Energy Transition

Analyzing the Regional Impact of a Fossil Energy Cap in China

Decoupling fossil energy demand from economic growth is crucial to China’s sustainable development. In addition to energy and carbon intensity targets enacted under the Twelfth Five-Year Plan (2011–2015), a coal or fossil energy cap is under discussion as a way to constrain the absolute quantity of energy used. Importantly, implementation of such a cap may be compatible with existing policies and institutions. We evaluate the efficiency and distributional implications of alternative energy cap designs using a numerical general equilibrium model of China’s economy, built on the 2007 regional input-output tables for China and the Global Trade Analysis Project global data set. We find that a national cap on fossil energy implemented through a tax on final energy products and an energy saving allowance trading market is the most costeffective design, while a regional coal-only cap is the least cost-effective design. We further find that a regional coal cap results in large welfare losses in some provinces. Capping fossil energy use at the national level is found to be nearly as cost effective as a national CO2 emissions target that penalizes energy use based on carbon content.

Applying Engineering and Fleet Detail to Represent Passenger Vehicle Transport in a Computable General Equilibrium Model

A well-known challenge in computable general equilibrium (CGE) models is to maintain correspondence between the forecasted economic and physical quantities over time. Maintaining such a correspondence is necessary to understand how economic forecasts reflect, and are constrained by, relationships within the underlying physical system. This work develops a method for projecting global demand for passenger vehicle transport, retaining supplemental physical accounting for vehicle stock, fuel use, and greenhouse gas (GHG) emissions. This method is implemented in the MIT Emissions Prediction and Policy Analysis Version 5 (EPPA5) model and includes several advances over previous approaches. First, the relationship between per-capita income and demand for passenger vehicle transport services (in vehicle-miles traveled, or VMT) is based on econometric data and modeled using quasi-homothetic preferences. Second, the passenger vehicle transport sector is structured to capture opportunities to reduce fleet-level gasoline use through the application of vehicle efficiency or alternative fuel vehicle technologies, introduction of alternative fuels, or reduction in demand for VMT. Third, alternative fuel vehicles (AFVs) are introduced into the EPPA model. Fixed costs as well as learning effects that could affect the rate of AFV introduction are captured explicitly. This model development lays the foundation for assessing policies that differentiate based on vehicle age and efficiency, alter the relative prices of fuels, or focus on promoting specific advanced vehicle or fuel technologies.
 

Assessing Early Investments in Low Carbon Technologies under Uncertainty: The Case of Carbon Capture and Storage

Climate change is a threat that could be mitigated by introducing new energy technologies into the electricity market that emit fewer greenhouse gas (GHG) emissions. We face many uncertainties that would affect the demand for each of these technologies in the future. The costs of these technologies decrease due to learning-by-doing as their capacity is built out. Given that we face uncertainties over future energy demands for particular technologies, and that costs reduce with experience, an important question that arises is whether policy makers should encourage early investments in technologies before they are economically competitive, so that they could be available in the future at lower cost should they be needed. If society benefits from early investments when future demands are uncertain, then there is an option value to investing today. This question of whether option values exist is investigated by focusing on Coal-fired Power Plants with Carbon Capture and Storage (CCS) as a case study of a new high-cost energy technology that has not yet been deployed at commercial scale.

A decision analytic framework is applied to the MIT Emissions Prediction Policy Analysis (EPPA) model, a computable general equilibrium model that captures the feedback effects across different sectors of the economy, and measures the costs of meeting emissions targets. Three uncertainties are considered in constructing a decision framework: the future stringency of the US GHG emissions policy, the size of the US gas resource, and the cost of electricity from Coal with CCS. The decision modeled is whether to begin an annual investment schedule in Coal with CCS technology for 35 years. Each scenario in the decision framework is modeled in EPPA, and the output measure of welfare is used to compare the welfare loss to society of meeting the emissions target for each case. The decision framework is used to find which choice today, whether to invest in CCS or not, gives the smallest welfare cost and is therefore optimal for society. Sensitivity analysis on the probabilities of the three uncertainties is carried out to determine the conditions under which CCS investment is beneficial, and when it is not.

The study finds that there are conditions, specified by ranges in probabilities for the uncertainties, where early investment in CCS does benefit society. The results of the decision analysis demonstrate that the benefits of CCS investment are realized in the latter part of the century, and so the resulting optimal decision depends on the choice of discount rate. The higher the rate, the smaller the benefit from investment until a threshold is reached where choosing to invest becomes the more costly decision. The decision of whether to invest is more sensitive to some uncertainties investigated than others. Specifically, the size of the US gas resource has the least impact, whereas the stringency of the future US GHG emissions policy has the greatest impact.

This thesis presents a new framework for considering investments in energy technologies before they are economically competitive. If we can make educated assumptions as to the real probabilities we face, then extending this framework to technologies beyond CCS and expanding the decision analysis, would allow policymakers to induce investment in energy technologies that would enable us to meet our emissions targets at the lowest cost possible to society.

Beyond 2020 - Strategies and Costs for Transforming the European Energy System

The Energy Modeling Forum 28 (EMF28) study systematically explores the energy system transition required to meet the European goal of reducing greenhouse gas (GHG) emissions by 80% by 2050. The 80% scenario is compared to a reference case that aims to achieve a 40% GHG reduction target. The paper investigates mitigation strategies beyond 2020 and the interplay between different decarbonization options. The models present different technology pathways for the decarbonization of Europe, but a common finding across the scenarios and models is the prominent role of energy efficiency and renewable energy sources. In particular, wind power and bioenergy increase considerably beyond current deployment levels. Up to 2030, the transformation strategies are similar across all models and for both levels of emission reduction. However, mitigation becomes more challenging after 2040. With some exceptions, our analysis agrees with the main findings of the “Energy Roadmap 2050” presented by the European Commission.

© 2013 the authors

Characterization of the Solar Power Resource in Europe and Assessing Benefits of Co-Location with Wind Power Installations

The extent, availability and reliability of solar power generation are assessed over Europe, and—following a previously developed methodology—special attention is given to the intermittency of solar power. Combined with estimates of wind power resource over Europe from a companion assessment, we assess the benefits of co-location of solar and wind power installations, particularly with respect to aggregate power generation and local mitigation of intermittency. Consistent with previous studies, our results show that the majority of solar potential is found in southern Europe, which also displays the strongest availability. We also found that higher latitude locations, around central Europe, benefit from medium to high solar power during the warm season. If a region’s availability of solar power is sufficient—as determined by a minimum technological threshold for photovoltaic extraction— it possesses the potential to reduce intermittency by aggregation and interconnection. We find these conditions occurring to a moderate extent over mainland central Europe. Finally, the result of co location of wind and solar power is increased power availability over the whole continent, especially in central Europe where neither resource is strong. In terms of local intermittency mitigation, the regions that benefit most are the Mediterranean and Baltic countries.

Characterization of the Wind Power Resource in Europe and its Intermittency

Wind power is assessed over Europe, with special attention given to the quantification of intermittency.  Using the methodology developed in Gunturu and Schlosser (2011), the MERRA boundary flux data was used to compute wind power density profiles over Europe. Besides of the analysis of capacity factor, other metrics are presented to further quantify the availability and reliability of this resource and the extent to which wind-power intermittency is coincident across Europe. The analyses find that, consistent with previous studies, the majority of European wind power resources are located offshore. The largest  wind power resources at onshore locations are found to be over Iceland, the United Kingdom, and along the northern coastlines of continental Europe. Other isolated pockets of higher wind power are found over Spain and along the Mediterranean coast of France. Overall, the availability of onshore wind power is low and is highly intermittent, while offshore locations show a high degree of persistence. However, for the strongest onshore locations of wind power—primarily over northern coastlines as well as the United Kingdom and Iceland—the evidence indicates that intermittency can be reduced by aggregation and interconnection of wind-power installations. 

Characterization of wind power resource in Australia

The wind resource in Australia has been reconstructed and characterized in terms of its geographical distribution, abundance, variability, availability, persistence and intermittency. The impact of raising the wind turbine hub height on these metrics is analyzed. The Modern Era Retrospective Analysis for Research and Applications (MERRA) boundary layer flux data was used to construct wind power density (WPD) and wind speed at 50 m, 80 m, 100 m, and 150 m, which represent current and potential wind turbine hub heights. The wind speeds at 80 m were quantitatively and spatially similar to a map of wind sp

Characterization of wind power resource in the United States

Wind resource in the continental and offshore United States has been reconstructed and characterized using metrics that describe, apart from abundance, its availability, persistence and intermittency. The Modern Era Retrospective-Analysis for Research and Applications (MERRA) boundary layer flux data has been used to construct wind profile at 50 m, 80 m, 100 m, 120 m turbine hub heights. The wind power density (WPD) estimates at 50 m are qualitatively similar to those in the US wind atlas developed by the National Renewable Energy Laboratory (NREL), but quantitatively a class less in some regions, but are within the limits of uncertainty. The wind speeds at 80 m were quantitatively and qualitatively close to the NREL wind map. The possible reasons for overestimation by NREL have been discussed. For long tailed distributions like those of the WPD, the mean is an overestimation and median is suggested for summary representation of the wind resource.

The impact of raising the wind turbine hub height on metrics of abundance, persistence, variability and intermittency is analyzed. There is a general increase in availability and abundance of wind resource but there is an increase in intermittency in terms of level crossing rate in low resource regions.

© 2012 the Authors

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