[en] Understanding technology dynamics, a fundamental driving factor of the evolution of energy systems, is essential for sound policy formulation and decision making. Technological change is not an autonomous process, but evolves from a number of endogenous interactions within the social system. Technologies evolve and improve only if experience with them is possible. Efforts must be devoted to improve our analytical tools concerning the treatment given to the technological variable, recognising the cumulative and gradual nature of technological change and the important role played by learning processes. 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 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 provide important policy insights: Stimulation of technological learning of emerging, promising energy technologies, by R and D, demonstration projects and deployment in niche markets, appears as the optimal strategy to achieve the long term transition towards more productive and clean energy systems. (author)