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• This paper exposes two special AC machines adaptable for electric vehicle application. • Their mathematical models are described and defined. • Their corresponding field oriented vector control topology was designed and verified through simulation steps. There has been a rapid adoption of electric vehicles (EVs), and the industry has been looking for the most enhanced traction system to improve performance and reliability. Such specifications are mostly dependent on the magnetic materials for brushless DC or AC machines, and typically EVs are manufactured with those machines. In the past two decades, there have also been improved performance ratings of power electronic devices. On one hand, making possible AC motors more suitable for EVs. On the other hand, it allows further advanced topology circuits to fit machines with two rotors or two stators. This paper shows two configurations of electrical machine control for EVs, discuss the pros and cons, and corresponding mathematical models have been developed in order to enhance a Field Oriented Control (FOC) strategy, which has been analyzed and studied through MATLAB based simulations, allowing novel design based on the analytical models developed by the authors. [Display omitted] [ABSTRACT FROM AUTHOR]

Background: Analgesics and antimicrobial agents are commonly prescribed drugs in dental patients. Therefore, monitoring their use and adverse reactions is very important. Aims and Objectives- To evaluate the prescription patterns and associated adverse drug reactions of analgesics and antimicrobial agents in dental patients. Materials and Methods: This is an observational study conducted in a tertiary care centre in northern India from July 2022 to September 2022. The study assessed a total of 100 prescriptions from dental practitioners. The majorities of patients visiting were in the age group of 21-30 years. The standard ADR reporting forms of CDSCO were used to record all adverse events experienced by the patients. Adverse drug reaction's causality assessment was done using Naranjo's Scale and the severity assessment by Modified Hartwig & Siegel scale. Results: The majorities of patients visiting the dental practitioners were males (53%) and the most common dental infections for which antimicrobials and analgesics prescribed were acute/chronic Caries (33%), acute/chronic Periodontitis (30%), Pericoronitis (19%), Periapical abscess (4%), Post RCT (5%), Trismus (2%), Sialadenitis (1%), Post operative TMJ ankylosis (1%), Oral Cancer (1%), Oroantral Fistula (1%), Ameloblastoma of jaw (1%), Cellulitis (2%). The antimicrobials most frequently used for management of the observed conditions were Amoxycillin + potassium clavulanate(67%), Cefixime (27%), cefixime + clavulanic acid (2%), Ceftriaxone (inj.) (1%), Amikacin sulphate (1%), Metronidazole (2%) and the most common Analgesics prescribed were Aceclofenac + paracetamol (71%), Paracetamol (5%), Diclofenac sodium(23%), Dynapar AQ (1%) Out of 100 patients, 19 reported adverse drug reactions (ADRs), and out of 19 patients with ADRs, 52 different types of adverse drug reactions were reported. Conclusion: Our findings suggested caries as the most frequently reported conditions for which Antimicrobials and Analgesics were prescribed. Amoxicillin+ Potassium Clavulanate followed by cefixime was the most commonly used antimicrobials. 19% patients reported ADRs of which nausea and diarrhea were most frequent. [ABSTRACT FROM AUTHOR]

Summary: The continuous increase of the distributed energy resources (DERs) penetration levels leads to voltage stability problems in the distribution system. One of the approaches for the mentioned emerging challenge is the proper placement of automatic voltage regulators (AVRs). This paper investigates the optimal placement and sizing of AVRs in a distribution network by presenting a new modification of the teaching‐learning‐based optimization (TLBO) algorithm. The objective functions consist of minimizing the distribution system voltage deviation, energy generation cost, and electrical losses. The modification improves the convergence velocity and accuracy of the TLBO algorithm using the combination of mutation technique and quasi‐opposition‐based‐learning concept. This paper compares the performance of the proposed algorithm with other famous evolutionary algorithms. The test distribution system contains installed DERs that work more efficiently after the placement of AVRs based on the mentioned objective functions by the proposed optimization algorithm. The simulation results display the best optimization algorithms for AVRs placement with a significant level of less than 0.10 (ie, probability‐value). The proposed multiobjective optimization algorithm's considerable merit is the accuracy and convergence velocity in solving this specific optimization problem. [ABSTRACT FROM AUTHOR]

Plug-in hybrid electric vehicles (PHEVs) and plug-in electric vehicles (PEVs) have gained enormous attention for their ability to reduce fuel consumption in transportation and are, thus, helpful in the reduction of the greenhouse effect and pollution. However, they bring up some technical problems that should be resolved. Due to the ever-increasing demand for these PHEVs, the simultaneous connection of large PEVs and PHEVs to the electric grid can cause overloading, which results in disturbance to overall power system stability and quality and can cause a blackout. Such situations can be avoided by adequately manipulating power available from the grid and vehicle power demand. State of charge (SoC) is the leading performance parameter that should be optimized using computational techniques to charge vehicles efficiently. In this research, an efficient metaheuristic algorithm, accelerated particle swarm optimization (APSO), and its five variants were applied to allocate power to vehicles connected to the grid intelligently. For this, the maximization of average SoC is considered a fitness function, and each PHEV can be connected to the grid once a day so that the maximum number of cars can be charged daily. To statistically compare the performance of these six algorithms, one-way ANOVA was used. Simulation and statistical results obtained by maximizing this highly non-linear objective function show that accelerated particle swarm optimization with Variant 5 achieved some improvements in terms of computational time and best fitness value. The APSO-5 solution has a considerable percentage increase compared with the solution of other variants of APSO for the four PHEV datasets considered. Moreover, after 30 trials, APSO 5 gives the highest possible fitness value among all the algorithms. [ABSTRACT FROM AUTHOR]

In developing countries, many areas are deprived of electrical energy. Access to cleaner, more affordable energy is critical for improving the poor's living conditions in developing countries. With the advent of smart grid technology, the integration and coordination of small grids, known as nanogrids, has become very easy. The purpose of this research is to propose a nanogrid model that will serve the purpose of providing the facility of electrical power to the poor rural community in Pakistan using hybrid renewable energy sources. This paper targets the electrification of a poor rural community of Akora Khatak, a small district located in Pakistan. The mathematical modeling of solar and wind energy, neural network-based forecasting of solar irradiance and wind velocity, and the social analysis to calculate the payback period for the community have been discussed in this paper. [ABSTRACT FROM AUTHOR]

Europe anchored aspiring targets in achieving climate neutrality and this motivates the research communities to analyze, investigate and frame strategies to achieve the goals in the stipulated timeframe. This study reviews the effective decarbonization strategies in the context of Europe's climate neutral vision. Initially, the study analyzes the reasons for ever-increasing emissions and investigates the perception of decarbonization in line with various influencing factors such as population size, economic growth, energy intensity, emission intensity, innovation, affordability and time. Subsequently, an in-depth qualitative analysis is performed to force out the challenges that Europe faces in decarbonizing the heating sector from the aspects, such as using clean energy resources, effective heat energy conversion and management approaches. Sustainable approaches and practices are proposed to mitigate carbonization and to promote carbon sink in line with the major problems associated with various sectors such as building, energy, industry and transportation sector. Furthermore, the roles of digitalization in decarbonization are explored and the inherent challenges are also discussed. This study also reviews various decarbonization policies that can direct the governments' action to effectively make a transition towards a climate-neutral society. The key findings highlight that solar energy utilization in small-scale is relatively not preferable for heat energy conversion in Europe due to climatic conditions, while district heating network with bio- and geothermal energy resource highly favors clean heat transformation scenario. In addition, 3D printing has a prodigious role to reduce building lifecycle emissions and hybrid policies as well as reward-based policies yields better outcomes. Promoting hydrogen utilization and carbon capture storage and utilization technologies can pivot climate neutrality in the sectors that are difficult to decarbonize. On the other hand, it can be observed that more focus is provided to reduce emissions and significantly less attention is focused on carbon sink. • Serious complexity exists in decarbonization with the influence of numerous interlinked factors. • Bio- and geothermal based CHP and district heating can effectively decarbonize the heating sector. • The transport and industrial sector depend on hydrogen energy to witness effective decarbonization. • Coherent policies across borders are suggested for strengthening Europe's decarbonization vision. [ABSTRACT FROM AUTHOR]

The present research article focuses on an analytically based method for the optimal allocation and sizing of a renewable energy source (RES) capable of injecting both active and reactive powers in the distribution network. The placement of distributed generation (DG) in the distribution network reduces the magnitude of branch current in between the reference bus and the bus where DG is to be installed. Due to this, system power loss decreases significantly. The proposed method considers different levels of load in addition to peak load demand. The goal of the developed method is to minimize system losses by optimal DG allocations. In the proposed method, the optimum size of the DG is obtained on the basis of maximum loss saving criterion. For the execution of proposed method, only a base case load flow solution is required. The developed method has been tested on IEEE 69-bus and 33-bus radial distribution networks. On the basis of obtained results, it has been realized that the developed method is more capable of diminishing system energy losses. [ABSTRACT FROM AUTHOR]

• Proposed a novel SDG 7 composite index for assessing energy sustainability. • Dimensions include clean energy, security, emission, intensity, accessibility. • Proposed ANMMO normalization method to reduce the influence of outliers. • SDG 7 composite index depicts a more accurate reality than the actual SDG 7 index. • Iceland, Norway, and Sweden tops in energy sustainability aspects. The Sustainable Development Goal 7 endeavors to ameliorate the energy system towards sustainability. Monitoring the country's progress to the goal will be of utmost for the government to take suitable actions and thus, constructing a performance monitoring index for Sustainable Development Goal 7 would tune the pace of implementation. This study aims to develop a novel Sustainable Development Goal 7 or Energy Sustainability Composite Index to assess the energy sustainability performance. Since Europe tends to have diverged efforts towards energy sustainability, assessing them with the proposed Sustainable Development Goal 7 composite index would provide the evidence needed for effective sustainable development strategies. By describing Europe, the authors signify 40 European countries and the selection of country depends on the availability of all the data that are required for the energy sustainability assessment. In this study, the analyzed energy sustainability aspects include clean energy conversion, energy security, energy accessibility, energy intensity and carbon intensity. The results show that Iceland, Norway, and Sweden tops in energy sustainability aspects with scores of 0.7313, 0.6967, and 0.6313 (on a scale of 0 to 1), respectively. The proposed Sustainable Development Goal 7 composite index is also compared with the actual Sustainable Development Goal 7 index, which comprises the indicators defined by the United Nations. The prime difference between the proposed Sustainable Development Goal 7 composite index and the actual Sustainable Development Goal 7 index resides in the consideration of energy security and carbon intensity indicators and in the framework designed to evaluate the clean energy prevalence. The evaluated actual Sustainable Development Goal 7 index scores of Germany and France are 0.4915 and 0.4656, respectively. On comparing with the proposed Sustainable Development Goal 7 composite index scores, the scores decreased by 20.9% for Germany and increased by 7.2% for France. The robustness of the proposed composite index relies on reducing the effect of outliers by using a modified min–max methodology, namely Aggregated Normalization based on Maximum and Minimum Outliers for normalization and the subsequent weightage allocation criteria utilized in Analytic Hierarchy Process methodology. Sensitivity analyses highlighted that the clean energy indicator is the most influencing indicator for the designed composite index. Nevertheless, uncertainty analysis indicates that the weightage scenario has a more prominent influence than various normalization and aggregation methods. [ABSTRACT FROM AUTHOR]

The objective of the present paper is to study the optimum installation of Non-dispatchable Distributed Generations (NDG) in the distribution network of given sizes under the given scheme. The uncertainty of various random (uncertain) parameters like load, wind and solar operated DG besides uncertainty of fuel prices has been investigated by the three-point estimate method (3-PEM) and Monte Carlo Simulation (MCS) based methods. Nearly twenty percent of the total number of buses are selected as candidate buses for NDG placement on the basis of system voltage profile to limit the search space. Weibull probability density function (PDF) is considered to address uncertain characteristics of solar radiation and wind speed under different scenarios. Load uncertainty is described by Standard Normal Distribution Function (SNDF). To investigate the solution of optimal probabilistic load flow (OPLF) three-point PEM-based technique was applied. For optimization, Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and GA-PSO hybrid-based Artificial Intelligent (AI) based optimization techniques are employed to achieve the optimum value of the multi-objectives function. The proposed multi-objective function comprises loss and different costs. The proposed methods have been applied to IEEE 33- bus radial distribution network. Simulation results obtained by these techniques are compared based on loss minimization capability, enhancement of system bus voltage profile and reduction of cost and fitness functions. The major findings of the present study are the PEM-based method which provides almost similar results as MCS based method with less computation time and as far as loss minimization capacity, voltage profile improvement etc. is concerned, the hybrid-based optimization methods are compared with GA and PSO based optimization techniques. [ABSTRACT FROM AUTHOR]

An innovative union of fuzzy controller and proportional-integral-derivative (PID) controller under the environment of fractional order (FO) calculus is described in the present study for an isolated hybrid power system (IHPS) in the context of load frequency control. The proposed controller is designated as FO-fuzzy PID (FO-F-PID) controller. The undertaken model of IHPS presented here involves different independent power-producing units, a wind energy-based generator, a diesel engine-based generator and a device for energy storage (such as a superconducting magnetic energy storage system). The selection of the system and controller gains was achieved through a unique quasi-oppositional harmony search (QOHS) algorithm. The QOHS algorithm is based on the basic harmony search (HS) algorithm, in which the combined concept of quasi-opposition initialization and HS algorithm fastens the profile of convergence for the algorithm. The competency and potency of the intended FO-F-PID controller were verified by comparing its performance with three different controllers (integer-order (IO)-fuzzy-PID (IO-F-PID) controller, FO-PID and IO-PID controller) in terms of deviation in frequency and power under distinct perturbations in load demand conditions. The obtained simulation results validate the cutting-edge functioning of the projected FO-F-PID controller over the IO-F-PID, FO-PID and IO-PID controllers under non-linear and linear functioning conditions. In addition, the intended FO-F-PID controller, considered a hybrid model, proved to be more robust against the mismatches in loading and the non-linearity in the form of rate constraint under the deviation in frequency and power front. [ABSTRACT FROM AUTHOR]

Power suppliers in a dynamic power market can achieve full benefit by introducing a bidding strategy mechanism. In the power sector, renewable resources have significant gradual usage and their effect on the production of detailed bidding approaches is becoming further complicated in the industry. Due to the irregular nature of these renewable resources and because they are subject to several fluctuations, there is an inherent issue with generating electricity. Taking these considerations into account, attempts have been made to create a model of bidding strategy to optimize the benefit of the electricity producers using the oppositional gravitational search algorithm. The Weibull and Beta distribution functions are utilized to describe the stochastic characteristics of the wind-speed profile and solar-irradiation, respectively. For the IEEE-30 and IEEE-57 frameworks, the suggested method is being checked and explained. In comparison to other optimization approaches, the results of this approach were taken into account, and it was discovered that it outperformed other techniques in addressing bid difficulties. In addition, it is worth noting that the impact of renewable energy on the bidding strategy lowered market clearing and thermal power generating costs, and encouraged renewable influenced producers to put forward the excess electricity into the real-time market. [ABSTRACT FROM AUTHOR]

The access of large-scale electric vehicles (EVs) will increase the network loss of medium voltage distribution network, which can be alleviated by adjusting the network structure and orderly charging for EVs. However, it is difficult to accurately evaluate the charging efficiency in the orderly charging of electric vehicle (EV), which will cause the scheduling model to be insufficiently accurate. Therefore, this paper proposes an EV double-layer scheduling model based on the isolated bidirectional DC–DC (IBDC) converter optimal efficiency model, and establishes the hierarchical and partitioned optimization model with feeder–branch–load layer. Firstly, based on the actual topology of medium voltage distribution network, a dynamic reconfiguration model between switching stations is established with the goal of load balancing. Secondly, with the goal of minimizing the branch layer network loss, a dynamic reconstruction model under the switch station is established, and the chaotic niche particle swarm optimization is proposed to improve the global search capability and iteration speed. Finally, the power transmission loss model of IBDC converter is established, and the optimal phase shift parameter is determined to formulate the double-layer collaborative optimization operation strategy of electric vehicles. The example verifies that the above model can improve the system load balancing degree and reduce the operation loss of medium voltage distribution network. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) is a highly promising energy source because of its environment friendly property. However, there is an uncertainty present in the modeling of PV modules owing to varying irradiance and temperature. To solve such uncertainty, the fuzzy logic control-based intelligent maximum power point tracking (MPPT) method is observed to be more suitable as compared with conventional algorithms in PV systems. In this paper, an isolated PV system using a push pull converter with the fuzzy logic-based MPPT algorithm is presented. The proposed methodology optimizes the output power of PV modules and achieves isolation with high DC gain. The DC gain is inverted into a single phase AC through a closed loop fuzzy logic inverter with a low pass filter to reduce the total harmonic distortion (THD). Dynamic simulations are developed in Matlab/Simulink by MathWorks under linear loads. The results show that the fuzzy logic algorithms of the proposed system efficiently track the MPPT and present reduced THD. [ABSTRACT FROM AUTHOR]

An increase in demand for renewable energy resources, energy storage technologies, and electric vehicles requires high-power level DC-DC converters. The DC-DC converter that is suitable for high-power conversion applications (i.e., resonant, full-bridge or the dual-active bridge) requires magnetic transformer coupling between input and output stage. However, transformer design in these converters remains a challenging problem, with several non-linear scaling issues that need to be simultaneously optimized to reduce losses and maintain acceptable performance. In this paper, a new transformer-less high step-up boost converter with a charge pump capacitorand capacitor-inductor-diode CLD cell is proposed using dynamic modeling. The experimental and simulation results of the proposed converter are carried out in a laboratory and through Matlab Simulink, where 10 V is given as an input voltage, and at the output, 100 V achieved in the proposed converter. A comparative analysis of the proposed converter has also been done with a conventional quadratic converter that has similar parameters. The results suggest that the proposed converter can obtain high voltage gain without operating at the maximum duty cycle and is more efficient than the conventional converter. [ABSTRACT FROM AUTHOR]