In the near future the conventional fuel-based transportation system is expected to be entirely replaced by Electric Vehicles (EVs) due to their significant environmental benefits. While contemporary charging technologies demonstrate great potential to accelerate the growth of the EV market, several challenges remain to be addressed for their widespread advancement. The primary concerns of EV charging technology include charging time, speed, cost, reliability, power converter performance, and its impact on the utility grid. With this background, this study provides a detailed overview of various charging topologies used in EVs, which encompass the charging methods, power levels, as well as unidirectional and bidirectional AC-DC and DC-DC converters. Each analysis is carried out according to the viewpoints, effects, efficiency, power density, transfer speed, reliability, and limitations of charging converters, for both slow and fast-charging phenomena. It explores diverse EV charging methods, including CC, WC, and swapping stations, focusing on on-board and off-board charging, as well as advanced wireless technologies like inductive, capacitive, and optical charging. Moreover, EV charging impacts on the electrical grid are discussed, focusing on waveform distortion, voltage fluctuations, grid stability, frequency regulation, reactive power compensation, and load management. It emphasizes how EV helps balance out grid oscillations brought on by the unpredictability of the power grid. To explore the current charging techniques and suggest modern solutions, a deep literature assessment is undertaken to identify exciting research gaps. This research bridges gaps in the current model and opens possibilities for future solutions by thoroughly examining EV interactions with the smart grid. Ultimately, this review will aid academics in driving further research and development while offering industry professionals valuable guidance for optimizing and implementing reliable and efficient EV charging solutions.

Single-molecule techniques have shifted the paradigm of biological measurements from ensemble measurements to probing individual molecules and propelled a rapid revolution in related fields. Compared to ensemble measurements of biomolecules, single-molecule techniques provide a breadth of information with a high spatial and temporal resolution at the molecular level. Usually, optical and electrical methods are two commonly employed methods for probing single molecules, and some platforms even offer the integration of these two methods such as optofluidics. The recent spark in technological advancement and the tremendous leap in fabrication techniques, microfluidics, and integrated optofluidics are paving the way toward low cost, chip-scale, portable, and point-of-care diagnostic and single-molecule analysis tools. This review provides the fundamentals and overview of commonly employed single-molecule methods including optical methods, electrical methods, force-based methods, combinatorial integrated methods, etc. In most single-molecule experiments, the ability to manipulate and exercise precise control over individual molecules plays a vital role, which sometimes defines the capabilities and limits of the operation. This review discusses different manipulation techniques including sorting and trapping individual particles. An insight into the control of single molecules is provided that mainly discusses the recent development of electrical control over single molecules. Overall, this review is designed to provide the fundamentals and recent advancements in different single-molecule techniques and their applications, with a special focus on the detection, manipulation, and control of single molecules on chip-scale devices.

Smart buildings require an energy management system that can meet inhabitants’ demands with a reduced amount of energy consumed by the heating ventilation and air-conditioning system (HVAC), as well as the lighting and shading systems. This work provides a detailed review of available methods proposed in the literature for effective control of automated systems such as HVAC, lighting, shading, etc. Moreover, effective forecasting of renewable energy generations and loads, scheduling of loads, and efficient operations of thermal and electric energy storage are crucial elements for energy management systems for ensuring reliability and stability. In this work, these aspects of energy management systems, that have been popular over the last ten years, are analyzed. In addition, the development of internet-of-things (IoT)-based sensors widens the artificial intelligence (AI) and machine learning applications in smart buildings. However, this system can be vulnerable against cyber-attacks. The state of the art of AI and machine learning applications along with cyber security issues and solutions for smart building systems are discussed. Finally, some recommendations for future research trends and directions on smart building systems are provided. This work will provide a basic guideline and will also be very useful to researchers in the area of smart building systems in the future.

Load scheduling is an effective way of utilizing loads of smart residential buildings according to the preferences of the inhabitants or grid demands, while reducing the cost of energy. This work proposes objective functions for load scheduling to confine the cost of energy within the consumers’ preference range while keeping the load consumption closer to the load demand as possible, to minimize system loss during normal and pandemic condition such as COVID-19 periods, fulfilling the unique features of a net-zero energy building. The proposed objective function is implemented by considering the realistic grid power cost, levelized cost of renewable sources, battery, and incentives offered by the utility system existing in California, USA. In addition to three different types of days such as normal working days, weekends and pandemic situations, brown out power outages are considered as operating conditions. Particle swarm optimization (PSO) is utilized in all considered operating conditions. Two terms that account for the total energy cost savings and the total delayed/scheduled load over a fixed time horizon are formulated as performance indices to illustrate the effectiveness of the proposed objective functions for load scheduling. All of the cases are optimized by the Paticle Swarm Optimization (PSO) and non-optimized systems are simulated in the MATLAB environment. It is evident from the simulation results that the proposed objective function is very efficient in tackling the energy resources, loads and grid power to maximize cost savings and minimize shifting of loads for later hours for normal and pandemic situations in net-zero energy buildings. Moreover, it is equally effective in responding to any emergency situations such as brown out energy crisis situations, which are not considered in the literature so far. In all cases, the performance index also validates the effectiveness of the proposed objective function-based scheduling system for net-zero energy buildings.

This paper investigates the month-wise impact of COVID-19 conditions on residential load due to people’s presence at home during office hours in Memphis city, Tennessee, USA. The energy consumption patterns of four consumers are analyzed based on the data available from pre-COVID to continuing COVID-19 situations. It is observed that the energy consumptions from April 2020 to June 2021 for all families have increased during office hours than that of pre-COVID months. Therefore, the impacts of the increased loads on distribution transformer are analyzed in terms of hottest spot, top-oil temperature, and loss of transformer life. Moreover, an experimental setup is made to produce the harmonics in currents of non-linear residential load which has detrimental effects on temperature rise of distribution transformer. In addition, this work proposes implementation of harmonic filter at the load side considering the impact of harmonics produced by loads to reduce the temperature rise due to the increased load consumption and presence of harmonics in currents produced by the load. The rise in temperatures and the loss of life of distribution transformer with and without the proposed solutions are simulated in MATLAB to show the efficacy of the proposed solution. Moreover, cost value analysis among different methods, which can be implemented to reduce the adverse impact on distribution transformer, are provided to rank the available methods.