Optimization of Energy Storage Systems in HEV\u27s

This paper introduces a novel battery model inspired by molecular structures mainly for applications in HEV’s. This novel 3-D adaptive battery topology shows potential for improvement in input and output performance as well as the charging/ discharging efficiency of batteries. The proposed topology provides flexible connections between battery cells to achieve different configurations of battery. A new switching matrix has been developed to achieve the required configurations. Preliminary simulations provide promising results for an adaptive 3-D battery configuration. Comparison between traditional battery configurations and the adaptive 3-D configuration is considered. A significant improvement in power curves is achieved by the proposed topology

Addressable And Energy Management System For The Built Environment (I)

The increasing awareness for a cleaner earth has created more interests in the electric vehicle (EV) technology. Electric vehicles (EVs) will not only cause a reduction in our current greenhouse gas emissions, but also stop or reverse the trend at which our natural resources are being depleted. However, the introduction of the EVs into our societies will still require energy usage in the form of electricity. Integration of charging infrastructures will eventually be required in the built environment for these vehicles. This will add to the current energy consumption in the built environment which will mean more emission of CO2, NO2 etc., in the electrical power generation process. Therefore, this research looks at reducing energy wastage as a way of saving energy for the future integration of EVs into the built environment

A Review of Approaches for Sensing, Understanding, and Improving Occupancy-Related Energy-Use Behaviors in Commercial Buildings

Buildings currently account for 30–40 percent of total global energy consumption. In particular, commercial buildings are responsible for about 12 percent of global energy use and 21 percent of the United States’ energy use, and the energy demand of this sector continues to grow faster than other sectors. This increasing rate therefore raises a critical concern about improving the energy performance of commercial buildings. Recently, researchers have investigated ways in which understanding and improving occupants’ energy-consuming behaviors could function as a cost-effective approach to decreasing commercial buildings’ energy demands. The objective of this paper is to present a detailed, up-to-date review of various algorithms, models, and techniques employed in the pursuit of understanding and improving occupants’ energy-use behaviors in commercial buildings. Previous related studies are introduced and three main approaches are identified: (1) monitoring occupant-specific energy consumption; (2) Simulating occupant energy consumption behavior; and (3) improving occupant energy consumption behavior. The first approach employs intrusive and non-intrusive load-monitoring techniques to estimate the energy use of individual occupants. The second approach models diverse characteristics related to occupants’ energy-consuming behaviors in order to assess and predict such characteristics’ impacts on the energy performance of commercial buildings; this approach mostly utilizes agent-based modeling techniques to simulate actions and interactions between occupants and their built environment. The third approach employs occupancy-focused interventions to change occupants’ energy-use characteristics. Based on the detailed review of each approach, critical issues and current gaps in knowledge in the existing literature are discussed, and directions for future research opportunities in this field are provided

Framework for Extracting and Characterizing Load Profile Variability Based on a Comparative Study of Different Wavelet Functions

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\u27 protection and control equipment to operate outside their intended parameters, due to DERs\u27 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\u27s 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\u27 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

A Comparative Study of Three Feedback Devices for Residential Real-Time Energy Monitoring

Residential energy consumption accounts for 21% of the total electricity use in the United States. Unfortunately, research indicates that almost 41% of this power is wasted. Changing the way that consumers use energy may be important in reducing home energy consumption. This paper looks at whether the implementation of certain real-time energy monitors has an impact on the residential rate of energy consumption in a metropolitan area with relatively low electricity rates. In the following case study, 151 Omaha residences were equipped with two variants of the Aztech In-Home Display (Aztech) as well as the Blue Line Power Cost Monitor (PCM) real-time energy monitors for a period of 16 months. The results of the data, 30 days after installation, revealed a statistically insignificant reduction of 12% in mean electrical consumption in houses equipped with a PCM and no reduction in mean consumption in homes using either variants of the Aztech device when compared to a randomly selected control sample. However, they proved effective in the short term if utilized by utilities for mass distribution to foster awareness among participating residents of their own patterns of residential electricity consumption and on the environmental impacts of energy saving

A Case Study to Quantify Variability in Building Load Profiles

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\u27s 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\u27 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

Data-Driven Charging Demand Prediction at Public Charging Stations Using Supervised Machine Learning Regression Methods

Plug-in Electric Vehicle (PEV) user charging behavior has a significant influence on a distribution network and its reliability. Generally, monitoring energy consumption has become one of the most important factors in green and micro grids; therefore, predicting the charging demand of PEVs (the energy consumed during the charging session) could help to efficiently manage the electric grid. Consequently, three machine learning methods are applied in this research to predict the charging demand for the PEV user after a charging session starts. This approach is validated using a dataset consisting of seven years of charging events collected from public charging stations in the state of Nebraska, USA. The results show that the regression method, XGBoost, slightly outperforms the other methods in predicting the charging demand, with an RMSE equal to 6.68 kWh and R2 equal to 51.9%. The relative importance of input variables is also discussed, showing that the user’s historical average demand has the most predictive value. Accurate prediction of session charging demand, as opposed to the daily or hourly demand of multiple users, has many possible applications for utility companies and charging networks, including scheduling, grid stability, and smart grid integration

Resiliency of Smart Power Meters to Common Security Attacks

AbstractThe development of Smart Grid power systems is gaining momentum in many countries leading to massive deployment of smart meters to realize the envisioned benefits. However, there are several concerns among the consumer communities and the service providers with respect to information security when it comes to the deployment of smart meters. This paper attempts to address the main challenge related to smart grid information security by examining the resiliency of smart meters to security threats and attacks. Several common information security attacks are being used to study their impact on the performance of smart meters in a controlled laboratory environment. Results obtained showed drastic effect on the functionality of smart meters and their associated data gathering servers

Framework to Develop Time- and Voltage-Dependent Building Load Profiles Using Polynomial Load Models

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\u27s 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

Cognitive Radio for Smart Grid with Security Considerations

In this paper, we investigate how Cognitive Radio as a means of communication can be utilized to serve a smart grid deployment end to end, from a home area network to power generation. We show how Cognitive Radio can be mapped to integrate the possible different communication networks within a smart grid large scale deployment. In addition, various applications in smart grid are defined and discussed showing how Cognitive Radio can be used to fulfill their communication requirements. Moreover, information security issues pertained to the use of Cognitive Radio in a smart grid environment at different levels and layers are discussed and mitigation techniques are suggested. Finally, the well-known Role-Based Access Control (RBAC) is integrated with the Cognitive Radio part of a smart grid communication network to protect against unauthorized access to customer’s data and to the network at large