Learning and cost reductions for generating technologies in the national energy modeling system (NEMS)

Gumerman, Etan; Marnay, Chris

doi:10.2172/824272

Cited by 9 publications

(9 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The National Energy Modeling System (NEMS) [40] uses endogenous learning with different learning stages [41]. They differentiated between a revolutionary stage (up to three doublings in capacity), evolutionary stage (three to eight doublings in capacity), and a conventional stage (after eight doublings).…”

Section: Application Of Learning-by-doing In Energy System Modelsmentioning

confidence: 99%

“…Their approach is similar to NEMS [41], where the LR is assumed to be ∼20% for the first three capacity doublings (revolutionary stage), ∼10% for the next five doublings (evolutionary stage), and ∼1% for any further capacity expansions (mature stage). As outlined in Section 3.1, Böhm et al [19] used a component learning curve approach that resulted in diminishing electrolyzer LRs as the capacity increased.…”

Section: Unconventional Learning Curvesmentioning

confidence: 99%

See 1 more Smart Citation

Applying Endogenous Learning Models in Energy System Optimization

et al. 2021

View full text Add to dashboard Cite

Conventional energy production based on fossil fuels causes emissions that contribute to global warming. Accurate energy system models are required for a cost-optimal transition to a zero-emission energy system, which is an endeavor that requires a methodical modeling of cost reductions due to technological learning effects. In this review, we summarize common methodologies for modeling technological learning and associated cost reductions via learning curves. This is followed by a literature survey to uncover learning rates for relevant low-carbon technologies required to model future energy systems. The focus is on (i) learning effects in hydrogen production technologies and (ii) the application of endogenous learning in energy system models. Finally, we discuss methodological shortcomings of typical learning curves and possible remedies. One of our main results is an up-to-date overview of learning rates that can be applied in energy system models.

show abstract

Section: Application Of Learning-by-doing In Energy System Modelsmentioning

confidence: 99%

Section: Unconventional Learning Curvesmentioning

confidence: 99%

Applying Endogenous Learning Models in Energy System Optimization

et al. 2021

View full text Add to dashboard Cite

show abstract

“…OCC is a typical parameter that is used in the power generation industry to describe the cost of building a power plant overnight [18]. OCC is useful to compare the economic feasibility of building plants with various technologies or technology advancement.…”

Section: Model Development For Technology Advancementmentioning

confidence: 99%

The impacts of wind technology advancement on future global energy

Zhang

Song

et al. 2016

Applied Energy

View full text Add to dashboard Cite

h i g h l i g h t sIntegrated assessment model perform a series of scenarios of technology advances. Explore the potential roles of wind energy technology advance in global energy. Technology advance impacts on energy consumption and global low carbon market. Technology advance influences on global energy security and stability. a r t i c l e i n f o b s t r a c tTo avoid additional global warming and environmental damage, energy systems need to rely on the use of low carbon technologies like wind energy. However, supply uncertainties, production costs, and energy security are the main factors considered by the global economies when reshaping their energy systems. Here, we explore the potential roles of wind energy technology advancement in future global electricity generations, costs, and energy security. We use an integrated assessment model performing a series of technology advancement scenarios. The results show that double of the capital cost reduction causes 40% of generation increase and 10% of cost decrease on average in the long-term global wind electricity market. Today's technology advancement could bring us the benefit of increasing electricity production in the future 40-50 years, and decreasing electricity cost in the future 90-100 years. The technology advancement of wind energy can help to keep global energy security and stability. An aggressive development and deployment of wind energy could in the long-term avoid 1/3 of gas and 1/28 of coal burned, and keep 1/2 biomass and 1/20 nuclear fuel saved from the global electricity system. The key is that wind resources are free and carbon-free. The results of this study are useful in broad coverage ranges from innovative technologies and systems of renewable energy to the economic industrial and domestic use of energy with no or minor impact on the environment.

show abstract

“…Merrow, (1989), "An Analysis of Cost Improvement in Chemical Process Technologies," RAND R-3357-DOE. 74 E. Gummerman, C. Marnay (2004), "Learning and Cost Reductions for Generating Technologies in the National Energy Modeling System (NEMS)," LBNL-52559, Ernest Orlando Lawrence Berkeley National Laboratory, University of California Berkeley.…”

Section: Im-g Biofuels Distribution Infrastructurementioning

confidence: 99%

Bioenergy Technologies Office Multi-Year Program Plan: March 2015 Update

None¹

2015

View full text Add to dashboard Cite

The Bioenergy Technologies Office is one of the 10 technology development offices within the Office of Energy Efficiency and Renewable Energy at the U.S. Department of Energy. This Multi-Year Program Plan (MYPP) sets forth the goals and structure of the Bioenergy Technologies Office (the Office). It identifies the research, development, and demonstration (RD&D), and market transformation and crosscutting activities the Office will focus on over the next five years and outlines why these activities are important to meeting the energy and sustainability challenges facing the nation.This MYPP is intended for use as an operational guide to help the Office manage and coordinate its activities, as well as a resource to help communicate its mission and goals to stakeholders and the public. Bioenergy Technologies Office Mission and GoalsThe mission of the Office is to Develop and transform our renewable biomass resources into commercially viable, highperformance biofuels, bioproducts, and biopower through targeted research, development, and demonstration supported through public and private partnerships.The goal of the Office is to develop commercially viable bioenergy and bioproduct technologies to  Enable sustainable, nationwide production of biofuels that are compatible with today's transportation infrastructure, can reduce greenhouse gas emissions relative to petroleum-derived fuels, and can displace a share of petroleum-derived fuels to reduce U.S. dependence on foreign oil  Encourage the creation of a new domestic bioenergy and bioproduct industry.

show abstract

Learning and cost reductions for generating technologies in the national energy modeling system (NEMS)

Cited by 9 publications

References 7 publications

Applying Endogenous Learning Models in Energy System Optimization

Applying Endogenous Learning Models in Energy System Optimization

The impacts of wind technology advancement on future global energy

Bioenergy Technologies Office Multi-Year Program Plan: March 2015 Update

Contact Info

Product

Resources

About