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Accelerators for heavy-ion inertial fusion energy (HIF) have an economic incentive to fit beam tubes tightly to beams, putting them at risk from electron clouds produced by emission of electrons and gas from walls. Theory and PIC simulations suggest that the electrons will be radially trapped in the /spl ges/1 kV ion-beam potential. We are beginning studies on the High-Current Experiment (HCX) with unique capabilities to characterize electron production and trapping, the effects on ion beams, and mitigation techniques. We are measuring the flux of electrons and gas evolved from a target, whose angle to the beam can be varied between 78/spl deg/ and 88/spl deg/ from normal incidence. Quadrupole magnets are operating with a variety of internal charged particle diagnostics to measure the beam halo loss, net charge, electron ionization rate, and gas density.

We study the role of technology subsidies in climate policies, using a simple dynamic equilibrium model with learning-by-doing. The optimal subsidy rate of a carbon-free technology is high when the technology is first adopted, but falls significantly over the next decades. However, the efficiency costs of uniform instead of optimal subsidies, may be low if there are introduction or expansion constraints for a new technology. Finally, supporting existing energy technologies only, may lead to technology lock-in, and the impacts of lock-in increase with the learning potential of new technologies as well as the possibilities for early entry and thight carbon constraints.

Biofuels have been identified as a mid-term GHG emission abatement solution for decarbonising the transport sector. This study examines the techno-economic analysis of biofuel production via biomass fast pyrolysis and subsequent bio-oil upgrading via zeolite cracking. The aim of this study is to compare the techno-economic feasibility of two conceptual catalyst regeneration configurations for the zeolite cracking process: (i) a two-stage regenerator operating sequentially in partial and complete combustion modes (P-2RG) and (ii) a single stage regenerator operating in complete combustion mode coupled with a catalyst cooler (P-1RGC). The designs were implemented in Aspen Plus® based on a hypothetical 72 t/day pine wood fast pyrolysis and zeolite cracking plant and compared in terms of energy efficiency and profitability. The energy efficiencies of P-2RG and P-1RGC were estimated at 54% and 52%, respectively with corresponding minimum fuel selling prices (MFSPs) of £7.48/GGE and £7.20/GGE. Sensitivity analysis revealed that the MFSPs of both designs are mainly sensitive to variations in fuel yield, operating cost and income tax. Furthermore, uncertainty analysis indicated that the likely range of the MFSPs of P-1RGC (£5.81/GGE  £11.63/GGE) at 95% probability was more economically favourable compared with P-2RG, along with a penalty of 2% reduction in energy efficiency. The results provide evidence to support the economic viability of biofuel production via zeolite cracking of pyrolysis-derived bio-oil.

The main problem involved in utilization of solar energy is low efficiency of photovoltaic conversion and high cost. The objectives of the paper are to increase the efficiency of photovoltaic cells and to reduce the cost of the photovoltaic module. A novel method is presented in this paper to increase the efficiency of photovoltaic modules and reduce the cost by the use of specially designed solar concentrators. The efficiency of photovoltaic module is increased by covering an angle of nearly 180° so that the sun's radiation converges through out the day from all angles on to a fixed flat plate module. The cost is reduced by use of cheaper glass for module and covering a wider area for the radiation. The above objectives are achieved by the use of a special arrangement of three lenses for the concentrators.

Abstract The convergence characteristics of two Monte Carlo models are investigated in this paper. These two models or their variations have been used by the electric power industry. Model 1 is based on the random sampling of generator and transmission line status for each hour. Model 2 advances the generation and transmission using the next-event approach. Mathematical analysis and system studies indicate that model 2 converges slower than model 1. The simulation time for model 2 is generally more than twice the mean duration of loss of load. The CPU time per year of simulation is shorter for model 2. However, the total CPU time for model 2 is much longer than for model 1.

Global sensitivity analysis (GSA) of distribution system with respect to stochastic PV and load variations plays an important role in designing optimal voltage control schemes. This paper proposes a data-driven framework for GSA of distribution system. In particular, two representative surrogate modeling-based approaches are developed, including the traditional Gaussian process-based and the analysis of variance (ANOVA) kernel ones. The key idea is to develop a surrogate model that captures the hidden global relationship between voltage and real and reactive power injections from the historical data. With the surrogate model, the Sobol indices can be conveniently calculated through either the sampling-based method or the analytical method to assess the global sensitivity of voltage to variations of load and PV power injections. The sampling-based method estimates the Sobol indices using Monte Carlo simulations while the analytical method calculates them by resorting to the ANOVA expansion framework. Comparison results with other model-based GSA methods on the unbalanced three-phase IEEE 37-bus and 123-bus distribution systems show that the proposed framework can achieve much higher computational efficiency with negligible loss of accuracy. The results on a real 240-bus distribution system using actual smart meter data further validate the feasibility and scalability of the proposed framework.

The Houston-Dallas (I-45) corridor is the busiest route among 18 traffic corridors in Texas, USA. The expected population growth and the surge in passenger mobility may result in a significant impact on the regional environment. This study uses a life cycle framework to predict and evaluate the net changes of environmental impact associated with the potential development of a high-speed rail (HSR) System along the I-45 corridor through its life cycle. The environmental impact is estimated in terms of CO2 and greenhouse gas (GHG) emissions per vehicle/passenger-kilometers traveled (V/PKT) using life cycle assessment. The analyses are performed referring to the Ecoinvent 3.4 inventory database through the phases: material extraction and processing, infrastructure construction, vehicle manufacturing, system operation, and end of life. The environmental benefit is evaluated by comparing the potential development of the HSR system with those of the existing transportation systems. The vehicle component, especially operation and maintenance of vehicles, is the primary contributor to the total global warming potential with about 93% of the life cycle GHG emissions. For the infrastructure component, 56.76% of GHG emissions result from the material extraction and processing phase (23.75 kgCO2eq/VKT). Various life cycle emissions of HSR except PM are significantly lower than for passenger cars.

ABSTRACTThe California generation fleet manages the existing variability and uncertainty in the demand for electric power (load). When wind power is added, the dispatchable generators manage the variability and uncertainty of the net load (load minus wind power). The variability and uncertainty of the load and the net load are compared when 8790 MW of wind power are added to the California power system, a level expected when California achieves its 33% renewable portfolio standard, using a data set of 26,296 h of synchronous historic load and modeled historic wind power output. Variability was calculated as the rate of change in power generated by wind farms or consumed by the load from 1 h to the next (MW/h). Uncertainty was calculated as the 1 h ahead forecast error [MW] of the wind power or of the load. The data show that wind power adds no additional variability than is already present in the load variability. However, wind power adds additional uncertainty through increased forecast errors in the net load compared with the load. Forecast errors in the net load increase 18.7% for negative forecast errors (actual less than forecast) and 5.4% for positive forecast errors (actual greater than forecast). The increase in negative forecast errors occurs only during the afternoon hours when negative load forecasts and positive wind forecasts are strongly correlated. Managing the integration of wind power in the California power system should focus on reducing wind power forecast uncertainty for wind ramp ups during the afternoon hours. Copyright © 2013 John Wiley & Sons, Ltd.