From fossil fuels to renewables: An analysis of long-term scenarios considering technological learning

This study analyses a diffusion of renewable energy in an electricity system accounting for technological learning. We explore long-term scenarios for capacity expansion of the Java-Bali electricity system in Indonesia, considering the country’s renewable energy targets. We apply the Long-range Energy Alternative Planning (LEAP) model with an integration of technological learning. Our results reveal that, at the medium and high pace of technological learning, the total costs of electricity production to achieve the long-term renewable energy target are 4–10% lower than the scenario without considering technological learning. With respect to technology, solar PV and wind become competitive with other types of renewables and nuclear. Moreover, the fulfilment of the renewable energy targets decreases CO2 emissions by 25% compared to the reference scenario. Implications of our results indicate that energy policies should focus on the early deployment of renewables, upgrading the grid capacity to accommodate variable renewable energy, and enabling faster local learning.

[1]  Leila Dagher,et al.  Modeling Lebanon's electricity sector: Alternative scenarios and their implications , 2011 .

[2]  Ambuj D. Sagar,et al.  Technological innovation in the energy sector : R&D, deployment, and learning-by-doing , 2006 .

[3]  A. Zobaa,et al.  Techno-economic and environmental analysis of power generation expansion plan of Ghana , 2017 .

[4]  Tirso Leonardo Barreto Gómez,et al.  Technological Learning in Energy Optimisation Models and Deployment of Emerging Technologies , 2001 .

[5]  H. Ishaque,et al.  Is it wise to compromise renewable energy future for the sake of expediency? An analysis of Pakistan's long-term electricity generation pathways , 2017 .

[6]  Sun-Jin Yun,et al.  An analysis of long-term scenarios for the transition to renewable energy in the Korean electricity sector , 2013 .

[7]  Wongkot Wongsapai,et al.  Integrated Model for Energy and CO2 Emissions Analysis from Thailand's Long-term Low Carbon Energy Efficiency and Renewable Energy Plan , 2016 .

[8]  J. Guerra,et al.  Future scenarios and trends in energy generation in brazil: supply and demand and mitigation forecasts , 2015 .

[9]  Subhash Kumar.  Assessment of renewables for energy security and carbon mitigation in Southeast Asia: The case of Indonesia and Thailand , 2016 .

[10]  A. Maleki,et al.  Reshaping energy policy for sustainable development: Curbing Iran’s carbon emission monster via renewable energies , 2016 .

[11]  Wilfried van Sark,et al.  Photovoltaic Solar Energy: From Fundamentals to Applications , 2017 .

[12]  Tatiana Filatova,et al.  Trade-offs between electrification and climate change mitigation: An analysis of the Java-Bali power system in Indonesia , 2017 .

[13]  Chihiro Watanabe,et al.  Identification of the role of renewable energy. A view from Japan's challenge: the New Sunshine Program , 1995 .

[14]  Wolfgang Lucht,et al.  The world's biggest gamble , 2016 .

[15]  J. Farmer,et al.  How Predictable is Technological Progress? , 2015, 1502.05274.

[16]  Leo Schrattenholzer,et al.  Learning rates for energy technologies , 2001 .

[17]  Lixiao Zhang,et al.  Scenario analysis of urban energy saving and carbon abatement policies: A case study of Beijing city, China , 2012 .

[18]  John Rust,et al.  On the Optimal Lifetime of Nuclear Power Plants , 1996 .

[19]  Nebojsa Nakicenovic,et al.  Dynamics of energy technologies and global change , 1999 .

[20]  Angel Nikolaev,et al.  Development and assessment of renewable energy policy scenarios by 2030 for Bulgaria , 2017 .

[21]  B. Lundvall.  The Learning Economy and the Economics of Hope , 2016 .

[22]  R. Madlener,et al.  CO2 Emission Reduction Potential Assessment Using Renewable Energy in India , 2016 .

[23]  S. Kahouli-Brahmi.  Technological learning in energy–environment–economy modelling: A survey , 2008 .

[24]  Alexander Wokaun,et al.  Technological learning in energy optimisation models and deployment of emerging technologies , 2001 .

[25]  J. Doyne Farmer,et al.  How well do experience curves predict technological progress? A method for making distributional forecasts , 2017, 1703.05979.

[26]  Edward S. Rubin,et al.  Power capacity expansion planning considering endogenous technology cost learning (vol 204, pg 831, 2017) , 2017 .

[27]  P. Jaramillo,et al.  A review of learning rates for electricity supply technologies , 2015 .

[28]  En Sup Yoon,et al.  Optimization of Korean energy planning for sustainability considering uncertainties in learning rates and external factors , 2012 .

[29]  Richard G. Newell,et al.  Modeling endogenous technological change for climate policy analysis , 2008 .

[30]  T. P. Wright,et al.  Factors affecting the cost of airplanes , 1936 .

[31]  Patrik Söderholm,et al.  Modeling technical change in energy system analysis: analyzing the introduction of learning-by-doing in bottom-up energy models , 2006 .

[32]  Z. Griliches.  Productivity, R&D, and the Data Constraint , 1998 .

[33]  T. Schmidt,et al.  The Effect of Local and Global Learning on the Cost of Renewable Energy in Developing Countries , 2014 .

[34]  Arnulf Grubler,et al.  Lessons from the history of technological change for clean energy scenarios and policies , 2011 .