Search

The power consumption of buildings over the course of each minute, hour, day and season plays a major role in how this load influences the Electric Power System voltage and frequency, and vice versa. This consumption is based on the building’s load component types, efficiencies, and how they consume power and react to changes in real time. Due to this complexity, standard full-building load models are typically voltage-invariant. This paper proposes a novel framework to transform these voltage-invariant building load models into fully time- and voltage-dependent load profiles using available data on the voltage sensitivity of individual load components. While a voltage-dependent building model could theoretically be generated from static load models of every component in a building, this approach faces two challenges: first, load models representing all load components are impractical to develop for all possible load component types; second, building energy consumption is never measured or modeled at the individual component level. The proposed framework compiles available component data in the form of static ZIP load model parameters, and maps them into the end use categories utilized by standard building modeling programs. The voltage sensitivity of each end use category is then bounded by the extrema of the component models within it. This framework is applied to a load profile case study representing the aggregate U.S. residential building stock. In addition to the minimum/maximum conditions, a load profile based on typical load composition and weighted ZIP parameters is generated for the same building stock. The results show that for a 10% drop in voltage, using the least sensitive ZIP parameters, active power is expected to be 3% to 14% lower than nominal, depending on the season and time of day. Using the most sensitive ZIP parameters, the active power is expected to be 9% to 20% lower than nominal, also depending on the season and time of day.

Recent technology development and penetration of advanced metering infrastructure (AMI), advanced building control systems, and the internet-of-things (IoT) in the built environment are providing detailed information on building operation, performance, and user’s comfort and behavior. Building owners can obtain a wide range of energy consumption details at various levels of time granularity to augment their decisions as they manage the building operation and interact with the grid. AMI data are providing a new level of detail and visibility that may enhance building services and assets in the smart grid domain and make buildings inch closer to becoming a grid-interactive energy efficient buildings (GEB). While utility-installed AMI typically records energy consumption at a 15, 30, or 60-minute resolution, building-owner-installed metering can record energy consumption at one-minute or sub-minute time scales, providing information about how much the energy consumption varies from one sub-minute to the other (i.e. variability) at a finer time resolutions than typically available from AMI. This paper examines one-minute building load profile data sets and presents a framework to study, define, extract, quantify and analyze variability in buildings’ load profiles. The discussion of variability and its analysis is based on a case study of an actual sub-minute time-resolution data set, collected in 2019, for two buildings in a Midwest state in the USA. The result shows that for the case studies, the level of variability in an end-use category is not simply proportional to its consumption. Furthermore, distinct and predictable daily variability patterns emerge in end-use load categories. This information is useful for a host of applications including prediction, forecasting, and modeling.

The penetration of distributed energy resources (DERs) on the electric power system is changing traditional power flow and analysis studies. DERs may cause the systems' protection and control equipment to operate outside their intended parameters, due to DERs' variability and dispatchability. As this penetration grows, hosting capacity studies as well as protection and control impact mitigation become critical components to advance this penetration. In order to conduct such studies accurately, the electric power system's distribution components should be modeled correctly, and will require realistic time series loads at varying temporal and spatial conditions. The load component consists of the built environment and its load profiles. However, large-scale building load profiles are scarce, expensive, and hard to obtain. This article proposes a framework to fill this gap by developing detailed and scalable synthesized building load profile data sets. Specifically, a framework to extract load variability characteristics from a subset of buildings' empirical load profiles is presented. Thirty-four discrete wavelet transform functions with three levels of decomposition are used to extract a taxonomy of load variability profiles. The profiles are then applied to modeled building load profiles, developed using the energy simulation program EnergyPlus®, to generate synthetic load profiles. The synthesized load profiles are variations of realistic representations of measured load profiles, containing load variabilities observed in actual buildings served by the electric power system. The paper focuses on the framework development with emphasis on variability extraction and application to develop 750 synthesized load profiles at a 15-minute time resolution.

There are many socio-economic and travel-related factors of a given region that will impact the local adoption of Battery Electric Vehicles (BEVs). In this paper, eleven states are considered to study BEV uptake, and a total of 242 features are used to characterize each zip code. The results are analyzed to determine the degree of correlation between zip code features and BEV uptake. Preliminary results show several features well-correlated with BEV uptake, in particular individuals or households with high income in a single-dwelling unit and owning more than one vehicle.

Plug-in Electric Vehicle (PEV) users tend to plugin and leave their vehicles for an extended periods of time in PEV specified areas of public parking lots. The extended amount of idle time concerns other PEV users who need to charge their vehicles. Several well-known, supervised machine-learning methods were applied by using data from 27,481 charging sessions. The data was obtained from existing public charging stations within Nebraska, predicting idle time calculations to be able to help state leaders minimize prolonged charging sessions. XGboost outperforms the other methods in the results with Root Mean Square Error equivalent to 0.9552 and R2 equating to 40.80%. Additionally, this study considers the relative importance of the input variable. By using the proposed data-driven strategy to predict the idle time at public charging stations, PEV users PEV can decide to wait or consider a different charging station.

While the increase in EV use is a positive step towards embracing green technology, the heightened energy demands resulting from this rapid growth present major challenges to local energy grid load management. For context, the new EVs being deployed store approximately 100 kWh, about four times the daily electricity use of the average household in the U.S. Current local distribution grids do not have the capacity to accommodate these massively increased loads. For this reason, it is important that local utilities have a full understanding of the charging demand within a given grid. The main objective of this research is to deepen the understanding of charging behavior at the household level using real data. Specifically, data from existing 417 residential Level-2 charging stations, located in Omaha, Nebraska, USA, are collected and analyzed. The results show a clear pattern in user behavior for the starting time of sessions, as well as the connection duration, charging duration, and subsequent energy demand.

Plug-in Electric Vehicle (PEV) charging stations can have a significant influence on the electric grid and its reliability. In order to evaluate the influence of rapid penetration of PEVs and their charging infrastructure on the electric grid, and assess their potential benefits to the future micro-grid, statistical analysis of PEV charging behavior is essential for planning as well as running public charging stations in different areas. In this paper, 27,481 charging sessions from public charging stations in the state of Nebraska are analyzed to determine the relationship between energy consumption, time, and location of charging. A multinomial logistic regression model is presented to aid utility companies in anticipating the charging demand of PEV users, as well as the impact on the electric grid at increasing PEV penetration rates.

We report optically pumped 1 μm low threshold GaAs photonic crystal surface emitting lasers. The GaAs photonic crystal is etched on 3-period InGaAs/AlGaAs MQW heterostructure. A laser peak is achieved at 1,006 nm with a linewidth of 0.6 nm at 5 kW/cm2 threshold power density.

Plug-in electric vehicles (PEVs) play significant role in the development of green cities, since they have less impact on the environment compared to conventional vehicles. To promote PEV adoption, charging infrastructure should be deployed at strategic locations that are readily accessible to the public to mitigate range anxiety of PEV users. In this paper, 17,675 charging events were collected from charging stations located in the state of Nebraska to accurately quantify the factors that have an influential role in determining user connection periods at public charging stations. This analysis aims to aid policy-makers in understanding the charging behaviors of PEV users, in order to optimize the characteristics, size and locations of public charging stations.

The requirements of systems using digitized computer images demand the use of image compression schemes. An image compression scheme based on variable resolution (VR) sensing is outlined. Performance comparisons are made with other compression methods, and a prototype teleconferencing system based on VR is introduced. >

There exists considerable promise for the use of charge‐coupled device (CCD) imagers in the fast recording of parts of macromolecular crystal Laue diffraction patterns. As part of this development CCD tests have been made with direct detection of Laue patterns from a small molecule test crystal and a protein crystal. Merging R factors (on intensity), for strong reflections, of 3% have been obtained. A time‐slicing scheme for a CCD camera is discussed based on the stacking of slices held in storage in the CCD in the submillisecond time resolution range.