Walter Short

Scenarios for a clean energy future

Abstract This paper summarizes the results of a study—Scenarios for a Clean Energy Future—that assess how energy-efficient and clean energy technologies can address key energy and environmental challenges facing the US. A particular focus of this study is the energy, environmental, and economic impacts of different public policies and programs. Hundreds of technologies and approximately 50 policies are analyzed. The study concludes that policies exist that can significantly reduce oil dependence, air pollution, carbon emissions, and inefficiencies in energy production and end-use systems at essentially no net cost to the US economy. The most advanced scenario finds that by the year 2010, the US could bring its carbon dioxide emissions three-quarters of the way back to 1990 levels. The study also concludes that over time energy bill savings in these scenarios can pay for the investments needed to achieve these reductions in energy use and associated greenhouse gas emissions.

Evaluating the Impacts of Real-Time Pricing on the Usage of Wind Generation

One of the impediments to large-scale use of wind generation within power systems is its nondispatchability and variable and uncertain real-time availability. Operating constraints on conventional generators such as minimum generation points, forbidden zones, and ramping limits as well as system constraints such as power flow limits and ancillary service requirements may force a system operator to curtail wind generation in order to ensure feasibility. Furthermore, the pattern of wind availability and electricity demand may not allow wind generation to be fully utilized in all hours. One solution to these issues, which could reduce these inflexibilities, is the use of real-time pricing (RTP) tariffs which can both smooth-out the diurnal load pattern in order to reduce the impact of binding unit operating and system constraints on wind utilization, and allow demand to increase in response to the availability of costless wind generation. We use and analyze a detailed unit commitment model of the Texas power system with different estimates of demand elasticities to demonstrate the potential increases in wind generation from implementing RTP.

Electricity sector analysis in the clean energy futures study

Abstract This paper examines the impact of policies to reduce carbon and other air emissions in the electric sector. The analysis is from a recent scenario development effort, Scenarios for a Clean Energy Future (CEF), by five National Laboratories. The CEF assesses how policies can be used to promote energy-efficient and clean energy technologies to address key energy and environmental challenges facing the United States. The impact of policies in the electric sector is evaluated using the CEF-NEMS model, which is derived from the National Energy Modeling System (NEMS) model developed by the DOE Energy Information Administration. The analysis shows that by 2020 under the policies analyzed, CO2 and other emissions can be substantially reduced by moving from coal to advanced gas combined cycle systems and renewable energy. Prices show little change and may drop due to decreased end-use demands.

Comparative Analysis of Three Proposed Federal Renewable Electricity Standards

This paper analyzes potential impacts of proposed national renewable electricity standard (RES) legislation. An RES is a mandate requiring certain electricity retailers to provide a minimum share of their electricity sales from qualifying renewable power generation. The analysis focuses on draft bills introduced individually by Senator Jeff Bingaman and Representative Edward Markey, and jointly by Representative Henry Waxman and Markey. The analysis uses NRELs Regional Energy Deployment System (ReEDS) model to evaluate the impacts of the proposed RES requirements on the U.S. energy sector in four scenarios.

Opportunities for the integration of intermittent renewable resources into networks using existing storage

The absorption of energy from intermittent resources (IR) into the existing storage in a system is described. A subsequent reshaping of this energy to sell it as firm energy is possible by exploiting the diversity in demands and generations. An algorithm to optimize the installations of IR to maximize firm sales is presented.

A sensitivity analysis of the treatment of wind energy in the AEO99 version of NEMS

Each year, the U.S. Department of Energys Energy Information Administration (EIA) publishes a forecast of the domestic energy economy in the Annual Energy Outlook (AEO). During the forecast period of the AEO (currently through 2020), renewable energy technologies have typically not achieved significant growth. The contribution of renewable technologies as electric generators becomes more important, however, in scenarios analyzing greenhouse gas emissions reductions or significant technological advancements. We examined the economic assumptions about wind power used for producing forecasts with the National Energy Modeling System (NEMS) to determine their influence on the projected capacity expansion of this technology. This analysis should help illustrate to policymakers what types of issues may affect wind development, and improve the general understanding of the NEMS model itself. Figure 1 illustrates the model structure and factors relevant to wind deployment. We found that NEMS uses various cost multipliers and constraints to represent potential physical and economic limitations to growth in wind capacity, such as resource depletion, costs associated with rapid manufacturing expansion, and grid stability with high levels of capacity from intermittent resources. The models flexibility allows the user to make alternative assumptions about the magnitude of these factors. While these assumptions have little effect on the Reference Case forecast for the 1999 edition of the AEO, they can make a dramatic difference when wind is more attractive, uch as under a carbon permit trading system. With

Renewable Energy and Efficiency Modeling Analysis Partnership: An Analysis of How Different Energy Models Addressed a Common High Renewable Energy Penetration Scenario in 2025

100/ton carbon permits, the wind capacity projection for 2020 ranges from 15 GW in the unaltered model (AEO99 Reference Case) to 168 GW in the extreme case when all the multipliers and constraints examined in this study are removed. Furthermore, if modifications are made to the model allowing inter-regional transmission of electricity, wind capacity is forecast to reach 214 GW when all limitations are removed. The figures in the upper end of these ranges are not intended to be viewed as reasonable projections, but their magnitude illustrates the importance of the parameters governing the growth of wind capacity and resource availability in forecasts using NEMS. In addition, many uncertainties exist regarding these assumptions that potentially affect the growth of wind power. We suggest several areas in which to focus future research in order to better model the potential development of this resource. Because many of the assumptions related to wind in the model are also used for other renewable technologies, these suggestions could be applied to other renewable resources as well.

Renewable Energy Load Matching for Continental U.S.

Energy system modeling can be intentionally or unintentionally misused by decision-makers. This report describes how both can be minimized through careful use of models and thorough understanding of their underlying approaches and assumptions. The analysis summarized here assesses the impact that model and data choices have on forecasting energy systems by comparing seven different electric-sector models. This analysis was coordinated by the Renewable Energy and Efficiency Modeling Analysis Partnership (REMAP), a collaboration among governmental, academic, and nongovernmental participants.

Correlations Between Geographically Dispersed Concentrating Solar Power and Demand in the United States

The variability of wind and solar energy technologies is perceived as a major obstacle to employing otherwise abundant renewable energy resources. Based on the available geographically dispersed data for the continental U.S. (excluding Alaska), we analyze to what extent the geographic diversity of these resources can offset their variability. We determine the best match to loads that can be achieved with wind power and photovoltaics with no transmission limitations. Without storage, wind and PV can meet up to 50% of loads in Western US. It is beneficial to build more wind than PV mostly because the wind contributes at night. When storage is available, the optimal mix has almost 75% as much nominal PV capacity as wind, with the PV energy contribution being 32% of the electricity produced from wind. With only 40 GW of storage (twice the pumped hydro storage capacity that already exists in the continental US), up to 82% of the load can be matched with wind and PV, while at the same time curtailing less than 10% of the renewable energy throughout the year.© 2011 ASME

Regional Energy Deployment System (ReEDS)

Correlations between the electricity generated by concentrating solar thermal power (CSP) plants, as well as cross-correlations between CSP, wind power and electricity demand, have significant impacts on decisions for how much and where to build utility-scale CSP capacity, the optimal amount of thermal storage in the CSP plants, reserve capacity needed to back-up the system, as well as the expected levels of curtailed renewable power. Accurately estimating these correlations is vital to performing detailed analyses of high renewable penetration scenarios. This study quantifies the degree of correlation between geographically dispersed CSP, as well as the correlation between CSP and wind power, and CSP and electricity demand in 356 discrete regions in the contiguous US. Correlations are calculated using hourly data on an annual basis. Maps of the correlations will be presented to illustrate the degree of correlation between solar power and the demand it is serving, as well as the synergies between the negatively-correlated wind power and solar power serving the same region.Copyright