Modeling technical change in energy system analysis: analyzing the introduction of learning-by-doing in bottom-up energy models

Berglund, Christer; Söderholm, Patrik

doi:10.1016/j.enpol.2004.09.002

Cited by 87 publications

(53 citation statements)

References 30 publications

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“…Incorporating learning in an endogenous manner into bottomup linear optimization models often causes computational problems because the learning curve is non-convex and non-linear [50]. The most common way of solving this problem in linear programming models is mixed-integer programming (MIP), as reported in the literature for MARKAL [51], MESSAGE [52] and GENIE energy models [53].…”

Section: Literature Reviewmentioning

confidence: 99%

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

2017

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a b s t r a c tMarket penetration of energy-efficient technologies can be estimated using energy optimization models that minimize cost; however, such models typically estimate the minimum cost of optimal pathways under a certain set of non-dynamic assumptions, so technology penetrations determined for the longterm do not fully respond to changing circumstances or costs. In this study, investment costs of energy-efficient technologies are modeled dynamically in the Industrial Sector Energy-Efficiency Model (ISEEM) using a technological learning formula. Results from 24 energy-efficient technologies -14 existing, 10 emerging -selected from the United States (U.S.) iron and steel sector show that when technological learning is incorporated into the model, total energy consumption of this sector is expected to decrease by 13% (180 PJ) in 2050 compared to energy consumption in a non-learning scenario. Average energy intensity of the steel production improves from 12.3 GJ/t in the non-learning scenario to 10.7 GJ/t in the learning scenario in 2050. This decrease represents a cost savings of US$1.6 billion and a carbon dioxide emissions reduction potential of 14.9 billion tonnes. Results discussed in this paper focus on the U.S. iron and steel sector, but the proposed framework can be applied to study new technology development in any other industrial processes and regions.

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Section: Literature Reviewmentioning

confidence: 99%

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

2017

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“…Aggregating all of the highly diverse power plant technologies into a single electricity sector will produce inaccurate results (de Koning et al, 2015). Berglund and Söderholm (2006); Böhringer (1998); Böhringer and Rutherford (2008); Frei et al (2003); Herbst et al (2012); Jacobsen (1998); Koopmans and te Velde (2001);McFarland et al (2004). Therefore, developing hybrid approaches is warranted to solve the weaknesses of the bottom-up and top-down approaches (Nakata et al, 2011).…”

Section: Hybrid Energy Toolsmentioning

confidence: 99%

Selecting Tools for Renewable Energy Analysis in Developing Countries: An Expanded Review

et al. 2017

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Renewable energy planners in developing countries should be cautious in using analytical tools formulated in developed countries. Traditional energy consumption, economic and demography transitions, high-income inequality, and informal economy are some characteristics of developing countries that may contradict the assumptions of mainstream, widely used analytical tools. In this study, we synthesize the debate in previous review studies on energy models for developing countries and then extend the scope of the previous studies by highlighting emerging methods of system thinking, life cycle thinking, and decision support analysis. We then discuss how these tools have been used for renewable energy analysis in developing countries and found out that not all studies are aware of the emerging critical issues in developing countries. We offer here a guidance to select the most appropriate analytical tool, mainly when dealing with energy modeling and analysis for developing countries. We also suggest potential future improvements to the analytical tool for renewable energy modeling and analysis in the developing countries.

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“…7 In many learning curve studies model performance is only evaluated by checking the t-statistics for each coefficient and the values of the R 2 measures (e.g., [11]). However, even if the R 2 is close to one (1) and all coefficients are statistically significant, omitted variable bias may still pose a problem.…”

Section: Article In Pressmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

“…In the case of the learning curve model this could be a real problem since costs clearly are influenced by other variables than cumulative capacity, most notably input prices and scale effects. 7 In order to illustrate the potential importance of this problem, it is useful to see how one can integrate technology learning into a coherent microeconomic framework. In Section 2.2, we therefore integrate the learning curve model in Eq.…”

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confidence: 99%

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

Söderholm

Sundqvist²

2007

Renewable Energy

Self Cite

174

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In bottom-up energy models endogenous technical change is introduced by implementing technology learning rates, which specify the quantitative relationship between the cumulative experiences of the technology on the one hand and cost reductions on the other. The main purpose of this paper is to critically analyze the choice of modeling and estimation strategies in learning curve analyses of power generation costs. We identify and discuss a number of theoretical and econometric issues involved in the estimation of learning curves. These include the presence of omitted variable bias and simultaneity, but also methodological problems related to the operationalization of theoretical concepts (i.e., learning-bydoing) and the associated use of data. We illustrate the importance of these issues by employing panel data for wind power installations in four western European countries, which are used to compare the results from different learning curve model specifications. The results illustrate that the estimates of learning rates may differ significantly across different model specifications and econometric approaches. The paper ends by outlining a number of recommendations for energy model analysts, who need to select appropriate energy technology learning rates from the empirical literature, or who choose to perform the empirical work themselves. r

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Modeling technical change in energy system analysis: analyzing the introduction of learning-by-doing in bottom-up energy models

Cited by 87 publications

References 30 publications

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

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

Selecting Tools for Renewable Energy Analysis in Developing Countries: An Expanded Review

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

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