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This paper addresses the challenge of meeting increasing energy needs by assessing the potential of bioenergy as a sustainable resource option in South Central Texas. Available agricultural crop residues suitable for bioenergy production are evaluated from the 21 counties in South Central Texas Regional Water Planning Area (Region L). The residues produced and available for bioenergy are quantified according to the production areas for each field crop and tree area. Residue-to-product ratios of field crops are determined according to crop type and production quantity. Biomass potential of trees is calculated based on tree density and biomass production per tree. The results demonstrate that the potential productions of utilizable agricultural wastes are in the range of 898.7 t kt–1421.39 kt for Region L. The average annual energy potential is estimated at 19.27 PJ, and ranges between 14.36 and 24.18 PJ. The average potential biomass-based electricity production could compensate significant amount of coal-based electricity generated in the Texas and when agricultural wastes are available. [ABSTRACT FROM AUTHOR]

The main challenges of the input current control in synchronous DC-DC buck converters are the nonlinear model of the system, changes of the operating point in a wide range, and the need to use an input LC filter for current smoothing, which may result in the instability of the closed-loop system. In this paper, a step-by-step approach is developed for the design and improvement of a PI-feedforward closed-loop controller. It is shown that a linear PI controller cannot stabilize the closed-loop system properly during wide changes in model parameters, e.g., an equivalent series resistance of the input filter. To cope with the stability issues, a fixed-frequency sliding mode controller (SMC) has been developed in this paper for the implementation of an electro-mechanical actuator (EMA) emulator. Moreover, a systematic approach is proposed for controller tuning and the selection of the SMC's gains. To achieve high power efficiency, high-frequency GaN switches are used for the practical implementation of the DC-DC converter. Despite large changes in the load current, the designed nonlinear controller can track the input current reference satisfactorily. Steady-state and dynamic responses of the proposed SMC are compared with conventional linear controllers. Considering the Lyapunov stability theorem, it is proved that the designed SMC can stabilize the closed-loop system in the entire utilizable domain. The proposed nonlinear SMC controller enjoys a very simple control law. Hence, despite having very high switching and sampling frequencies, it can be easily implemented. The experimental response of the designed synchronous DC-DC buck converter is evaluated experimentally by implementing the control strategy in a TMS320F28335PGFA DSP from Texas Instrument. Moreover, the comprehensive comparison of the proposed SMC controller and a PI-feedforward controller proved the superior performance of the developed closed-loop system, in terms of the transient time response, robustness, and stability of the EMA emulator. [ABSTRACT FROM AUTHOR]

Formation fracturing is the method of choice for developing shale oil and gas reservoirs that constitute a gigantic resource in the U.S.A. and many other countries but are characterized by a low permeability in the nano-Darcy range. The oil production of Texas has increased by about 5 million B/D in 15 years as a result of shale exploitation by massive multistage hydraulic fracturing. The mathematical modeling of this fracturing process is complex and can be approached in several ways. This paper first gives a concise description of the fracturing process as carried out in Texas. Included are the ranges of the key reservoir properties, as well as the injection fluid volumes and pressure, the composition of the injected fluid, proppant type, and volume, and other relevant data. Also included are the number of fracture stages, methods of zonal isolation, and diagnostic techniques used. An important variable considered is flowback, in particular, fluid retention and oil and gas production. High-salinity water production is discussed. Given the above variables, the currently used fracture simulators are briefly considered and compared, both the geomechanics-based and fracture-propagation-based. No single simulator can model the complete process. The directions currently being followed are briefly described. Also discussed is the simulation of the re-fracturing process and its range of success in increasing oil recovery from about the original ~5% to ~8%. Future processes such as plasma fracturing are mentioned, and their future applicability is discussed for further increasing oil recovery. [ABSTRACT FROM AUTHOR]

The utilization of wind energy has become increasingly popular in the United States and many European countries due to its abundant nature and optimized design. While existing wind turbines are predominantly large-scale and not suitable for standalone or distributed power production, Lubbock County in West Texas offers a diverse range of renewable energy options to meet its energy needs. The region relies heavily on utility-scale wind energy sources to supply power to the Texas Grid, replacing conventional fossil fuel-based systems. Currently, standalone solar PV systems are the preferred choice for renewable energy generation. However, West Texas possesses an ample supply of wind energy that can be harnessed to establish a microgrid and provide standalone power to rural communities. Distributed wind energy offers localized power generation, reducing transmission losses and grid strain, while conventional wind farms require long-distance transmission, leading to efficiency gains. By employing the latest technology and optimizing efficiency, even in low-scale generation, a 6-kilowatt permanent magnet alternator-based distributed wind turbine has been designed. This paper focuses on analyzing the techno-economic aspects of implementing this wind turbine in a real-world scenario, taking into account wind attributes, such as velocity and available power, at the specific location. [ABSTRACT FROM AUTHOR]

Motivated by the relatively infrequent but very large price spikes in the day-ahead and real-time energy markets operated by the Electric Reliability Council of Texas, this paper proposes an insurance that a small and risk-averse retailer in Texas (i.e., a retail electric provider (REP)) may buy to prevent financial insolvency caused by inadequate risk management. It also demonstrates the insurance's practical design, pricing, and implementation. As participation in the REP's procurement auction is voluntary, the insurance is mutually beneficial for the REP and the insurance seller. Hence, the proposed insurance is a newly developed wholesale market product that deserves consideration by REPs in Texas and competitive retailers elsewhere. [ABSTRACT FROM AUTHOR]

To prevent climate change, Europe and the world must shift to low-carbon and renewable energies. Hydrogen, as an energy vector, provides viable solutions for replacing polluting and carbon-emitting fossil fuels. Gaseous hydrogen can be stored underground and coupled with existing natural gas pipe networks. Salt cavern storage is the best suited technology to meet the challenges of new energy systems. Hydrogen storage caverns are currently operated in the UK and Texas. A preliminary risk analysis dedicated to underground hydrogen salt caverns highlighted the importance of containment losses (leaks) and the formation of gas clouds following blowouts, whose ignition may generate dangerous phenomena such as jet fires, unconfined vapor cloud explosions (UVCEs), or flashfires. A blowout is not a frequent accident in gas storage caverns. A safety valve is often set at a 30 m depth below ground level; it is automatically triggered following a pressure drop at the wellhead. Nevertheless, a blowout remains to be one of the significant accidental scenarios likely to occur during hydrogen underground storage in salt caverns. In this paper, we present modelling the subterraneous and aerial parts of a blowout on an EZ53 salt cavern fully filled with hydrogen. [ABSTRACT FROM AUTHOR]

Power systems optimization is generally subject to the compromise between performance and cost. The 2021 Texas grid outage illustrates the worldwide dangers for the regional-centralized power grid, with comparable advantages to safety and flexibility for the distributed energy system. The storage of household batteries helps balance grid load and increase system stability and flexibility. However, household storage battery is still not widely used today because of its high costs. Currently, research on increasing household battery storage applicability is focused largely on optimizing economic strategies, such as configuration, dispatching and subsidy policies, which rely substantially more on technologies and financial perspectives. Consumers are not 'rational' individuals, and non-economic incentives can affect their decisions without raising prices. This paper consequently proposes to encourage users to acquire household battery storage to increase efficiency of power dispatching and economic advantages based on behavioral economics. In this paper, an empirical research builds upon the utility model of behavioral economics incentives and purchase willingness. Moreover, the multi-objective genetic algorithm is utilized to optimize the dispatching of household battery storage by using grid variance and user revenues as optimizing goals. The results of this paper show that the behavioral economics incentive improves intention to buy the household battery energy storage by 10.7% without raising subsidies. By improving the energy dispatching strategy, peak-load shifting performance and user revenues are improved by 4.2% and 10.6%, respectively. [ABSTRACT FROM AUTHOR]

Among numerous functions performed by the battery management system (BMS), online estimation of the state of health (SOH) is an essential and challenging task to be accomplished periodically. In electric vehicle (EV) applications, accurate SOH estimation minimizes failure risk and improves reliability by predicting battery health conditions. The challenge of accurate estimation of SOH is based on the uncertain dynamic operating condition of the EVs and the complex nonlinear electrochemical characteristics exhibited by the lithium-ion battery. This paper presents an artificial neural network (ANN) classifier experimentally validated for the SOH estimation of lithium-ion batteries. The ANN-based classifier model is trained experimentally at room temperature under dynamic variable load conditions. Based on SOH characterization, the training is done using features such as the relative values of voltage, state of charge (SOC), state of energy (SOE) across a buffer, and the instantaneous states of SOC and SOE. At implementation, due to the slow dynamics of SOH, the algorithm is triggered on a large-scale periodicity to extract these features into buffers. The features are then applied as input to the trained model for SOH estimation. The classifier is validated experimentally under dynamic varying load, constant load, and step load conditions. The model accuracies for validation data are 96.2%, 96.6%, and 93.8% for the respective load conditions. It is further demonstrated that the model can be applied on multiple cell types of similar specifications with an accuracy of about 96.7%. The performance of the model analyzed with the confusion matrices is consistent with the requirements of the automotive industry. The classifier was tested on a Texas F28379D microcontroller unit (MCU) board. The result shows that an average real-time execution speed of 8.34 µs is possible with a negligible memory occupation. [ABSTRACT FROM AUTHOR]

Waterflooding is a widely used secondary oil recovery technique. The oil and gas industry uses a complex reservoir numerical simulation and reservoir engineering analysis to forecast production curves from waterflooding projects. The application of such standard methods at the stage of assessing the potential of a huge number of projects could be computationally inefficient and requires a lot of effort. This paper demonstrates the applicability of machine learning to rate the outcome of waterflooding applied to an oil reservoir. We also explore the relationship of project evaluations by operators at the final stages with several performance metrics for forecasting. Real data about several thousand waterflooding projects in Texas are used in the current study. We compare the ML models rankings of the waterflooding efficiency and the expert rankings. Linear regression models along with neural networks and gradient boosting on decision threes are considered. We show that machine learning models allow reducing computational complexity and can be useful for rating the reservoirs, with respect to the effectiveness of waterflooding. [ABSTRACT FROM AUTHOR]

It is normally accepted that Battery Energy Storage Systems improve frequency regulation by providing fast response to the Automatic Generation Control. However, currently available control strategies may lead to early Energy Storage Systems aging given that Automatic Generation Control requirements are increasing due to zero carbon power generation integration. In this sense, it is important to analyze the aging phenomena in order to assess the technical–economical usefulness of Battery Energy Storage Systems towards zero carbon power systems. In order to avoid early aging, various proposals on aging-reducing algorithms can be found; however, it is unclear if those aging-reducing algorithms affect the performance of Battery Energy Storage Systems. It is also unclear whether those effects must be internalized to properly dimension the capacity of Battery Energy Storage Systems to both comply with performance standards and to prevent early aging. Thus, this paper estimates the storage capacity of a Battery Energy Storage Systems to comply with Automatic Generation Control performance standard under aging-reducing operating algorithms by dynamics simulations of a reduced-order, empirically-validated model of the Electric Reliability Council of Texas. The results show the relationship between the required performance of Automatic Generation Control and Battery Energy Storage System capacity, considering a 1-year simulation of Automatic Generation Control dynamics. It can be concluded that the compliance with performance standards is strongly related to the storage capacity, regardless of how fast the device can inject or withdraw power from the grid. Previous results in the state-of-the-art overlook the quantification of this relationship between compliance with performance standards and storage capacity. [ABSTRACT FROM AUTHOR]

The online estimation of battery state of health (SOH) is crucial to ensure the reliability of the energy supply in electric and hybrid vehicles. An approach for enhancing the generalization of SOH estimation using a parallel layer extreme learning machine (PL-ELM) algorithm is analyzed in this paper. The deterministic and stable PL-ELM model is designed to overcome the drift problem that is associated with some conventional machine learning algorithms; hence, extending the application of a single SOH estimation model over a large set of batteries of the same type. The PL-ELM model was trained with selected features that characterize the SOH. These features are acquired as the discrete variation of indicator variables including voltage, state of charge (SOC), and energy releasable by the battery. The model training was performed with an experimental battery dataset collected at room temperature under a constant current load condition at discharge phases. Model validation was performed with a dataset of other batteries of the same type that were aged under a constant load condition. An optimum performance with low error variance was obtained from the model result. The root mean square error (RMSE) of the validated model varies from 0.064% to 0.473%, and the mean absolute error (MAE) error from 0.034% to 0.355% for the battery sets tested. On the basis of performance, the model was compared with a deterministic extreme learning machine (ELM) and an incremental capacity analysis (ICA)-based scheme from the literature. The algorithm was tested on a Texas F28379D microcontroller unit (MCU) board with an average execution speed of 93 μs in real time, and 0.9305% CPU occupation. These results suggest that the model is suitable for online applications. [ABSTRACT FROM AUTHOR]

Inductive power transfer (IPT) systems have become more and more popular recently. To improve transient responses and load disturbance responses, this paper proposes a predictive controller design for a three-winding inductive power transfer (IPT) system. First, a three-winding IPT is presented. Next, a predictive controller is designed based on augmented variables and a performance index. Finally, a digital signal processor, TMS 320F2808, made by Texas Instrument, is used to execute the predictive control algorithms and to control the switching states of the power devices. An IPT system, with DC 220 V input, DC 130 V output, and a rated power of 2 kW, is implemented. A buck converter is used to provide an adjustable output voltage and output current to charge a battery set. Experimental results show that the proposed predictive controllers of the IPT system have better performance than proportional-integral (PI) controllers, including faster transient responses and better load disturbance responses. [ABSTRACT FROM AUTHOR]

Farnsworth Field Unit (FWU), a mature oilfield currently undergoing CO2-enhanced oil recovery (EOR) in the northeastern Texas panhandle, is the study area for an extensive project undertaken by the Southwest Regional Partnership on Carbon Sequestration (SWP). SWP is characterizing the field and monitoring and modeling injection and fluid flow processes with the intent of verifying storage of CO2 in a timeframe of 100–1000 years. Collection of a large set of data including logs, core, and 3D geophysical data has allowed us to build a detailed reservoir model that is well-grounded in observations from the field. This paper presents a geological description of the rocks comprising the reservoir that is a target for both oil production and CO2 storage, as well as the overlying units that make up the primary and secondary seals. Core descriptions and petrographic analyses were used to determine depositional setting, general lithofacies, and a diagenetic sequence for reservoir and caprock at FWU. The reservoir is in the Pennsylvanian-aged Morrow B sandstone, an incised valley fluvial deposit that is encased within marine shales. The Morrow B exhibits several lithofacies with distinct appearance as well as petrophysical characteristics. The lithofacies are typical of incised valley fluvial sequences and vary from a relatively coarse conglomerate base to an upper fine sandstone that grades into the overlying marine-dominated shales and mudstone/limestone cyclical sequences of the Thirteen Finger limestone. Observations ranging from field scale (seismic surveys, well logs) to microscopic (mercury porosimetry, petrographic microscopy, microprobe and isotope data) provide a rich set of data on which we have built our geological and reservoir models. [ABSTRACT FROM AUTHOR]

Power quality and energy efficiency are of great importance in motor control. The motor of any medical device needs to have a smooth torque and minimal vibration in order to maximise its energy efficiency and patient comfort. Furthermore, in rotary blood pumps, excessive energy wasted due to vibration is converted into uncontrolled movement of the mechanical parts and thus could reduce the life of the motor-pump. Besides mechanical or hydraulic origin, one of the causes of vibration in any pump is torque ripple resulting from motor phase commutation. In this paper, using relevant equipment, two extreme scenarios were examined for vibration and electrical efficiency comparison due to power quality in a blood pump: one trapezoidal control with a trapezoidal phase current output; the other a field oriented control (FOC) with a non-distorted sinusoidal phase current. The test motor-pump was the Arteriovenous Fistula Eligibility (AFE) System that is used prior to haemodialysis. The trapezoidal technique was implemented utilising the Allegro a4941 fan driver (Allegro Microsystem, 2012), and the FOC technique was implemented using the Texas Instrument digital signal processor (TMS320F28335). The aim was to reduce the energy wasted over vibration, and to achieve smooth operation of the AFE System. Vibration was measured with a one-axis accelerometer; results showed considerably lower vibration due to less current ripple associated with the FOC control as well as lower power consumption. [ABSTRACT FROM AUTHOR]

This paper develops a neural network (NN) vector controller for an interior mounted permanent magnet (IPM) motor by using a Texas Instrument TMS320F28335 digital signal processor (DSP). The NN controller is developed based on the complete state-space equation of an IPM motor and it is trained to achieve optimal control according to approximate dynamic programming (ADP). A DSP-based NN control system is built for an IPM motor drives system, and a high efficient DSP program is developed to implement the NN control algorithm while considering the limited memory and computing capability of the TMS320F28335 DSP. The DSP-based NN controller is able to manage IPM motor control in linear, over, and six-step modulation regions to improve the efficiency of IPM drives and to allow for the full utilization of DC bus voltage with space-vector pulse-width modulation (SVPWM). The experiment results show that the proposed NN controller is able to operate with a sampling period of 0.1ms, even with limited DSP resources of up to 150 MHz cycle time, which is applicable in practical motor industrial implementations. The NN controller has demonstrated a better current and speed tracking performance than the conventional standard vector controller for IPM operation in both the linear and over-modulation regions. [ABSTRACT FROM AUTHOR]