Residential Demand Response (DR) is believed to be a feasible tool for increasing the power system operational flexibility and efficiency. In this paper, we develop a demand response methodology for residential peak load shaving. We present an optimal demand response model for scheduling the EV and storage space heating load in tandem. Realistic case studies based on Finnish household data is performed to showcase the effectiveness of the proposed methodology and the results are thoroughly compared with business as usual case. The simulation result suggests that proposed methodology can bring economic savings to the customers and reduce the peak power problem as well.
Apart from all the potential benefits offered by Electric Vehicles (EVs), they demand some structural changes in the power network to fullfill their charging needs. In this paper, we have studied the impact of EVs charging load on the distribution transformer loading. Different penetration levels of EVs have been considered to see the effect of EVs charging load on the capacities of distribution transformers. A real case study has been considered for 14 different distribution transformers in the city of Vantaa in Finland to have the real impact of the study. Analysis about the peak power of each transformer with and without EV load for different power tariffs has been done. Possible upgradation need of the network based on various penetration levels of EVs is also analyzed.
Global warming, depletion of fossil fuels and energy crisis has been the most debated and researched global challenges of the past few decades and more so from the beginning of the 21st century. One solution that apparently seems to answer our world's biggest challenges is the adoption and penetration of electric vehicles in both private and public transport. Though there has been a steady rise in the use of electric vehicles, yet the estimated targets of electric vehicles in the market share have failed to be met. At the current rate of adoption of EVs, it is not far-fetched to say that it might be too late to make an impact if rigorous efforts are not adopted to promote and incorporate EVs into the system. In this paper, we take a look at some of the issues that are largely preventing consumers from switching toward zero-emission cars.
The fast growth of the world’s energy demand in the modernized world has stirred many countries around the globe to focus on power generation by abundantly available renewable energy resources. Among them, wind energy has attained significant attention owing to its environment-friendly nature along with other fabulous advantages. However, wind-integrated power systems experience numerous voltage instability complexities due to the sporadic nature of wind. This paper comprehensively reviews the problems of voltage instability in wind-integrated power systems, its causes, consequences, improvement techniques, and implication of grid codes to keep the operation of the network secure. Thorough understanding of the underlying issues related to voltage instability is necessary for the development of effective mitigation techniques in order to facilitate wind integration into power systems. Therefore, this review delves into the origin and consequences of voltage instability, emphasizing its adverse impacts on the performance and reliability of power systems. Moreover, it sheds light on the challenges of integrating wind energy with existing grids. This manuscript provides a comprehensive overview of the essential features required for critical analysis through a detailed examination of Voltage Stability Indices (VSIs). To address voltage stability issues in wind-integrated power systems, this review examines diverse techniques proposed by researchers, encompassing the tools utilized for assessment and mitigation. Therefore, in the field of power system operation and renewable energy integration, this manuscript serves as a valuable resource for researchers by comprehensively addressing the complexities and challenges associated with voltage instability in wind-integrated power systems.
This paper analyses the impact of square wave pulse voltage deadtime on the partial discharge (PD) and the lifetime of turn-to-turn insulation. A bipolar repetitive pulse voltage with a deadtime of 0–10 μs is produced using double half-bridge solid-state switches having push–pull technology controlled by a field-programmable gate array. The mechanism of the discharge process at rising and falling edges of the pulse voltage before and after deadtime is analysed in detail. The discharge amplitude and PD probability at the rising/falling edges of the voltage waveform increase as the deadtime increases from 0 to 10 μs due to the remanent charges. The number of PD and their intensity is higher at the first rising/falling edges of pulse voltage as compared to the second rising/falling edges for all deadtimes 0–10 μs. As the deadtime increases beyond 2 μs, the number of PDs increases and concentrates at a specific phase angle of rising/falling edges. These localise discharges degrade the insulation material and reduce its lifetime. This study helps to identify the inverter-fed motor insulation faults due to deadtime. It can provide guidelines to motor insulation designers to determine the limit value of deadtime to compensate PD and ensure the safer operation of such motors.
Cardiovascular diseases are some of the underlying reasons contributing to the relentless rise in mortality rates across the globe. In this regard, there is a genuine need to integrate advanced technologies into the medical realm to detect such diseases accurately. Moreover, numerous academic studies have been published using AI-based methodologies because of their enhanced accuracy in detecting heart conditions. This research extensively delineates the different heart conditions, e.g., coronary artery disease, arrhythmia, atherosclerosis, mitral valve prolapse/mitral regurgitation, and myocardial infarction, and their underlying reasons and symptoms and subsequently introduces AI-based detection methodologies for precisely classifying such diseases. The review shows that the incorporation of artificial intelligence in detecting heart diseases exhibits enhanced accuracies along with a plethora of other benefits, like improved diagnostic accuracy, early detection and prevention, reduction in diagnostic errors, faster diagnosis, personalized treatment schedules, optimized monitoring and predictive analysis, improved efficiency, and scalability. Furthermore, the review also indicates the conspicuous disparities between the results generated by previous algorithms and the latest ones, paving the way for medical researchers to ascertain the accuracy of these results through comparative analysis with the practical conditions of patients. In conclusion, AI in heart disease detection holds paramount significance and transformative potential to greatly enhance patient outcomes, mitigate healthcare expenditure, and amplify the speed of diagnosis.
Contraction of resilience on generation side due to the introduction of inflexible renewable energy sources is demanding more elasticity on consumption side. It requires more intelligent systems to be implemented to maintain power balance in the grid and to fulfill the consumer needs. This paper is concerned about the energy balance management of the system using intelligent agent-based architecture. The idea is to limit the peak power of each individual household for different defined time regions of the day according to power production during those time regions. Monte Carlo Simulation (MCS) has been employed to study the behavior of a particular number of households for maintaining the power balance based on proposed technique to limit the peak power for each household and even individual load level. Flexibility of two major loads i.e. heating load (heat storage tank) and electric vehicle load (battery) allows us to shift the peaks on demand side proportionally with the generation in real time. Different parameters related to heating and Electric Vehicle (EV) load e.g. State of Charge (SOC), storage capacities, charging power, daily usage, peak demand hours have been studied and a technique is proposed to mitigate the imbalance of power intelligently.
Limited range of Electric Vehicle's (EV) battery in terms of number of miles per recharge, requires EV to recharge the battery at least once during the long trip. However, long charging durations are not acceptable in these cases, therefore fast charging of EV is needed. Fast charging of EV needs more charging power for relatively shorter period of time, which gives rise to overloading of the power network due to simultaneous charging of multiple EVs at the fast charging stations. This paper studies the power network requirements to meet the EV fast charging needs on highways. Different parameters are considered in the study, for instance, arrival order of EVs at the charging stations, number of charging slots per charging station, power rating of installed chargers, distance between charging stations, state of charge (SoC) of EVs arriving for charging, battery capacities, waiting time at charging stations and the duration of charging. Various scenarios are simulated using Monte Carlo Simulations (MCS) to see the impact of fast charging on the power network loading. The key finding is the peak load on individual fast charging stations with respect to different penetration levels of EVs on the road. A real highway case in Finland is considered in this study to show the rationality of the results.
Friction stir welding is used for a few things like mechanical, biomedical, aerospace, fabricating, and nuclear innovation. In this process, two standing-up workpieces are joined together without the need to dissolve the composites. The process of scouring between the material of the workpiece and the rotating device, which loosens the material in the vicinity of the device, produces heat. Gadget dares to make a solid stage joint that is energized along its entire length. The Inclination Point, Turn Speed, and Travel Speed are the main welding constraints that were considered in this examination. Information explains what's happening the aluminum composite's strength and hardness are noticeably diminished when the joints are present. By virtue of the force created by the linking framework, this can be shown to represent an over-development of the aluminum composite. Normal T6 warming exercises, however, restore the mechanical characteristics of the aluminum-aluminum joint. This demonstrated the viability of FSW for connecting metals that are both related and dissimilar. The construction of top-notch welds and the weld's mechanical characteristics are the foundation of this job.
This paper presents the development of fast charging infrastructure for Electric Vehicles (EVs) with the proportional increase in the number of EVs on highways. Development of charging infrastructure includes the installation of charging units in the urban or rural areas depending upon the need, forecast for the increased energy demand on power grid and consideration of capital invested on the system as a whole. Monte Carlo Simulations (MCS) have been performed to investigate the power demand, energy needed, utilization factor of each station and socket, and the Socket-to-EV ratio considering different waiting time scenarios. Socket-to-EV ratio varies with the limitation of waiting times offered to the EVs at charging stations. Socket-to-EV ratio is considered to be the parameter that tells the number of sockets needed to fulfill the charging need of EVs in a particular scenario. Utilization factor of charging sockets for different scenarios gives an estimate of power delivery hours of charging sockets to study the cost-and-benefit ratio of various cases.
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