Endogenizing R&D and market experience in the “bottom-up” energy-systems ERIS model

Abstract

ERIS, an energy-systems optimization model that endogenizes learning curves, is modified in order to incorporate the effects of R&D investments, an important contributing factor to the technological progress of a given technology. For such purpose a modified version of the standard learning curve formulation is applied, where the investment costs of the technologies depend both on cumulative capacity and the so-called knowledge stock. The knowledge stock is a function of R&D expenditures that takes into account depreciation and lags in the knowledge accumulated through R&D. An endogenous specification of the R&D expenditures per technology allows the model to perform an optimal allocation of R&D funds among competing technologies. The formulation is described, illustrative results presented, some insights are derived, and further research needs are identified.

Empirical challenges in the use of learning curves for assessing the economic prospects of renewable energy technologies

“…These latter specifications are commonly known as two-factor learning curves, and they produce also an estimate of the so-called learning-by-searching rate, which shows the impact on costs of a doubling in the R&D-based variable. Incorporating such R&D-based measures into bottom-up energy models permits an analysis of the optimal allocation of R&D funds among competing technologies (e.g., [6]). The two-factor learning curve equation can thus be written as…”

“…2 The energy system models that incorporate learning-by-doing for energy technologies typically have a bottomup structure, i.e., optimization models in which different technological options are explicitly specified. See, for instance, Messner [3], Mattsson and Wene [4], Kouvaritakis et al [5], Barreto and Kypreos [6], and the survey article by Berglund and So¨derholm [7]. Rasmussen [8] introduces learning-by-doing in a top-down (general equilibrium) model.…”

“…This suggests that both innovation and diffusion ought to be viewed as endogenous, i.e., they are simultaneously determined and should not be analyzed in isolation. 6 Technically endogeneity implies that in the learning equation in Eq. (2) the regressor CC nt and the disturbance term e nt are negatively correlated.…”

Modeling technological change and its impact on energy savings in the U.S. iron and steel sector

“…Faster adoption of the technology may simulate further decrease in costs [6][7][8][9]. Learning curve approach has been incorporated into many energy models to project cost reductions from investment in new energy generation or conversion [10].…”

Swiss Energy Strategies under Global Climate Change and Nuclear Policy Uncertainty

“…As mentioned above, ETL captures the possibility for relatively immature and expensive technologies to improve with the accumulation of experience and knowledge to achieve long-term competitiveness. This is represented by means of a two-factor learning curve, developed originally by Manne and Barreto (2004) and further developed in Barreto and Kypreos (2004), Kypreos (2005), and Kypreos and Bahn (2003). The first factor corresponds to the so called "learning-by doing", describing the investment cost as a function of the cumulative capacity, which is used as a proxy for the cumulative experience with the technology (Kypreos and Bahn, 2003).…”