Well integrity monitoring with electric fields by using hierarchical geo-electric models.
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This is a presentation on the Temperature Testing of Proppants for EGS and Stimulation of Electromagnetic Fracture Mapping Using Electrically-Conducive Proppants by Stevens Institute of Technology, presented by Cheng Chen. This video slide presentation discusses well controlled laboratory experiments testing the high-temperature performance of electrically-conductive and non-electrically-conductive proppants in maintaining fractures hydraulic and electrical conductivities to understand and quantify the thermal-hydrologic-mechanical-chemical mechanisms that regulate the observed behaviors. This will be tested using proppant/fracture imaging via borehole electromagnetic measurements. This presentation was featured in the Utah FORGE R&D Annual Workshop on August 15, 2024.
A predictive-control approach allows an electricity provider to monitor and proactively manage peak and off-peak residential intra-day electricity usage in an emerging smart energy grid using time-dependent dynamic pricing incentives. The daily load is modeled as time-shifted, but cost-differentiated and substitutable, copies of the continuously-consumed electricity resource, and a consumer-choice prediction model is constructed to forecast the corresponding intra-day shares of total daily load according to this model. This is embedded within an optimization framework for managing the daily electricity usage. A series of transformations are employed, including the reformulation-linearization technique (RLT) to obtain a Mixed-Integer Programming (MIP) model representation of the resulting nonlinear optimization problem. In addition, various regulatory and pricing constraints are incorporated in conjunction with the specified profit and capacity utilization objectives.
Windows are thermally the “weakest link” in the building envelope. Increasing the thermal resistance of windows can make buildings more energy efficient and reduce the cost of electricity needed for conditioning the building. The proper design and placement of framing can also help to reduce the thermal bridging that occurs near the window frame area. This study investigates the energy performance of multi-pane acrylic windows fitting 24" on-center framing. Initial parametric analysis is done for a single zone accessory dwelling unit (ADU). Then, an energy model was developed for three types of wood-framed buildings: townhomes, stacked flats and hotels. A whole building energy simulation is performed for each of these building types in hot-humid Houston, mixed-humid New York, and cold-humid Minneapolis climates. The results show up to a 39% reduction in heating, ventilation, and air-conditioning (HVAC) related electricity consumption for the cold climate compared to the Base case which has window and wall properties based on ASHRAE standard 90.1 2019. In the hot climate, a modest increase in electricity consumption was seen due to an increase in cooling electricity demand. The ADU achieved Net Zero Energy performance in all 3 Climate Zones despite having the highest exterior surface area-to-floor area ratio: the ADU also had the highest PV kW to floor area ratio compared to the other multi-story building types. The townhomes, hotel and stacked flats respectively met 73, 52 and 57 % of electrical use in Houston, 71, 48 and 56 % in New York, and 63, 40 and 53 % in Minneapolis from energy produced by rooftop solar. If 400 W solar panels are used instead of 320 W panel used for energy simulation, it is estimated that in the townhomes, hotel and stacked flats rooftop solar can meet 91, 65 and 71 % of electrical use in Houston; 89, 60 and 70% in New York, and 79, 50 and 66 % in Minneapolis. A preliminary evaluation of cost shows that such superior performance can potentially be achieved at less first cost with this 24"on-center solution than conventional construction. All the building types at the three locations used for simulation had net energy use intensity under 20 kBtu/sf/year with 320 W solar panel and under 17 kBtu/sf/year with 400 W solar panel. Further tailoring of building envelope R-values and window solar heat gain to particular Climate Zone locations for each building type shows promise in reducing the HVAC electricity use that comprises almost half of building energy use.
Baryon number violation is a key ingredient of baryogenesis. It has been hypothesized that there could also be a parity-conjugated copy of the standard model particles, called mirror particles. The existence of such a mirror universe has specific testable implications, especially in the domain of neutral particle oscillation, viz. the baryon number violating neutron to mirror-neutron (n−n′) oscillation. Consequently, there were many experiments that have searched for n−n′ oscillation, and imposed constraints upon the parameters that describe it. Recently, further analysis on some of these results have identified anomalies which could point to the detection of n−n′ oscillation. All the previous efforts searched for n−n′ oscillation by comparing the relative number of ultracold neutrons that survive after a period of storage for one or both of the two cases: (i) comparison of zero applied magnetic field to a non-zero applied magnetic field, and (ii) comparison where the orientation of the applied magnetic field was reversed. However, n−n′ oscillations also lead to variations in the precession frequency of polarized neutrons upon flipping the direction of the applied magnetic field. Precession frequencies are measured, very precisely, by experiments searching for the electric dipole moment. For the first time, we used the data from the latest search for the neutron electric dipole moment to constrain n−n′ oscillation. After compensating for the systematic effects that affect the ratio of precession frequencies of ultracold neutrons and cohabiting 199Hg-atoms, chief among which was due to their motion in non-uniform magnetic field, we constrained any further perturbations due to n−n′ oscillation. We thereby provide a lower limit on the n−n′ oscillation time constant of τnn′/|cos(β)|>5.7s,0.36T′
This work demonstrates that magnetoelectric composite heterostructures can be designed at the length scale of 10µms that can be switched from a magnetized state to a vortex state, effectively switching the magnetization off, using electric field induced strain. This was accomplished using thin film magnetoelectric heterostructures of Fe 81.4 Ga 18.6 on a single crystal (011) [Pb(Mg 1/3 Nb 2/3 )O 3 ] 0.68 -[PbTiO 3 ] 0.32 (PMN-32PT) ferroelectric substrate. Here, the heterostructures were tripped from a multi-domain magnetized state to a flux closure vortex state using voltage induced strain in a piezoelectric substrate. FeGa heterostructures were deposited on a Si-substrate for superconducting quantum interference device magnetometry characterization of the magnetic properties. The magnetoelectric coupling of a FeGa continuous film on PMN-32PT was characterized using a magneto optical Kerr effect magnetometer with bi-axial strain gauges, and magnetic multi-domain heterostructures were imaged using x-ray magnetic circular dichroism—photoemission electron microscopy during the transition to the vortex state. The domain structures were modelled using MuMax 3 , a micromagnetics code, and compared with observations. The results provide considerable insight into designing magnetoelectric heterostructures that can be switched from an ‘on’ state to an ‘off’ state using electric field induced strain.
Globally, the number of electric vehicles (EVs) continues to increase. This on-going trend poses challenges for power distribution systems. The inclusion of electric vehicle supply equipment (EVSE) should be compatible with a changing energy system, whose structure is becoming increasingly distributed. The impact of future EVs should be analyzed and considered in the system planning and upgrades. In this study, impact analysis of future EVs is conducted for a utility company of U.S. West Coast. Real data of distribution feeders is converted into the GridLAB-D model for running power flow analysis. An estimation of the additional loading of EVs in 2050 is provided by the utility company. Simple mitigation methods are tested to improve voltage profiles and complete this case study.
Thirty-eight editions of the Transportation Energy Data Book (Data Book) have been published since the report began in 1976. It was designed as a desk-top reference of statistics and information that characterize transportation activity. The most recent Data Book, edition 38, has 226 tables and 70 figures in the body of the report, with an additional 51 tables in appendices. One of the most-often used tables in the Data Book is referred to by the authors as “the big energy table.” It has transportation energy use in British thermal units (Btu) by mode and fuel type. The data are compiled from about 20 different sources and combined to create a detailed picture of transportation energy use. From the first edition to edition 35 of the Data Book, electricity use in the big energy table was converted from the original kilowatt-hours (kWh) to Btu using the net generation heat rate for fossil fuels. In those days, electricity was used mainly by transit rail. As electricity began to be used in the highway modes with consumer sales of electric vehicles, that process was revisited. Since energy use in the Data Book’s big energy table for all other fuels did not include the upstream energy to create the fuel, it seemed inconsistent to include upstream energy for generation of electricity. Beginning with edition 36, electricity use for the big energy table was converted from kilowatt hours to Btu using the heat content of electricity. Using heat content instead of net generation reduced electricity use data by about two-thirds. Feedback in recent years on the change in process for electricity led to an investigation of the differences in transportation energy use when adding estimates of upstream energy use to all the fuels used in transportation. This report documents the result of that effort.
Herein we study responses of thermally annealed ultrathin films deposited on silicon substrates and containing polyzwitterions to applied electric fields by using specular neutron reflectometry (NR). In particular, we applied 7 kV under vacuum at 150 °C on the films containing poly(1-(3-sulfonatopropyl)-2-vinylpyridinium) (P2VPPS) and its blends with either a deuterated ionic liquid (EMIMBF 4 - d 11 ), potassium bromide (KBr), or deuterated sodium polystyrenesulfonate (NaPSS-d 7 ). The voltage was applied over an air gap, and the in situ neutron reflectivity measurements allowed us to measure changes in the films. In all the cases, we measured decreases in thicknesses of the films, which varied up to ~8% depending on the added salt. Posteriori X-ray reflectivity (XRR) measurements on the same films at room temperature reveal that these films were highly hygroscopic, which led to the presence of water in these films. Analysis of the NR and the XRR revealed that the decrease in the thickness of the films in the neutron reflectivity experiments on heating resulted from the loss of water and the ionic liquid but not from electrostrictive effects. The in situ NR and posteriori XRR experiments revealed not only the hygroscopic nature of these films but also depth-resolved structural rearrangements due to the applied electric fields in the films containing electrolytes and polyelectrolytes. This work shows that a combination of NR and XRR can be used to distinguish between mass loss and electrostriction in films containing charged polymers such as polyzwitterions.
Low-grade thermal energy, either in the form of waste heat or nature heat, is available abundantly around us. Since Carnot efficiency reduces with decrease in operating temperature difference, there are very limited practical options for low-grade thermal energy conversion. Thermomagnetic effect is the effect of heat on magnetic properties of a material. This phenomenon can be used to generate electricity using either direct thermal to electrical energy conversion or indirect thermal to mechanical energy conversion. This chapter provides a broad overview of various aspects of thermomagnetic energy conversion. We first explain the working principle behind the thermomagnetic energy harvesting. We then formulate mathematical equations needed to calculate the work output and energy conversion efficiency of a thermomagnetic cycle. Based on this formulation, we provide a list of working materials that can be employed in the thermomagnetic harvesters. At last, we describe various thermomagnetic harvesters proposed in the literature.
The majority of energy use in controlled environment agriculture is typically from electric lighting. Certain lighting controls can help minimize the energy required in these operations. This analysis simulates a greenhouse with different types of lighting controls in different U.S. climates to explore the effects of the lighting controls on energy use.
Effective utilization of flexible loads for grid services, while satisfying end-user preferences and constraints, requires an accurate estimation of the aggregated predictive flexibility offered by the electrical loads. Recently, there have been efforts to quantify the predictive flexibility of thermostatic loads (e.g. residential air-conditioners, electric water-heaters) using the notion of virtual battery (VB), whose state evolution is governed by a first order dynamics including self-dissipation rate, and power and energy capacities. Identifying the VB model parameters for a collection of thermostatic loads, however, is challenging primarily due to uncertainties and lack of information regarding the end-user behavior, underlying device models and parameters. In this paper, we propose a \textit{variational autoencoder}-based deep learning algorithm to identify the parameters of the VB model. Using available sensors and meters data, the proposed algorithm generates not only point estimates of the VB parameters, but also confidence intervals around those values. Effectiveness of the proposed frameworks is demonstrated on a collection of electric water-heater loads, whose operation is driven by uncertain water usage profiles.
The increasing popularity of Electric Vehicles (EVs) in the distribution grid along with technological advancement in EV electronics such as vehicle to grid (V2G) technique has enabled them to participate in grid ancillary services. To achieve this, the EVs need to establish a contract with third-party aggregators and connect to a charging unit, either residential or commercial. At any time they are connected, the EVs can decide to take part in the ancillary services program offered to them by the aggregators. If agreed, the aggregators will use the EVs as a power source capable of charging/discharging power according to the input signal, and in return, they will be compensated. This inter-temporal nature of charging/discharging is also transforming the traditional optimal power flow (OPF) problem into a dynamic OPF problem. This chapter aims at developing a multi-layer time-dependent optimization algorithm to utilize EV potential and provide ancillary services while maximizing its utilization function. Specifically, in the upper layer, an autonomous distributed ADMM algorithm is developed to optimize the cost for charging/discharging EVs while using them to regulate the voltage at each bus in the distribution grid. The distributed ADMM algorithm is also expanded to the lower layer where the individual EVs active and reactive power is controlled for voltage regulation while maintaining the desired state of the charge of the vehicle at the end of the charging period. Here, the effectiveness and performance improvement of the proposed multi-layer algorithm is illustrated through analytical analysis and simulation results.
The increasing popularity of Electric Vehicles (EVs) in the distribution grid along with technological advancement in EV electronics such as vehicle to grid (V2G) technique has enabled them to participate in grid ancillary services. To achieve this, the EVs need to establish a contract with third-party aggregators and connect to a charging unit, either residential or commercial. At any time they are connected, the EVs can decide to take part in the ancillary services program offered to them by the aggregators. If agreed, the aggregators will use the EVs as a power source capable of charging/discharging power according to the input signal, and in return, they will be compensated. This inter-temporal nature of charging/discharging is also transforming the traditional optimal power flow (OPF) problem into a dynamic OPF problem. This chapter aims at developing a multi-layer time-dependent optimization algorithm to utilize EV potential and provide ancillary services while maximizing its utilization function. Specifically, in the upper layer, an autonomous distributed ADMM algorithm is developed to optimize the cost for charging/discharging EVs while using them to regulate the voltage at each bus in the distribution grid. The distributed ADMM algorithm is also expanded to the lower layer where the individual EVs active and reactive power is controlled for voltage regulation while maintaining the desired state of the charge of the vehicle at the end of the charging period. The effectiveness and performance improvement of the proposed multi-layer algorithm is illustrated through analytical analysis and simulation results.
Current state-of-the-art techniques for non-invasive imaging of cardiac electrical phenomena require voltage recordings from dozens of different torso locations and anatomical models built from expensive medical diagnostic imaging procedures. Here this study aimed to assess if recent machine learning advances could alternatively reconstruct electroanatomical maps at clinically relevant resolutions using only the standard 12-lead electrocardiogram (ECG) as input. To that end, a computational study was conducted to generate a dataset of over 16000 detailed cardiac simulations, which was then used to train neural network (NN) architectures designed to exploit both spatial and temporal correlations in the ECG signal. Analysis over a validation set showed average errors in activation map reconstruction below 1.7 msec over 75 intracardiac locations. Furthermore, phenotypical patterns of activation and the morphology of the activation potential were correctly reconstructed. The approach offers opportunities to stratify patients non-invasively, both retrospectively and prospectively, using metrics otherwise only available through invasive clinical procedures.
The electric vehicle (EV) market in the U.S. has grown rapidly. However, poor public charging reliability could challenge achieving widespread adoption. We reviewed peer-reviewed articles and market research reports to understand how the public charging experience affects the willingness to purchase EVs among first-time buyers and current owners. Ample literature identifies the measures of the public charging experiences, characterizes the current public charging experience, and identifies the effect of infrastructure availability on EV purchase decisions. However, there is no causal evidence linking the customer experience at public chargers to purchase decisions or intentions for current EV owners or prospective buyers.
Pumped thermal energy storage (PTES) systems use an electrically-driven heat pump to store electricity in the form of thermal energy, and subsequently dispatch the stored thermal energy to generate electricity using a thermodynamic heat engine. Optimal day-ahead operational scheduling and annual value of a PTES system based on Joule-Brayton thermodynamic cycles and two-tank molten salt hot thermal storage is evaluated in this work. Production cost models, which simultaneously optimize commitment and dispatch schedules for an entire set of generators to minimize the cost of satisfying electricity demand, are employed to determine system-optimal operation and day-ahead energy value of the PTES system within each of six hypothetical near-future grid scenarios intended to approximately represent the U.S. Western Interconnection or the Texas Interconnection. Sensitivity to grid scenario (including the contribution of variable renewable energy sources), thermal storage capacity, relative heat pump and heat engine capacities, and startup/shutdown cycling costs are evaluated. PTES energy value and heat engine annual capacity factor increase strongly as the contribution of variable renewable resources increases, heat pump capacity increases relative to heat engine capacity, or PTES cycling costs decrease. Grid scenarios in which the contribution of variable renewable energy is dominated by solar photovoltaics (PV) vs. wind produce inherently different PTES operational patterns. Annual PTES energy value within PV-dominated scenarios increased with storage capacity only up to approximately seven hours of full-load discharge capacity, whereas that within wind-dominated scenarios exhibited a continual increase with storage duration up to at least 16 hours.