Decreasing costs of renewable energy resources and net-zero emission energy production policy, set by U.S. government, are two preeminent factors that motivate power utilities to deploy more system- or consumer-centric distributed energy resources (DERs) to decarbonize electricity production. Since, deep energy decarbonization cannot be achieved without high penetration of renewable energy sources, utilities should develop and invest in new business models for power system operation and planning during the energy transition. Considering the pathways to deeply decarbonize power systems, first, this dissertation proposes a novel hierarchical peer-to-peer (P2P) energy market design in active distribution networks. The framework integrates the distributional locational marginal price to a multi-round double auction with average price mechanism to integrate the network usage charges into the bills of customers. Second, this dissertation investigates the role of grid-integrated hydrogen (H2) systems for improved utility operations and to supply fuel to transportation sector. Power quality concerns as well as risk of uncertain parameters are considered using conditional value at risk based epsilon constraint method. Third, this dissertation proposes a bi-level proactive rolling-horizon based scheduling of H2 systems in integrated distribution and transmission networks considering the flexibility of these assets as controllable load or generation, in addressing the utility operators' normal and emergency operation signals. Fourth, a detailed model is developed for grid-integrated Electrolyzer considering polarization curve and non-linear conversion efficiency of these assets in the P2P enabled distribution network. This framework shows that reasonable penetration of P2P energy exchanges can significantly lower the H2 production cost. Finally, this dissertation proposes a cyber-physical vulnerability assessment of P2P energy exchanges in an unbalanced active distribution networks. Simulation results of this dissertation show the effectiveness of the proposed frameworks.

The adoption of plug-in hybrid electric vehicles (PHEV) as a means of transportation over conventionally fueled vehicles introduces new challenges to the existing infrastructure of the electrical transmission and distribution system. PHEV battery charging can represent a significant power demand that has the potential to overload electrical distribution components. This study examines the impacts of PHEV charging on household service transformers, distribution conductors and voltage levels of a Central California residential distribution system. The system is simulated using ETAP power system analysis software. Transformers are the most vulnerable to overloads, especially if PHEV charging occurs in clusters. Main feeder conductors will be overloaded if a large amount of high power, quick charging occurs. Branch conductors will not be affected by PHEV charging. Based on current PHEV market projections for the region this study shows that significant equipment overloads are not likely to occur until well after 2017.

The power requirements for the microprocessor have been increasing as per Moore's Law. According to International Technology Roadmap (ITRS), the Voltage Regulator Module (VRM) for the microprocessor will be 200 W(with 1V, 200A output) in 2010. With the VRMs topology of synchronous buck, serious technical challenges such as small duty cycle, high switching frequencies, and higher current demands, contribute to decreased power density and increased cost. This thesis proposes a Multiphase Multi-Interleave Buck topology to solve the technical challenges of powering future microprocessors. The critical design parameter values are selected using the theoretical design equations and calculations. The design is simulated in OrCAD Pspice to evaluate the performance criteria of the VRM. A prototype of four-phase Multiphase Multi-Interleave Buck Converter is constructed. The critical performance parameters of the prototype are tested and measured. The thesis concludes with the performance of the prototype as compared with the performance of the design simulation.

In this study, a standard bulk FET design is fabricated with a piezoelectric gate, allowing a microwave-frequency voltage signal to be transmitted from a transducer to the transistor via acoustic conduction through the substrate. Using a single polycrystalline aluminum nitride film as the piezoelectric material, microwave-frequency piezoelectric transducers were fabricated in parallel with piezoelectric FETs. These device pairs function by generating high frequency acoustic waves in the substrate via application of AC voltage to the transducers; the transistors then recover these signals by detecting these waves in their gates via the piezoelectric film, reproducing an attenuated version of the original voltage signal. By taking advantage of the active nature of the transistor and properly controlling the gate and drain bias voltages to maximize gain, the signal recovered by the transistor can be amplified beyond what is passively recovered. In this paper, the design, fabrication, and testing processes for these devices are described at length; the results of these processes, demonstrating devices equivalent to standard FETs that are capable of harnessing incident acoustic waves to generate AC voltages at microwave frequencies, are also presented.

According to the US Energy and Information Administration, between 2022-2023 60% of planned new electricity generation consists of solar farms with a battery energy storage system [1]. The demand for these paired systems has increased since batteries can be charged during the day with the energy captured from the solar farm then released to the customer in the evening during peak energy demand. This achieves peak load shaving which reduces the cost of electricity for the customer and is ecologically friendly. This thesis aims to create an efficient solar farm with a battery energy storage system for a farmer in California that achieves peak load shaving. Full cell modules and half-cell modules were explored to determine the type that best suits this project. The half-cell modules were best suited because of the increased efficiency. Six different solar farm designs were created, four fixed tilt designs and two single axis tracking designs. Two types of software, System Advisor Model (SAM) and REopt, were compared to determine which would be most useful in simulating these designs. It was concluded that System Advisor Model (SAM) would be the most accurate to simulate the six designs and produce metrics such as the annual energy production, capacity factor, DC to AC ratio, and levelized cost of energy. The final design, design 6, a 2-string single axis tracking design produced the best metrics that met the project requirements and a battery energy storage system was sized for the design.

The use of renewable energy and specifically solar energy in power systems is rapidly increasing due to significantly lower carbon emissions and low energy costs. Although the widespread use of renewable energy generation provides many benefits to the power system, high levels of renewable energy generation introduce several new challenges to the power system operation. The high level of uncertainty associated with solar power output complicates operation and planning decisions for the power system. Therefore, accurate and reliable solar power forecasts are needed for the planning and operation of the power system more than ever before. This thesis first focuses on improving probabilistic solar power forecasts that provide detailed information on the uncertainty of the forecasts. The proposed copula-based Bayesian method utilizes the underlying relation between temperature and solar power output to improve forecast accuracy and performance. The results show significant improvement compared to the direct use of temperature as an input to the forecast model. Secondly, a novel improvement is made to the State Frequency Memory (SFM) method for solar forecasting. The SFM model, which is based on the Long Short Term Memory (LSTM) method, incorporates the patterns in the frequency domain on top of the time domain considerations. The SFM model is improved by frequency band selection based on the Fourier transform of the solar power data. The improved SFM model is able to include the low-frequency patterns in solar data compared to the sampling frequency of second and minute-level and significantly improve results in very short-term forecasting. Thirdly, One of the challenges that arise from high penetration of solar power is investigated further. An essential part of power system operation is maintaining the balance between generation and loads in the power system. The intermittency of solar power makes it very challenging for the operators to maintain the balance and increases the need for spinning reserves in the power system. In this thesis, solar forecasts are used in a multistep optimization model to control energy storage and electric vehicle charging to minimize violations from the ramp rate limits of the system. A detailed analysis of forecast error is performed to tackle the trade-off between longer forecast horizons and increasing forecast error and find the optimal forecast horizon for predictive solar power smoothing.

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

Dwindling fossil fuels and the rising price of energy has meant that attitudes towards energy usage have changed in both domestic and commercial settings. This change in attitude has led to the development of smart metering technologies that are currently being rolled out across the world. The research has been developed to be able to add functionality to smart metering devices by providing information about energy usage within the premises through Non-Intrusive Load Monitoring (NILM). The thesis provides a detailed description of the work undertaken to develop a novel method of load disaggregation within NILM to aid in the monitoring of energy usage and the provision of consumer feedback which can be integrated into smart metering technologies. The research aims to provide a novel approach to NILM through the use of canopy clustering for its main process of load disaggregation. Canopy clustering provides the necessary tools for separating out appliances and groups of appliances for later classification into individual loads, which brings many benefits compared to other technologies. The research methodology has been developed with robust techniques of data gathering, model development and validation through a rigorous testing approach. Real world examples of loads have been used for the creation and development of the models. The use of contemporary appliances within the research has meant that the NILM algorithm developed is current and usable. In the final implementation it could be commercialised for use by the general public. The full procedures of the algorithm have been explained in detail with the addition of information on the final classification methods that could be used when implemented within smart metering devices. Further work and improvements to the research have also been included for consideration.

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.