundness etc, requires new generations
of technologies that could fulfill those substantial requirements.
ECS research on technology assessment seeks to better understand the dynamics
and determinants of technological change and explore the potential role
that new, promising technologies may play in the global energy system
in the long-term future.
In doing so, the main determinants of energy technology development
are incorporated into ECS models in order to assess alternative policies
for the acceleration
deployment of environmentally compatible technologies into the energy
sector. The models contribute
to the
development
of
long-term
global
scenarios
to compare policy options of selected alternative generic technologies.
The role of those technologies is examined by combining an investigation
of their current and future development, opportunities and obstacles
with an examination of their contribution in different scenarios.
Within our technology assessment includes the maintenance of the Carbon
Dioxide (Technology) Database CO2DB.
The database currently contains approximately 3000 technologies, including
detailed technical, economic and environmental characteristics as well
as data on innovation, commercialization and diffusion. Users can add
to, select, filter, arrange, and compare CO2DB's data according to any
of the technology characteristics included in each database entry. For
more information, click
here.
Among other characteristics, learning rates have been collected for a number of technologies and some of them are being examined in more detail in collaboration with the EXCETP network.
Learning Rates for Energy Technologies
Technological learning is increasingly
being incorporated in models to assess long-term energy strategies and
related greenhouse
gas emissions. Most of these applications use learning rates based on
studies of non-energy technologies, or spars results from a few energy
studies. 
This
study is a step towards a larger empirical basis for choosing learning
rates (or learning rate distributions) of energy
conversion
technologies. The authors compiled and estimated 42 learning rates of
energy technologies. They analyzed their variability,
and
evaluated
their
usefulness
for
applications
in long-term energy models.
McDonald, A., Schrattenholzer,
L., 2001: Learning rates for energy technologies. Energy Policy 29(4):255-261.
Reprinted as RR-01-014.
International Institute for Applied Systems Analysis, Laxenburg, Austria.
McDonald, A., Schrattenholzer, L., 2002: Learning curves and technology assessment. International Journal of Technology Management 23(7/8):718-745. Reprinted as RR-03-002. International Institute for Applied Systems Analysis, Laxenburg, Austria.
|
EU FP6 Collaboration:
Case Study Comparisons And Development of Energy Models for Integrated Technology Systems (CASCADE-MINTS) (2004-2006) |
Institute of Communication and Computer Systems of National Technical University of Athens (ICCS/NTUA) (Co-ordination); Energy Research Centre of the Netherlands (ECN); Centre National de la Recherche Scientifique (CNRS); International Institute for Applied Systems Analysis (IIASA); Joint Research Centre (JRC), Paul Scherrer Institut (PSI); Centre for European Economic Research (ZSW); German Aerospace Center, Institute of Technical Thermodynamics (DLR-ITT), Institute of Vehicle Concepts (DLR-IFK); Institute of Energy Economics and the Rational Use of Energy, University of Stuttgart (IER); Centrale Recherche (CRSA/ERASME).
This project is divided into two parts: 1) Modeling possible configurations of a hydrogen economy and its prospects; 2) Case studies on policy issues with operational energy models.
Part 1 is a modeling project where a number of existing models are extended and radically redesigned so as to describe all possible configurations of a hydrogen economy. All demand categories where fuel cells can be used are included as well as the different options for distributing, storing and producing hydrogen from different primary sources. The models are used to analyze scenarios assuming favorable trajectories for the technical and economic characteristics of hydrogen related technologies (both on the demand and supply side). Special attention is given to technology clusters where particular breakthroughs may produce cumulative effects. In evaluating the scenarios, emphasis is given to the investment requirements, particularly infrastructure investments implied by them. The models used are specifically designed to describe analytically the whole energy system and will explicitly address issues of competition and complementarity of hydrogen with other energy system configurations and notably alternative futures for the power generation sector.
Technology dynamics mechanisms are incorporated in the models to enable them 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. Stochastic modeling is undertaken to allow a systematic assessment of the likelihood of different paths towards a hydrogen dominated energy system.
The main aim of PART 2 is to use a wide range of existing operational energy and energy/economy models in order to build an analytical consensus concerning the impacts of policies aimed at sustainable energy systems. Main expected outcomes of PART 2 are policy reports that address the potential role of technologies in promoting sustainable development. In tackling climate change and security of supply in the medium and long term, hydrogen and fuel cells, CO2 capture and storage, renewables and nuclear energy could play a key role. The question is to what extent these technologies can contribute to lowering GHG emissions and import dependency, and to what extent appropriate policies can foster their development and subsequent deployment. This part builds on the experience obtained in the EC-sponsored ACROPOLIS project, assisted by DG research within the 5th framework programme and includes a wide variety of models and modeling teams from EU countries as well as from institutions based outside the EU.
| Influence
of Technological Learning of Advanced Energy Conversion Technologies
on Future Energy Perspectives (2004-2006) |
Using the scenario approach, the study will examine the role of a selected group of innovative energy-conversion technologies in the electricity generation and fuel production sectors which could have an effect on the mid- and long-term perspectives of the global energy system. The analysis will use the multi-regional energy-system MESSAGE model, which provides substantial technological detail (Messner and Strubegger, 1995). The role of these technologies could be highlighted in the context of one of the IPCC-SRES scenarios developed at IIASA-ECS (SRES, 2000). Most likely, a “dynamics-as-usual” development, such as the B2 scenario, would be chosen (Riahi and Roehrl, 2000). The contribution of these technologies will be assessed both in a baseline and an illustrative CO2-constrained scenario, the latter allowing for an assessment of the potential contribution of these selected technologies to energy sustainability goals related to climate change.
Central to this analysis is the concept of technology learning, i.e., performance improvements of technologies as a result of experience accumulated in the marketplace (e.g., Argote and Epple, 1990; McDonald and Schrattenholzer, 2002). The learning process of a given technology, however, is not independent. Technological clusters of related or complementary technologies co-evolve (Silverberg, 1991; Nakicenovic, 1997). As a part of the clustering process, spillovers of learning between the technologies occur, among others due to their sharing of common key components.
On the basis of previous technology assessment studies conducted for TEPCO (Makihira et al., 2002, Yamashita and Barreto, 2003, 2004), the effect of different assumptions of technology learning for several key components in the diffusion of selected advanced energy-conversion technologies will be examined.
Makihira, A, Barreto L, Riahi K., 2002. Assessment of Alternative Hydrogen Pathways: Natural Gas and Biomass. Final Report on the TEPCO-IIASA Collaborative Study. Laxenburg, Austria.
McDonald, A., Schrattenholzer, L., 2002: Learning Curves and Technology Assessment. Int. J. Technology Management 23 (7/8), 718-745.
Messner, S. and Strubegger, M., 1995: User's Manual of MESSAGE III, WP-95-69, International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria.
Nakicenovic, N., 1997. Technological Change as a Learning Process. IIASA Induced Technology Workshop. International Institute for Applied Systems Analysis. Laxenburg, Austria.
Riahi, K. and Roehrl, R.A., 2000: Greenhouse gas emissions in a dynamics-as-usual scenario of economic and energy development. Technological Forecasting and Social Change 63:175-205.
Silverberg, G., 1991: Adoption ad Diffusion of Technology as a Collective Evolutionary Process. Technological Forecasting and Social Change 39, 67-80.
SRES (Special Report on Emission Scenarios), 2000: A Special Report on Emissions Scenarios for Working Group III of the Intergovernmental Panel on Climate Change (IPCC), Cambridge University Press, Cambridge, UK.
Yamashita K., Barreto L., 2003. Integrated Energy Systems for the 21st Century: Coal Gasification for Co-producing Hydrogen, Electricity and Fuels. Interim Report IR-03-039 on the TEPCO-IIASA Collaborative Study. Laxenburg, Austria. June, 2003.
Yamashita, K., Barreto L., 2004. Assessment of Advanced Technologies for Alternative Final-Energy Carriers: Biomass Gasification for the Co-production of Fischer-Tropsch Liquids and, Electricity. Final Report on the TEPCO-IIASA Collaborative Study. Laxenburg, Austria.
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14 Mar 2006
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