The goal of the University of California Energy Institute (UCEI) is to foster research and educate students and policy makers on energy issues that are crucial to the future of California, the nation, and the world. UCEI focuses broadly on energy production and use, which are both essential to economic prosperity and a significant cause of environmental concerns. UCEI's objectives are to solve important energy problems, enrich UC faculty through the intellectual challenges inherent in these probelms, and increase research funding opportunities at the University. UCEI research covers the general areas of energy markets, resources and supply technologies, energy use efficiency, and the impacts of energy use on health, the environment, and the economy.
Severin Borenstein, Director
University of California Energy Institute
2547 Channing Way, #5180
Berkeley, CA 94720-5180
We describe the design, calibration, and deployment of a buoy and gas capture assembly for measuring bubbling gas flux in oceans and lakes. The assembly collects gas in a chamber while continuously measuring the position of the gas-water interface that forms as gas accumulates. Interface position is determined from the differential pressure between the chamber and ambient seawater. A spar buoy provides flotation and stability to reduce vertical motions due to surface waves. The gas collection assembly and spar, referred to as a flux buoy, is suitable for deployment from small boats under conditions of light wind and small waves. We are using the flux buoy to determine the spatial distribution of natural hydrocarbon seepage off the south-central California coast. Hydrocarbon seepage from continental shelves may be an important source of atmospheric methane.
Lean premixed prevaporized (LPP) gas turbine generators have become popular in energy conversion applications to meet strict emission requirements. Because the combustion process is very lean, combustion instabilities due to acoustic perturbations are more likely to occur than in a less lean fuel combustion process. Current design of damping strategies for mitigating these instabilities is often based on empirical trial and error, which precludes the possibility of determining an optimal configuration. A combustion system whose elements consist of flames, passive dampers, and ducts must be optimized to reduce or completely eliminate combustion instabilities. Hence, a modular simulation tool is developed to examine the interaction of plane acoustic waves with typical combustion system elements. The simulation tool represents these interactions in the form of transfer matrices, which can be modularly arranged for exploring a variety of configurations. In this work, a heuristic gain-delay flame model is represented as a transfer matrix, which can be used to test damping devices. Similarly, transfer matrices representing a Helmholtz resonator and a perforated liner with bias flow are developed, and preliminary results are obtained.
Hydrogen can serve as an energy carrier in a carbon-neutral system of energy production and use [1,2], but adequate hydrogen storage materials are still lacking in spite of many decades of investigations. In addition to being reversible and meeting stringent weight % and volume criteria, candidate materials must exhibit favorable kinetics for hydrogen uptake and release. The fundamental mechanisms of the (de)hydrogenation process have remained elusive to date. We have initiated a study of the relevant reactions, resulting in an identification of the dominant defect species involved in hydrogen transport in non-metallic hosts. While the concepts discussed here are general, we illustrate them with detailed first-principles results for sodium alanate. We identify hydrogen-related point defects as the essential mediators of hydrogen transport. A novel finding of this work is that the defects are positively or negatively charged, and hence their formation energies are Fermi-level dependent−an important feature that has not been recognized in past studies. This dependence enables us to explain why small amounts of transition-metal additives drastically alter the kinetics of dehydrogenation.
Private sector and governmental organizations have been promoting the deployment of small-scale, distributed electricity generation (DG) technologies for their many benefits as compared to the traditional paradigm of large, centralized power plants. While some researchers have investigated the impact of a shift toward DG in terms of energy use and even air pollutant concentrations, it is also important to evaluate the air pollutant exposure implications of this shift. We conducted a series of case studies within the state of California that combined air dispersion modeling and inhalation exposure assessment. Twenty-five central stations were selected and five air pollutant-emitting DG technologies were considered, including two that meet the 2003 and 2007 California Air Resources Board DG emissions standards (microturbines and fuel cells with on-site natural gas reformers, respectively). This investigation has revealed that the fraction of pollutant mass emitted that is inhaled by the downwind, exposed population can be more than an order of magnitude greater for all five DG technologies considered than for large, central-station power plants in California. This difference is a consequence mainly of the closer proximity of DG sources to densely populated areas as compared to typical central station, and is independent of the emissions characteristics of the plants assessed. Considering typical emission factors for the five DG technologies, the mass of pollutant inhaled per unit electricity delivered can be up to three orders of magnitude greater for DG units as compared to existing California central stations. To equalize the exposure burden between DG and central station technologies, DG emission factors will need to be reduced to a range between the level of the cleanest, new central stations in California and an order of magnitude below those levels, depending on the pollutant and siting. We conclude that there is reason to caution against an unmitigated embrace of DG technologies that emit air pollutants so that they do not pose a greater public health burden than the current electricity generation system.
Energy efficiency has recently come to the forefront of energy debates, especially in the state of California. This focus on efficiency has been driven by the deregulation of electrical-energy distribution, the increasing price of electricity, and the implementation of rolling blackouts. Currently, buildings consume over 1/3 of primary energy, and 2/3 of all electricity produced in the U.S. Commercial buildings consume roughly half of this, and lighting is responsible for approximately 40% of commercial building energy use. These numbers indicate that research in lighting efficiency has great potential to positively impact energy efficiency.
Efficient lighting controls proven to save up to 45% in electricity consumption are commercially available. In practice however, these systems are poorly received and greatly under-leveraged, resulting in a missed opportunity for impressive energy savings. Accordingly, we proposed the three-phase extension of an intelligent decision framework that addresses two major shortcomings of today’s energy-efficient lighting controls – user satisfaction, and lost energy savings stemming from naïve decision algorithms. The first phase of research was directed at enhancement of an existing preference-balancing control algorithm, in order that it accommodate demand-responsive control as well as the desires of the building manager. The second phase was devoted to identifying user preferences through empirical occupant testing. In the third phase, the resulting algorithms were simulated and evaluated.
Several facilities managers were interviewed and surveyed in order to identify appropriate variables and control policies to represent their desires within the decision algorithm. The preferred demand response strategy was found to be specific to the particular manager. Across all managers, energy was the most commonly selected indicator of the quality of lighting decisions. Automated occupant preference testing was conducted to demonstrate the feasibility of collecting such data in office environments, and to provide realistic occupant perceptions for use in simulation.
Simulated results indicate that the intelligent decision algorithm and framework present a promising control paradigm, and should be further expanded for the explicit inclusion of solar variables. Preliminary assessment showed that energy pricing can be factored into the control algorithm without significantly compromising occupant perceptions of lighting quality. Further energy savings are garnered by curtailing consumption during times of elevated pricing. Provided that curtailment is implemented with a slow enough dimming rate, reductions of up to 30% in illuminance are detected by roughly half of all occupants. Leveraging this research, the intelligent controller implements the specific demand response policy chosen by the facilities manager.
This paper presents a motor-integrated transmission mechanism for use in parallel hybrid electric vehicles. The transmission can provide five basic modes of operation that can be further classified into sixteen sub-modes: one electric motor mode, four engine modes, four engine/charge modes, three power modes, and four regenerative braking modes. Each of these sub-modes can be grouped into like clutching conditions, providing the functional appearance of a conventional 4-speed automatic transmission, with electric launch, engine-only, engine/charge, power-assist, and regeneration capability. CVT capability is provided with one of the engine/charge modes. The kinematics, static torque, and power flow relations for each mode are analyzed in detail. Finally, a notional control strategy is developed. The transmission can be incorporated not only in front-wheel drive but also in rear-wheel drive vehicles. The compactness, mechanical simplicity, and operational flexibility of the transmission make it an excellent candidate for future hybrid electric vehicles.
This paper develops an analytical framework to assess the second-best optimal level of gasoline taxation taking into account unpriced pollution, congestion, and accident externalities, as well as interactions with the broader fiscal system. We provide calculations of the optimal taxes for the US and the UK under a variety of parameter scenarios.
Under our central parameter values, the second-best optimal gasoline tax is $1.01/gal for the US and $1.34/gal for the UK. Current tax rates are much lower than this in the US and higher in the UK. The calculations are moderately sensitive to alternative parameter assumptions. The congestion externality is the largest component in both nations; revenue-raising needs also play a significant role, as do accident externalities and local air pollution.
Potential welfare gains from reducing the current UK tax rate are estimated at nearly one-fourth the production cost of all gasoline used in the UK. Even larger gains could be achieved by switching to a tax on vehicle miles with equal revenue yield. For the US, the welfare gains from optimizing the gasoline tax are smaller, but those from switching to an optimal tax on vehicle miles are very large.
How Good are Supply Function Equilibrium Models: An Empirical Analysis of the ERCOT Balancing Market
We present an empirical analysis of a supply function equilibrium model in the Texas spot electricity market. We derive condititions for optimal bidding behavior in a spot market with ex ante bilaterally contracted sales. By using generation cost information, we are able to derive a set of ex post- and ex ante-optimal supply functions and use a nonparametric model of firm behavior to compare our theoretically-optimal supply functions to actual offers made in years 2002 and 2003. Our results show that with markups and markdowns far in excess of what a model of profit-maximizing behavior suggests. For small generators, municipalities, and cogenerators we find evidence suggesting these firms may be acting to exclude themselves from the market by economically witholding their generation.By using partial-linear behavior model we demonstrate some learning effects to have taken place during the first quarter of 2002.
The Effect of Improved Fuel Economy on Vehicle Miles Traveled: Estimating the Rebound Effect Using U.S. State Data, 1966-2001
We estimate the rebound effect for motor vehicles, by which improved fuel efficiency causes additional travel, using a panel of US states for 1966-2001. Our model accounts for endogenous changes in fuel efficiency, distinguishes between autocorrelation and lagged effects, includes a measure of the stringency of fuel-economy standards, and interacts the rebound effect with income. At sample averages of variables, our 3SLS estimates of the short- and long-run rebound effect are 4.7% and 22.0%. But they decline substantially with income: with variables at 1997-2001 levels they become 2.6% and 12.1%, considerably smaller than typically assumed for policy analysis.