Electrification and Decarbonization: Exploring U.S. Energy Use and Greenhouse Gas Emissions in Scenarios with Widespread Electrification and Power Sector Decarbonization

Steinberg, Daniel; Bielen, Dave; Eichman, Joshua; Newman, Alexandra M.; Logan, Jeffrey; Mai, Trieu; McMillan, Colin; Parker, Andrew; Vimmerstedt, Laura; Wilson, Eric

doi:10.2172/1372620

Cited by 79 publications

(63 citation statements)

References 20 publications

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“…Reference case or business-as-usual scenarios in capacity expansion models regularly include increasing shares of VRE (Cole et al 2016;EIA 2017a;. And, the models are often employed to analyze scenarios with significant changes, such as carbon reduction scenarios (EPA 2015a), rapid electrification (Steinberg et al 2017), alternate oil and gas resource and technology projections (EIA 2017a), and deep decarbonization scenarios (Bistline and Chesnaye 2017). The systems projected in even the reference scenarios can be substantially different from today's system.…”

Section: Determination Of the Resource Adequacy Levelmentioning

confidence: 99%

Variable Renewable Energy in Long-Term Planning Models: A Multi-Model Perspective

Cole

Frew

Trieu

et al. 2017

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Long-term capacity expansion models of the U.S. electricity sector have long been used to inform electric sector stakeholders and decision makers. With the recent surge in variable renewable energy (VRE) generators-primarily wind and solar photovoltaics-the need to appropriately represent VRE generators in these long-term models has increased. VRE generators are especially difficult to represent for a variety of reasons, including their variability, uncertainty, and spatial diversity. To assess current best practices, share methods and data, and identify future research needs for VRE representation in capacity expansion models, four capacity expansion modeling teams from the Electric Power Research Institute, the U.S. Energy Information Administration, the U.S. Environmental Protection Agency, and the National Renewable Energy Laboratory conducted two workshops of VRE modeling for national-scale capacity expansion models. The workshops covered a wide range of VRE topics, including transmission and VRE resource data, VRE capacity value, dispatch and operational modeling, distributed generation, and temporal and spatial resolution. The objectives of the workshops were both to better understand these topics and to improve the representation of VRE across the suite of models. Given these goals, each team incorporated model updates and performed additional analyses between the first and second workshops. This report summarizes the analyses and model "experiments" that were conducted as part of these workshops as well as the various methods for treating VRE among the four modeling teams. The report also reviews the findings and learnings from the two workshops. We emphasize the areas where there is still need for additional research and development on analysis tools to incorporate VRE into long-term planning and decision-making.

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Section: Determination Of the Resource Adequacy Levelmentioning

confidence: 99%

Variable Renewable Energy in Long-Term Planning Models: A Multi-Model Perspective

Cole

Frew

Trieu

et al. 2017

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“…Although this is a subset of policy and rate design, we separated it as its own use case. 36 For more information on the impact of electrification on the electricity system, see Deason et al (2018) and Steinberg et al (2017).…”

Section: Photovoltaics Planningmentioning

confidence: 99%

End-Use Load Profiles for the U.S. Building Stock: Market Needs, Use Cases, and Data Gaps

Frick

Wilson

Reyna

et al. 2019

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States and utilities are developing increasingly ambitious energy goals. Part of the solution to meeting these goals is improving electric grid flexibility. This includes shifting electric demand to align with grid needs. Thus, identifying and using building energy efficiency and other distributed energy resources to produce the highest grid value requires highly resolved, accurate and accessible electricity end-use load profiles (EULPs). EULPs quantify how and when energy is used. Currently, few accurate and accessible end-use load profiles are available for utilities, public utility commissions, state energy offices and other stakeholders to use to prioritize investment and value energy efficiency, demand response, distributed generation and energy storage. Highquality EULPs are also critical for determining the time-sensitive value of efficiency 1 and other distributed energy resources, and the widespread adoption of grid-interactive efficient buildings (GEBs). 2 For example, EULPs can be used to accurately forecast energy savings in buildings or identify energy activities that can be shifted to different times of the day. This report serves as the first-year deliverable for a multiyear U.S. Department of Energy-funded project, End-Use Load Profiles for the U.S. Building Stock, that intends to produce a set of highly resolved EULPs of the U.S. residential and commercial building stock. The project team, made up of researchers from the National Renewable Energy Laboratory (NREL), Lawrence Berkeley National Laboratory (LBNL), and Argonne National Laboratory, 3 ultimately will use calibrated physics-based building energy models to create these EULPs.

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“…In order to achieve these NDC targets, the parties are implementing measures such as improving carbon intensity, expansion of renewable energy sources, improving energy efficiency, and revising fuel consumption standards [15]. Decarbonization is required in power, transportation, industry, and building, in order to achieve NDC targets [16]. The power sector is expected to undergo the strongest decarbonization process when compared to other sectors [17].…”

Section: Global Trends In the Energy Industrymentioning

confidence: 99%

A Study on the Optimal Ratio of Research and Development Investment in the Energy Sector: An Empirical Analysis in South Korea

2019

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The necessity for energy research and development (ER&D) is increasing as greenhouse gases and climate change are becoming global issues. To support sustainable economic growth through ER&D, it is necessary to examine the optimal ratio of ER&D investment to total R&D investment to maximize economic growth. However, there are no studies on the appropriate level of ER&D in total R&D investment for economic growth. This study attempts to empirically estimate the optimal ratio of ER&D investment to maximize gross domestic product (GDP) in South Korea. We utilized the Cobb-Douglas production function for our econometric model and corrected the autocorrelation problem using the Cochrane-Orcutt iterative procedure. Our results showed that both, ER&D and non-ER&D have positive correlations with GDP. The optimal ratio of ER&D is derived as 13.23%, which indicates that the current ratio of Korean ER&D should be revised upward. Further, ER&D investment in the private sector needs to be increased to achieve the optimal ratio because the current statistics in Korea describes that private companies in the energy industry invest much less in R&D than the government. Based on the results, we suggest government strategies to enhance ER&D investment in the private sector as well as the public sector.

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Electrification and Decarbonization: Exploring U.S. Energy Use and Greenhouse Gas Emissions in Scenarios with Widespread Electrification and Power Sector Decarbonization

Abstract: NREL prints on paper that contains recycled content.

Cited by 79 publications

References 20 publications

Variable Renewable Energy in Long-Term Planning Models: A Multi-Model Perspective

Variable Renewable Energy in Long-Term Planning Models: A Multi-Model Perspective

End-Use Load Profiles for the U.S. Building Stock: Market Needs, Use Cases, and Data Gaps

A Study on the Optimal Ratio of Research and Development Investment in the Energy Sector: An Empirical Analysis in South Korea

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