Growing concerns about the vulnerability of the electric grid, uncertainty about the cost of oil, and an increase in the deployment of renewable generation on domestic military installations have all led the Department of Defense (DoD) to reconsider its strategy for providing energy security for critical domestic operations. Existing solutions typically use dedicated backup generators to service each critical load. For large installations, this can result in over 50 small generators, each servicing a low voltage feeder to an individual building. The system as a whole is typically not well integrated either internally, with nearby renewable assets, or to the larger external grid. As a result, system performance is not optimized for efficient, reactive, and sustainable operations across the installation in the event of a power outage or in response to periods of high stress on the grid. Recent advances in energy management systems and power electronics provide an opportunity to interconnect multiple sources and loads into an integrated system that can then be optimized for reliability, efficiency, and/or cost. These integrated energy systems, or microgrids, are the focus of this study. 4. ENVIRONMENT 4.1 Location-dependent characteristics (resources and weather) 4.2 Domestic electric grid characteristics 4.3 Department of Defense installation characteristics 5. MODELING AND ANALYSIS 5.1 Description of microgrid architectures 5.2 Analysis methodology 5.3 Analysis inputs 5.4 Analysis results 6. RECOMMENDATIONS
Abstract-Plug-in hybrid electric vehicles (PHEVs) BACKGROUND: A VISION FOR PHEVSThe US energy economy suffers from the use of too much petroleum. This is a problem because the world's oil reserves are in places far from our national borders and often governed by countries with different political agendas; 60% of the reserves are in the Mideast [1]. It is also a problem because the long term supply of petroleum is limited, with discoveries declining since 1964 [2]. And, it is a problem because our burning of this non-renewable resource produces too much greenhouse gas, with 25% of CO 2 production coming from the US, and 33% of US CO 2 coming from oil. Transportation represents 28% of US energy use and 70% of petroleum use [1].Power obtained from gasoline burned in automobile engines is extremely inefficient, with an average final efficiency of only 15%, with the rest wasted in heat and drive train losses By shifting our least efficient energy conversion step from inefficient small engines to central power plants, the total well to wheel efficiency of our transportation sector can be greatly improved. Also, use of electric power transmission and distribution allows the source of transport energy to be renewable sources, nuclear, or natural gas, with far greater long term supplies and lower carbon footprint. If half the passenger vehicle fleet of the US were replaced by 100 mile per gallon (mpg) plug-in hybrids, the petroleum use of the US would be reduced by approximately the amount now imported from the Mideast. Our CO 2 emissions would be reduced by 0.5 billion tons per year, or 8%. Use of plug-in hybrid electric vehicles (PHEVs) allows the limited use of liquid fuels to extend range when that is needed for convenience, taking advantage of their very high energy density, while addressing the need for fuel conservation during the typical shorter commuting trips that dominate usage.
This paper discusses a novel approach for the automatic identification of biological agents. The essence of the approach is a combination of gene expression, microarray-based sensing, information fusion, machine learning and pattern recognition. Integration of these elements is a distinguishing aspect of the approach, leading to a number of significant advantages. Amongst them are the applicability to various agent types including bacteria, viruses, toxins, and others, ability to operate without the knowledge of a pathogen's genome sequence and without the need for bioagent-specific materials or reagents, and a high level of extensibility. Furthermore, the approach allows detection of uncataloged agents, including emerging pathogens. The approach offers a promising avenue for automatic identification of biological agents for applications such as medical diagnostics, bioforensics, and biodefense.
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