Annual Energy Outlook 2008 With Projections to 2030

ライブラリー Annual Energy Outlook 2000

This study addresses the greatest concern facing the large-scale integration of wind, water, and solar (WWS) into a power grid: the high cost of avoiding load loss caused by WWS variability and uncertainty. It uses a new grid integration model and finds low-cost, no-load-loss, nonunique solutions to this problem on electrification of all US energy sectors (electricity, transportation, heating/cooling, and industry) while accounting for wind and solar time series data from a 3D global weather model that simulates extreme events and competition among wind turbines for available kinetic energy. Solutions are obtained by prioritizing storage for heat (in soil and water); cold (in ice and water); and electricity (in phase-change materials, pumped hydro, hydropower, and hydrogen), and using demand response. No natural gas, biofuels, nuclear power, or stationary batteries are needed. The resulting 2050–2055 US electricity social cost for a full system is much less than for fossil fuels. These results hold for many conditions, suggesting that low-cost, reliable 100% WWS systems should work many places worldwide.

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Low-cost solution to the grid reliability problem with 100% penetration of intermittent wind, water, and solar for all purposes

The Role of Firm Low-Carbon Electricity Resources in Deep Decarbonization of Power Generation

Wind Vision: A New Era for Wind Power in the United States

http://gcep.stanford.edu/pdfs/events/workshops/Mai_2015-04-01%20GCEP%20Net%20Energy%20-%20public.pdf

Envisioning a renewable electricity future for the United States

This report describes a geospatial analysis method to estimate the economic potential of several renewable resources available for electricity generation in the United States. Economic potential, one measure of renewable generation potential, may be defined in several ways. For example, one definition might be expected revenues (based on local market prices) minus generation costs, considered over the expected lifetime of the generation asset. Another definition might be generation costs relative to a benchmark (e.g., a natural gas combined cycle plant) using assumptions of fuel prices, capital cost, and plant efficiency. Economic potential in this report is defined as the subset of the available resource technical potential where the cost required to generate the electricity (which determines the minimum revenue requirements for development of the resource) is below the revenue available in terms of displaced energy and displaced capacity. The assessment is conducted at a high geospatial resolution (more than 150,000 technology-specific sites in the continental United States) to capture the significant variation in local resource, costs, and revenue potential. This metric can be a useful screening factor for understanding the economic viability of renewable generation technologies at a specific location. In contrast to many common estimates of renewable energy potential,more » economic potential does not consider market dynamics, customer demand, or most policy drivers that may incent renewable energy generation.« less

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Estimating Renewable Energy Economic Potential in the United States: Methodology and Initial Results

This report is one of several products resulting from an initial effort to provide a consistent set of technology cost and performance data and to define a conceptual and consistent scenario framework that can be used in the National Renewable Energy Laboratory’s (NREL’s) future analyses. The long-term objective of this effort is to identify a range of possible futures of the U.S. electricity sector in which to consider specific energy system issues through (1) defining a set of prospective scenarios that bound ranges of key technology, market, and policy assumptions and (2) assessing these scenarios in NREL’s market models to understand the range of resulting outcomes, including energy technology deployment and production, energy prices, and carbon dioxide (CO2) emissions.

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2015 Standard Scenarios Annual Report: U.S. Electric Sector Scenario Exploration

NOTICE This report was prepared as an account of work sponsored by an agency of the United States government. Neither the United States government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States government or any agency thereof. Acknowledgments The Biomass Scenario Model development and analysis is sponsored by the U.S. Department of Energy (DOE) Biomass Program, under DE-AC36-08GO28308. We thank Zia Haq and Sheila Moynihan of DOE for supporting this effort. We also thank Mary Lukkonen and Scott Gossett of the National Renewable Energy Laboratory (NREL) for assistance in editing the manuscript; The Biomass Scenario Model (BSM) is a system dynamics model developed by the U.S. Department of Energy (DOE) as a tool to better understand the interaction of complex policies and their potential effects on the biofuels industry in the United States. However, it does not currently have the capability to account for allocation of biomass resources among the various end uses, which limits its utilization in analysis of policies that target biomass uses outside the biofuels industry. This report provides a more holistic understanding of the dynamics surrounding the allocation of biomass among uses that include traditional use, wood pellet exports, bio-based products and bioproducts, biopower, and biofuels by (1) highlighting the methods used in existing models' treatments of competition for biomass resources; (2) identifying coverage and gaps in industry data regarding the competing end uses; and (3) exploring options for developing models of biomass allocation that could be integrated with the BSM to actively exchange and incorporate relevant information. The review of existing models (Section 2) provides an overview on how competing demands for biomass are currently represented; these models do not include the dynamic interaction among biomass end users as a primary focus. There are models that specifically deal with the competition for biomass, but they are predominantly optimization models either …

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David Mulcahy

Papers

Envisioning a renewable electricity future for the United States

Estimating Renewable Energy Economic Potential in the United States: Methodology and Initial Results

2015 Standard Scenarios Annual Report: U.S. Electric Sector Scenario Exploration

Biomass Resource Allocation among Competing End Uses

2015 Standard Scenarios Annual Report: U.S. Electric Sector Scenario Exploration and ReEDS Model Description