Trieu Mai

Modeling low-carbon US electricity futures to explore impacts on national and regional water use

The US electricity sector is currently responsible for more than 40% of both energy-related carbon dioxide emissions and total freshwater withdrawals for power plant cooling (EIA 2012a Annual Energy Outlook 2012 (Washington, DC: US Department of Energy), Kenny et?al 2009 Estimated Use of Water in the United States 2005 (US Geological Survey Circular vol 1344) (Reston, VA: US Geological Survey)). Changes in the future electricity generation mix in the United States will have important implications for water use, particularly given the changing water availability arising from competing demands and climate change and variability. However, most models that are used to make long-term projections of the electricity sector do not have sufficient regional detail for analyzing water-related impacts and informing important electricity-?and water-related decisions. This paper uses the National Renewable Energy Laboratory?s Regional Energy Deployment System (ReEDS) to model a range of low-carbon electricity futures nationally that are used to calculate changes in national water use (a sample result, on water consumption, is included here). The model also produces detailed sub-regional electricity results through 2050 that can be linked with basin-level water modeling. The results will allow for sufficient geographic resolution and detail to be relevant from a water management perspective.

Bright Future: Solar Power as a Major Contributor to the U.S. Grid

The decreased costs of solar technologies have led to the prospect of a move for photovoltaic (PV ) and concentrating solar power (CSP ) from niche applications to major contributors to the U.S. electricity grid. This development has motivated a number of technoeconomic analyses of the potential deployment of both PV and CSP under varying economic conditions. Two studies sponsored by the U.S. Department of Energy (DOE ) and completed in 2012 can help us understand the potential opportunities and challenges for solar deployment on a large scale. These studies evaluated both the potential mix of renewable energy technologies that could serve a large fraction of the U.S. electricity demand and the associated evolution of the U.S. grid to 2050.

Renewable Electricity Futures for the United States

This paper highlights the key results from the Renewable Electricity (RE) Futures Study. It is a detailed consideration of renewable electricity in the United States. The paper focuses on technical issues related to the operability of the U.S. electricity grid and provides initial answers to important questions about the integration of high penetrations of renewable electricity technologies from a national perspective. The results indicate that the future U.S. electricity system that is largely powered by renewable sources is possible and the further work is warranted to investigate this clean generation pathway. The central conclusion of the analysis is that renewable electricity generation from technologies that are commercially available today, in combination with a more flexible electric system, is more than adequate to supply 80% of the total U.S. electricity generation in 2050 while meeting electricity demand on an hourly basis in every region of the United States.

Renewable Electricity Futures Study. Executive Summary

The Renewable Electricity Futures (RE Futures) Study investigated the challenges and impacts of achieving very high renewable electricity generation levels in the contiguous United States by 2050. The analysis focused on the sufficiency of the geographically diverse U.S. renewable resources to meet electricity demand over future decades, the hourly operational characteristics of the U.S. grid with high levels of variable wind and solar generation, and the potential implications of deploying high levels of renewables in the future. RE Futures focused on technical aspects of high penetration of renewable electricity; it did not focus on how to achieve such a future through policy or other measures. Given the inherent uncertainties involved with analyzing alternative long-term energy futures as well as the multiple pathways that might be taken to achieve higher levels of renewable electricity supply, RE Futures explored a range of scenarios to investigate and compare the impacts of renewable electricity penetration levels (30%-90%), future technology performance improvements, potential constraints to renewable electricity development, and future electricity demand growth assumptions. RE Futures was led by the National Renewable Energy Laboratory (NREL) and the Massachusetts Institute of Technology (MIT).

Renewable Electricity Futures Study. Volume 1. Exploration of High-Penetration Renewable Electricity Futures

The Renewable Electricity Futures (RE Futures) Study investigated the challenges and impacts of achieving very high renewable electricity generation levels in the contiguous United States by 2050. The analysis focused on the sufficiency of the geographically diverse U.S. renewable resources to meet electricity demand over future decades, the hourly operational characteristics of the U.S. grid with high levels of variable wind and solar generation, and the potential implications of deploying high levels of renewables in the future. RE Futures focused on technical aspects of high penetration of renewable electricity; it did not focus on how to achieve such a future through policy or other measures. Given the inherent uncertainties involved with analyzing alternative long-term energy futures as well as the multiple pathways that might be taken to achieve higher levels of renewable electricity supply, RE Futures explored a range of scenarios to investigate and compare the impacts of renewable electricity penetration levels (30%-90%), future technology performance improvements, potential constraints to renewable electricity development, and future electricity demand growth assumptions. RE Futures was led by the National Renewable Energy Laboratory (NREL) and the Massachusetts Institute of Technology (MIT).

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

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.

Implications of Model Structure and Detail for Utility Planning: Scenario Case Studies Using the Resource Planning Model

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. formerly) deserves our special gratitude for his thought leadership and insights at the inception of the Resource Planning Model. Any remaining errors or omissions are the sole responsibility of the authors. Abstract Capacity expansion models are computational tools designed to find the least cost option for planning a system under a variety of policy, business, and operational constraints. In this report, we analyze the impacts of model configuration and detail on resource selection decisions of capacity expansion models. Our analysis focuses on the importance of model configurations— particularly those related to capacity credit, dispatch modeling, and transmission modeling—to the construction of scenario futures. Our analysis is primarily directed toward advanced tools used for utility planning and those impacts that are most relevant to decisions about future renewable capacity deployment. To serve this purpose, we develop and employ the National Renewable Energy Laboratorys Resource Planning Model to conduct a case study analysis that explores 11 capacity expansion model configuration scenarios for the Western Interconnection through 2030. While the analysis results cover the entire Western Interconnection, the model and research examine in greater detail a region within the interconnection that consists of two balancing areas—the Public Service Company of Colorado and the Western Area Power Administration Colorado/Missouri—that serve load primarily in and around the state of Colorado. We examine how model investment decisions change under different model configurations and assumptions related to renewable capacity credit, the inclusion or exclusion of operating reserves, dispatch period sampling, transmission power flow modeling, renewable spur line costs, and the ability of a planning region to import and export power. For all modeled scenarios, we …

Potential Role of Concentrating Solar Power in Enabling High Renewables Scenarios in the United States

This work describes the analysis of concentrating solar power (CSP) in two studies -- The SunShot Vision Study and the Renewable Electricity Futures Study -- and the potential role of CSP in a future energy mix.

Implications of a PTC Extension on U.S. Wind Deployment

This analysis explores the potential effects of wind production tax credit expiration and various extension scenarios on future wind deployment with the Regional Energy Deployment System (ReEDS), a model of the U.S. electricity sector. The analysis does not estimate the potential implications on government tax revenue associated with the PTC. Key findings include: Under a scenario in which the PTC is not extended and all other policies remain unchanged, wind capacity additions are expected to be between 3 and 5 GW per year from 2013-2020; PTC extension options that ramp-down from the current level to zero-credit by year-end 2022 appear to be insufficient to support deployment at the recent historical average; Extending the PTC at its historical level may provide the best opportunity to support deployment consistent with recent levels across a range of potential market conditions; it therefore may also provide the best opportunity to sustain wind power installation and manufacturing sector at current levels.

Methodology for Clustering High-Resolution Spatiotemporal Solar Resource Data

In this report, we introduce a methodology to achieve multiple levels of spatial resolution reduction of solar resource data, with minimal impact on data variability, for use in energy systems modeling. The selection of an appropriate clustering algorithm, parameter selection including cluster size, methods of temporal data segmentation, and methods of cluster evaluation are explored in the context of a repeatable process. In describing this process, we illustrate the steps in creating a reduced resolution, but still viable, dataset to support energy systems modeling, e.g. capacity expansion or production cost modeling. This process is demonstrated through the use of a solar resource dataset; however, the methods are applicable to other resource data represented through spatiotemporal grids, including wind data. In addition to energy modeling, the techniques demonstrated in this paper can be used in a novel top-down approach to assess renewable resources within many other contexts that leverage variability in resource data but require reduction in spatial resolution to accommodate modeling or computing constraints.