Leonardo Barreto and Socrates Kypreos
Energy Modeling Group
General Energy Research Department
Paul Scherrer Institute (PSI)
CH-5232, Villigen, Switzerland
With technology being a fundamental driving factor of the evolution of energy systems, it is essential to study the basic mechanisms of technological change and its role in achieving more efficient, productive and clean energy systems. Understanding its dynamics constitutes a guide for policy formulation and decision making and the conception of effective intervening instruments.
Technology development does not occur as an autonomous independent process, but evolves from a number of endogenous interactions within the social system. Technologies evolve and improve only if experience with them is possible. Cumulative learning, both in R&D activities and the marketplace is, in fact, one of the basic mechanisms in the emergence and replacement of technological regimes.
Thus, efforts must be devoted to improve our analytical tools and decision-support frameworks concerning the treatment given to the technological variable. Despite its undeniable importance, several technological factors have been traditionally addressed in an oversimplified way in energy optimisation models, without recognising the cumulative and gradual nature of technological change and the important role that learning processes play in achieving performance increases in a given technology or group of them.
This report presents a collection of works developed by the authors concerning the endogenisation of technological change in energy optimisation models, as a contribution to the Energy Technology Dynamics and Advanced Energy System Modelling Project (TEEM), developed in the framework of the Non Nuclear Energy Programme JOULE III of the European Union (DGXII). Here, learning curves, an empirically observed manifestation of the cumulative technological learning processes, are endogenised in two energy optimisation models. MARKAL, a widely used bottom-up model developed by the ETSAP programme of the IEA and ERIS, a model prototype, developed within the TEEM project for assessing different concepts and approaches. The incorporation of the curves provides the model with a mechanism to represent path-dependence and self-reinforcing phenomena intervening in shaping the technological trajectories of the system.
The methodological approach is described and some results and insights derived from the model analyses are presented. The incorporation of learning curves results in significantly different model outcomes than those obtained with traditional approaches. New, innovative technologies, hardly considered by the standard models, are introduced to the solution when endogenous learning is present. Up-front investments in initially expensive, but promising, technologies allow the necessary accumulation of experience to render them cost-effective. When uncertainty in emission reduction commitments is considered, the results point also in the direction of undertaking early action as a preparation for future contingencies. Early investments stimulating technological learning prove beneficial in terms of both lower costs and emissions in the long run. On the other hand, when the learning rates of the technologies are uncertain, a more prudent intermediate path of installations is followed, but technological learning in emerging technologies continues to be an important hedging mechanism to prepare for future actions. Increasing returns associated to the effects of learning and uncertainty emerge as core mechanisms of the technological change process.
The results obtained using this modelling approach provide some important policy insights. Early investments on sustainable technologies, both in R&D, demonstration projects and niche markets, are required in order to ensure that they move along their learning curves and achieve long run competitiveness. New technologies will become competitive only if cumulative experience and investments are made. Their successful introduction requires then the promotion of innovation and learning at multiple technological, social and institutional levels.
It is necessary to advance further in the endogenisation of technological change into energy planning models. The treatment given to technology dynamics affects our understanding of a number of issues concerning the future structure of global energy systems and their environmental impacts (e.g. contribution to climate change). An adequate framework is necessary for gaining insights about the underlying forces that drive this evolution.
Therefore, other aspects of technological change, related, for instance, to other intervening factors such as R&D expenditures, or the spatial and temporal patterns of technological diffusion must be incorporated. It is also important to examine the interrelations between uncertain technological learning and policies for greenhouse gases reduction, examining the mutual impacts both on the technological evolution and the costs of abatement strategies and to address aspects such as "spill-over" of learning and co-evolution of technologies in clusters. In addition, different procedures to handle uncertainty in the learning processes and other technical and economic variables should be explored. Also, a careful technology characterisation and the study of the main driving factors of technological change must support the assumptions for the learning process and complement the analysis.
More details and related information on this subject
Both PSI and ECN have worked in this field since 1998. An overview of relevant publications are also available on this site