Microorganisms are capable of producing advanced biofuels that can be used as 'drop-in' alternatives to conventional liquid fuels. However, vital physiological processes and membrane properties are often disrupted by the presence of biofuel and limit the production yields. In order to make microbial biofuels a competitive fuel source, finding mechanisms of improving resistance to the toxic effects of biofuel production is vital. This investigation aims to identify resistance mechanisms from microorganisms that have evolved to withstand hydrocarbon-rich environments, such as those that thrive near natural oil seeps and in oil-polluted waters. In this study, screened the genomes of two types of bacteria, Pseudomonas aeruginosa and Marinobacter aquaeolei, looking for genes that impart biofuel tolerance when expressed in Escherichia coli. Both of these microbes have adapted in their respective natural environments to contain mechanisms for dealing with environmental stress. For initial work, P. aeruginosa was used to test our experimental design and procedure, and we validated our methods by identifying a gene, ohr from P. aeruginosa, that increased tolerance to the bio-jet fuel precursor limonene in Escherichia coli. Using genomic DNA from M. aquaeolei, we constructed a transgenic library that we expressed in E. coli. We exposed cells to inhibitory levels of pinene, a monoterpene that can serve as a jet fuel precursor with chemical properties similar to existing tactical fuels. Using a sequential strategy of a fosmid library followed by a plasmid library, we were able to isolate a region of DNA from the M. aquaeolei genome that conferred pinene tolerance when expressed in E. coli. We determined that a single gene, yceI, was responsible for the tolerance improvements. Overexpression of this gene placed no additional burden on the host. We also tested tolerance to other monoterpenes and showed that yceI selectively improves tolerance.

Abstract Power system load forecasting refers to the study or uses a mathematical method to process past and future loads systematically, taking into account important system operating characteristics, capacity expansion decisions, natural conditions, and social impacts, to meet specific accuracy requirements. Dependence of this, determine the load value at a specific moment in the future. Improving the level of load forecasting technology is conducive to the planned power management, which is conducive to rationally arranging the grid operation mode and unit maintenance plan, and is conducive to formulating reasonable power supply construction plans and facilitating power improvement, and improve the economic and social benefits of the system. At present, there are many methods for load forecasting. The newer algorithms mainly include the neural network method, time series method, regression analysis method, support vector machine method, and fuzzy prediction method. However, most of them do not apply to long-term time-series predictions, and as a result, the prediction accuracy for long-term power grids does not perform well. This thesis describes the design of an algorithm that is used to predict the load in a long time-series. Predict the load is significant and necessary for a dynamic electrical network. Improved the forecasting algorithm can save a ton of the cost of the load. In this paper, we propose a load forecasting model using long short-term memory(LSTM). The proposed implementation of LSTM match with the time-series dataset very well, which can improve the accuracy of convergence of the training process. We experiment with the difference time-step to expedites the convergence of the training process. It is found that all cases achieve significant different forecasting accuracy while forecasting the difference timesteps. Keywords—Load forecasting, long short-term memory, micro-grid

After more than a century of the ultimate dominance of AC in distribution systems, DC distribution is being re-considered. However, the advantages of AC systems cannot be omitted. This is mainly due to the cheap and efficient means of generation provided by the synchronous AC machines and voltage stepping up/down allowed by the AC transformers. As an intermediate solution, hybrid AC/DC distribution systems or microgrids are proposed. This hybridization of distribution systems, incorporation of heterogeneous mix of energy sources, and introducing Pulsed Power Loads (PPL) together add more complications and challenges to the design problem of distribution systems. In this dissertation, a comprehensive multi-objective optimization approach is presented to determine the optimal design of the AC/DC distribution system architecture. The mathematical formulation of a multi-objective optimal power flow problem based on the sequential power flow method and the Pareto concept is developed and discussed. The outcome of this approach is to answer the following questions: 1) the optimal size and location of energy storage (ES) in the AC/DC distribution system, 2) optimal location of the PPLs, 3) optimal point of common coupling (PCC) between the AC and DC sides of the network, and 4) optimal network connectivity. These parameters are to be optimized to design a distribution architecture that supplies the PPLs, while fulfilling the safe operation constraints and the related standard limitations. The optimization problem is NP-hard, mixed integer and combinatorial with nonlinear constraints. Four objectives are involved in the problem: minimizing the voltage deviation (ΔV), minimizing frequency deviation (Δf), minimizing the active power losses in the distribution system and minimizing the energy storage weight. The last objective is considered in the context of ship power systems, where the equipment’s weight and size are restricted. The utilization of Hybrid Energy Storage Systems (HESS) in PPL applications is investigated. The design, hardware implementation and performance evaluation of an advanced – low cost Modular Energy Storage regulator (MESR) to efficiently integrate ES to the DC bus are depicted. MESR provides a set of unique features: 1) It is capable of controlling each individual unit within a series/parallel array (i.e. each single unit can be treated, controlled and monitored separately from the others), 2) It is able to charge some units within an ES array while other units continue to serve the load, 3) Balance the SoC without the need for power electronic converters, and 4) It is able to electrically disconnect a unit and allow the operator to perform the required maintenance or replacement without affecting the performance of the whole array. A low speed flywheel Energy Storage System (FESS) is designed and implemented to be used as an energy reservoir in PPL applications. The system was based on a separately excited DC machine and a bi-directional Buck-Boost converter as the driver to control the charging/discharging of the flywheel. Stable control loops were designed to charge the FESS off the pulse and discharge on the pulse. All the developments in this dissertation were experimentally verified at the Smart Grid Testbed.

n this thesis project, a proposed architecture for the multiple input, single output conversion stage for the DC House was designed, simulated, and tested. This architecture allows for multiple different input sources to be used to create a single higher power output source. The design uses a DC-DC boost converter with a parallelable output which has been demonstrated to allow increased total output power as a function of the number of input sources available. The parallelable output has been shown to distribute load amongst the input sources relatively closely to optimize the system. This approach is also desirable since it allows for flexibility in multiple configurations it can be used in. The design was tested using hardware and data results show the performance met and exceeded the needs of the DC House project. Data was taken for configuration with 1, 2, 3, and 4 input sources providing greater than 600W of total output power at an efficiency of greater than 92%. This architecture demonstrates the possibility of expanding the total available power for a single output in proportion to the number of available input sources.

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Bacterial populations indigenous to surface (1 m) waters of the Middle Atlantic Outer Continental Shelf were sampled at seasonal intervals to determine the abundance and distribution of petroleum-degrading (HC) and "total" heterotrophic (HET) bacteria. Simultaneously, unweathered South Louisiana crude oil (SLCO) was added to aliquots of surface water samples to evaluate rates and patterns of degradation of hydrocarbon. Degradation was studied using selected nutrient and temperature regimes in both closed flask and prototype "open" or continuous dilution systems. HET bacterial levels generally decreased with distance from land (x inshore = 6 x 10('4) MPN/ml to x offshore = 6 x 10('2) MPN/ml). HC bacteria were most abundant in the coastal boundary layer (x = 2 x 10('2) MPN/ml). Changes in values of HC/HET often were not directly related to the abundance of petroleum-degrading bacteria. In closed flasks the composition of bacterial populations changed slightly in response to SLCO addition; Pseudomonas predominated after 48 days. Pseudomonas isolates exhibited the greatest ability for petroleum degradation in pure cultures. Degradation of SLCO in closed flasks characteristically resulted in removal of n-alkanes and selected aromatics at rates related to season, sampling location, and nutrient regime. Aromatic compounds were often removed concomitantly with n-alkanes. Although maximum rates of degradation were observed at 15�C using coastal water inocula and inorganic nutrient enrichment, all shelf waters sampled contained bacteria capable of petroleum degradation.