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As the world's demand for sustainable and reliable energy source intensifies, the need for efficient energy storage systems has become increasingly critical to ensuring a reliable energy supply, especially given the intermittent nature of renewable sources. There exist several energy storage methods, and this paper reviews and addresses their growing requirements. In this paper, the energy storage options are subdivided according to their primary discipline, including electrical, mechanical, thermal, and chemical. Different possible options for energy storage under each discipline have been assessed and analyzed, and based on these options, a handsome discussion has been made analyzing these technologies in the hybrid mode for efficient and reliable operation, their advantages, and their limitations. Moreover, combinations of each storage element, hybrid energy storage systems (HESSs), are systems that combine the characteristics of different storage elements for fulfilling the gap between energy supply and demand. HESSs for different storage systems such as pumped hydro storage (PHS), battery bank (BB), compressed air energy storage (CAES), flywheel energy storage system (FESS), supercapacitor, superconducting magnetic coil, and hydrogen storage are reviewed to view the possibilities for hybrid storage that may help to make more stable energy systems in the future. This review of combinations of different storage elements is made based on the previous literature. Moreover, it is assessed that sodium-sulfur batteries, lithium-ion batteries, and advanced batteries are the most helpful element in HESSs, as they can be hybridized with different storage elements to fulfill electricity needs. The results also show that HESSs outperformed other storage systems and, hence, hybridizing the characteristics of different storage elements can be employed for optimizing the performance of energy storage systems. [ABSTRACT FROM AUTHOR]

Predicting the need for modeling and solutions is one of the largest difficulties in the electricity system. The static-constrained solution, which is not always powerful, is provided by the Gradient Method Power Flow (GMPF). Another benefit of using both dynamic and transient restrictions is that GMPF will increase transient stability against faults. The system is observed under contingency situations using the Dynamic Stability for Constrained Gradient Method Power Flow (DSCGMPF). The population optimization technique is the foundation of a recent algorithm called Training Learning Based Optimization (TLBO). The TLBO-based approach for obtaining DSCGMPF is implemented in this work. The total system losses and the cost of the individual generators have been optimized. Analysis of the stability limits under contingency conditions has been conducted as well. To illustrate the suggested approaches, a Standard 3 machine 5-bus system is simulated using the MATLAB 2022B platform. [ABSTRACT FROM AUTHOR]

Hybrid microgrids, integrating local energy resources, present a promising but challenging solution, especially in areas with limited or no access to the national grid. Reliable operation of off-grid energy systems necessitates sustainable energy sources, given the intermittent nature of renewables. While fossil fuel diesel generators mitigate risks, they increase carbon emissions. This study assesses the viability of integrating a very small modular renewable energy reactor into a microgrid for replacing conventional diesel generators, substantially curbing greenhouse gas emissions. A comprehensive analysis, including design and economic evaluation, was conducted for an off-grid community microgrid with an annual generation and load of 8.5 GWh and 7.8 GWh, respectively. The proposed microgrid configurations incorporate very small modular reactors, alongside solar, wind, and battery storage systems. MATLAB modeling and simulation across eight cases, accounting for seasonal variations, demonstrate the technical and economic feasibility of case 7. This configuration, integrating modular reactors, photovoltaics, wind turbines, and battery storage, satisfactorily meets load demands. Notably, it boasts a high internal rate of return up to ~31% and a shorter payback period of around 4 years compared to alternative scenarios. [ABSTRACT FROM AUTHOR]

This study presents an advanced Model Predictive Control (MPC) technique designed to mitigate Circulating Current (CC) in HVDC systems equipped with Modular Multilevel Converters (MMC). This MPC strategy eliminates the need for traditional PI regulators and pulse width modulation, improving system dynamics and control accuracy. It excels in managing output currents and mitigating voltage fluctuations in sub-module capacitors. Moreover, the paper introduces a novel communication-free Fault Ride-Through (FRT) method that makes a DC chopper redundant, enabling rapid recovery from disturbances. To reduce the computational burden of standard MPC algorithms, an aggregated MMC model is proposed, significantly decreasing the computational complexity. Simulation studies validate the new MPC algorithm's capability in regulating AC side current, reducing CC, and ensuring capacitor voltage stability under varying conditions. The findings indicate that the proposed MPC controller outperforms traditional PI and PR-based methods, offering enhanced dynamic response, decreased steady-state error, and lowered converter losses, which contribute to smoother DC link voltages. Future research will focus on system scalability, renewable energy integration, and empirical validation through hardware-in-the-loop testing. [ABSTRACT FROM AUTHOR]

A reduced power system's inertia represents a big issue for high penetration levels of renewable generation sources. Recently, load frequency controllers (LFCs) and their design have become crucial factors for stability and supply reliability. Thence, a new optimized multiloop fractional LFC scheme is provided in this paper. The proposed multiloop LFC scheme presents a two-degree-of-freedom (2DOF) structure using the tilt–integral–derivatives with filter (TIDN) in the first stage and the tilt–derivative with filter (TDN) in the second stage. The employment of two different loops achieves better disturbance rejection capability using the proposed 2DOF TIDN-TDN controller. The proposed 2DOF TIDN-TDN method is optimally designed using the recent powerful marine predator optimizer algorithm (MPA). The proposed design method eliminates the need for precise modeling of power systems, complex control design theories, and complex disturbance observers and filter circuits. A multisourced two-area interlinked power grid is employed as a case study in this paper by incorporating renewable generation with multifunctionality electric vehicle (EV) control and contribution within the vehicle-to-grid (V2G) concept. The proposed 2DOF TIDN-TDN LFC is compared with feature-related LFCs from the literature, such as TID, FOTID, and TID-FOPIDN controllers. Better mitigated frequency and tie-line power fluctuations, faster response, lower overshot/undershot values, and shorter settling time are the proven features of the proposed 2DOF TIDN-TDN LFC method. [ABSTRACT FROM AUTHOR]

Active islanding detection techniques majorly affect power quality due to injected harmonic signals, whereas passive methods have a large non-detection zone (NDZ). This article presents a new method based on the resultant sequential impedance component (RSIC) as a new approach to island detection with zero NDZs. The abrupt variable in the conventional impedance approach was replaced by the RSIC of the inverter in this method. When the measured value exceeds the threshold range, islanding is detected by monitoring the variations in the RSIC at the point of common coupling (PCC). For proper power utilization in the identified islands, a priority-based load-shedding strategy is also recommended and implemented in this article. Its efficacy was verified in a wide range of real-world settings. It offers superior stability in various non-islanding (NIS) scenarios to prevent accidental tripping. The proposed method advantages include a cheap cost, the simplicity of implementation, independence from the number and type of distributed generation (DG) units connected, and no power quality effects. Compared to other methods reported in the literature, the obtained detection times illustrate that the proposed method is superior. [ABSTRACT FROM AUTHOR]

This article explores a single-source UXE-type asymmetrical multilevel inverter for its operation on 13-, 11-, and 9-levels with boosted output voltage. The 13- and 11-level operations require a single voltage sensor to maintain the auxiliary dc link at $V_{\text{dc}}/2$ and produce 1.5 and 1.25 boosting using redundant states and exhibit lower capacitor inrush currents. For the 9-level operation, the topology exhibits self-balancing of the dc-link at $V_{\text{dc}}$ and a boosting of 2. With a conventional pulsewidth modulation (PWM) for the 13-level operation, the inverter can drive a predominantly inductive load of 0.35 lagging at unity modulation index. For the 11-level operation, the levels cease to maintain beyond the modulation of 0.95. Thus, a hybrid PWM is proposed to fully utilize the redundant states for the charging of the switched-capacitors and enhance the inverter active load capability in the 13-level mode by 21% at unity modulation index ($m_a$) and address a 0.8 power factor load at the $m_a$ of 0.9. For the 11-level operation, the levels are maintained at any $m_a$ with the hybrid PWM. The operation of the inverter is also verified for nonlinear load. Further, the 9-level operation with twice boosting and self-balancing is presented. The results are verified on MATLAB/Simulink and validated experimentally. [ABSTRACT FROM AUTHOR]

The most recent developments in Thermal Barrier Coating (TBC) relate to engine performance, manufacturing and other related challenges. TBC on the piston crown and valves to enhance engine characteristics while using diesel and Mahua Methyl Ester (MME) as a petroleum fuel has a great sustainable development. For this utility, a Direct Injection (DI) conventional diesel engine was renewed to an LHR engine by applying 0.5 mm thickness of 3Al2O3-2SiO2 (as TBC) onto the piston crown and valves. The MME is used in the LHR (Low Heat Rejection) engine. For examination, the fuel injector pressure is set at 200 bar. Compared to a standard DI diesel engine, the results demonstrate that the application of TBC boosts brake thermal efficiency to 13.65% at 25% load. The LHR engine's SFC and BTE significantly improved at full load while using MME fuel. The lower temperature of exhaust gases is achieved by combining MME and diesel fuels with TBC. It was observed that both MME with and without TBC significantly reduced the smoke density. In addition, it was exposed that using MME fuel with TBC very slightly reduced carbon monoxide emissions under all loads. It was also shown that MME with TBC significantly reduced environmental hydrocarbon emissions at all loads. [ABSTRACT FROM AUTHOR]

Electric vehicles (EV) are promising alternate fuel technologies to curtail vehicular emissions. A modeling framework in a hybrid electric vehicle system with a joint analysis of EV in powering and regenerative braking mode is introduced. Bidirectional DC–DC converters (BDC) are important for widespread voltage matching and effective for recovery of feedback energy. BDC connects the first voltage source (FVS) and second voltage source (SVS), and a DC-bus voltage at various levels is implemented. The main objectives of this work are coordinated control of the DC energy sources of various voltage levels, independent power flow between both the energy sources, and regulation of current flow from the DC-bus to the voltage sources. Optimization of the feedback control in the converter circuit of HEV is designed using an artificial neural network (ANN). Applicability of the EV in bidirectional power flow management is demonstrated. Furthermore, the dual-source low-voltage buck/boost mode enables independent power flow management between the two sources—FVS and SVS. In both modes of operation of the converter, drive performance with an ANN is compared with a conventional proportional–integral control. Simulations executed in MATLAB/Simulink demonstrate low steady-state error, peak overshoot, and settling time with the ANN controller. [ABSTRACT FROM AUTHOR]

The gradual shift towards cleaner and green energy sources requires the application of electric vehicles (EVs) as the mainstream transportation platform. The application of vehicle-to-grid (V2G) shows promise in optimizing the power demand, shaping the load variation, and increasing the sustainability of smart grids. However, no comprehensive paper has been compiled regarding the of operation of V2G and types, current ratings and types of EV in sells market, policies relevant to V2G and business model, and the implementation difficulties and current procedures used to cope with problems. This work better represents the current challenges and prospects in V2G implementation worldwide and highlights the research gap across the V2G domain. The research starts with the opportunities of V2G and required policies and business models adopted in recent years, followed by an overview of the V2G technology; then, the challenges associated with V2G on the power grid and vehicle batteries; and finally, their possible solutions. This investigation highlighted a few significant challenges, which involve a lack of a concrete V2G business model, lack of stakeholders and government incentives, the excessive burden on EV batteries during V2G, the deficiency of proper bidirectional battery charger units and standards and test beds, the injection of harmonics voltage and current to the power grid, and the possibility of uneconomical and unscheduled V2G practices. Recent research and international agency reports are revised to provide possible solutions to these bottlenecks and, in places, the requirements for additional research. The promise of V2G could be colossal, but the scheme first requires tremendous collaboration, funding, and technology maturation. [ABSTRACT FROM AUTHOR]

It is becoming increasingly difficult to properly control the power consumption of widely dispersed data centers. Energy consumption is high because of the need to run these data centers (DCs) that handle incoming user requests. The rising cost of electricity at the data center is a contemporary problem for cloud service providers (CSPs). Recent studies show that geo-distributed data centers may share the load and save money using variable power prices and pricing derivatives in the wholesale electricity market. In this study, we evaluate the problem of reducing energy expenditures in geographically dispersed data centers while accounting for variable system dynamics, power price fluctuations, and renewable energy sources. We present a renewable energy-based load balancing employing an option pricing (RLB-Option) online algorithm based on a greedy approach for interactive task allocation to reduce energy costs. The basic idea of RLB-Option is to process incoming user requests using available renewable energy sources. In contrast, in the case of unprocessed user requests, the workload will be processed using brown energy or call option contract at each timeslot. We formulate the energy cost minimization in geo-distributed DCs as an optimization problem considering geographical load balancing, renewable energy, and an option pricing contract from the derivative market while satisfying the set of constraints. We prove that the RLB-Option can reduce the energy cost of the DCs close to that of the optimal offline algorithm with future information. Compared to standard workload allocation methods, RLB-Option shows considerable cost savings in experimental evaluations based on real-world data. [ABSTRACT FROM AUTHOR]

Distributed energy resources (DERs) and demand side management (DSM) strategy implementation in smart grids (SGs) lead to environmental and economic benefits. In this paper, a new DSM strategy is proposed for the day-ahead scheduling problem in SGs with a high penetration of wind energy to optimize the tri-objective problem in SGs: operating cost and pollution emission minimization, the minimization of the cost associated with load curtailment, and the minimization of the deviation between wind turbine (WT) output power and demand. Due to climatic conditions, the nature of the wind energy source is uncertain, and its prediction for day-ahead scheduling is challenging. Monte Carlo simulation (MCS) was used to predict wind energy before integrating with the SG. The DSM strategy used in this study consists of real-time pricing and incentives, which is a hybrid demand response program (H-DRP). To solve the proposed tri-objective SG scheduling problem, an optimization technique, the multi-objective genetic algorithm (MOGA), is proposed, which results in non-dominated solutions in the feasible search area. Besides, the decision-making mechanism (DMM) was applied to find the optimal solution amongst the non-dominated solutions in the feasible search area. The proposed scheduling model successfully optimizes the objective functions. For the simulation, MATLAB 2021a was used. For the validation of this model, it was tested on the SG using multiple balancing constraints for power balance at the consumer end. [ABSTRACT FROM AUTHOR]

This article presents a study on the performance characteristics of a Francis turbine operating with various guide vane openings to determine the best operating point based on unit quantities. The guide vane openings were specified based on the width between the vanes at their exit, i.e., 10 mm, 13 mm, 16 mm, and 19 mm. The performance characteristic curves of the Francis turbine—head versus speed, torque versus speed, discharge versus speed, and efficiency versus speed—were obtained at various input power and guide vane openings. From these data, unit curves were plotted and the corresponding best efficiency points were obtained. The highest efficiency of 50.25% was obtained at a guide vane opening of 19 mm. The values of head, discharge, speed, and output power at BEP were 7.84 m, 13.55 lps, 1250 rpm, and 524 W, respectively. [ABSTRACT FROM AUTHOR]

In this paper, a combined approach of Principal Component Analysis (PCA)-based Extreme Learning Machine (ELM) for boiler output forecasting in a thermal power plant is presented. The input used for this prediction model is taken from the boiler unit of the Yermarus Thermal Power Station (YTPS), India. Calculation of the accurate electrical output of a boiler in an operating system requires the knowledge of hundreds of operating parameters. The dimensionality of the input dataset is reduced by applying principal component analysis using IBM@SPSS Software. In the process of principal component analysis, a dataset of 232 parameters is standardized into 16 principal components. The total dataset collected is divided into training and testing datasets. The extreme learning machine is designed for various activation functions and the number of neurons. Sigmoid and hyperbolic tangent activation functions are studied here. Its generalization performance is examined in terms of the Mean Square Error (MSE), Mean Absolute Error (MAE), Root Mean Square (RMSE), and Mean Absolute Percentage Error (MAPE). ELM and PCA–ELM are compared. In both the ELM and PCA–ELM models, when the extreme learning machine was designed with a sigmoid activation function with 100 nodes in the hidden layer, RMSE was 5.026 and 4.730, respectively. Therefore, the developed combined approach of PCA–ELM proved as a promising technique in forecasting with reduced errors and reduced time. [ABSTRACT FROM AUTHOR]

Dc–dc converters with a high gain, continuous input current, and common ground are usually employed in renewable energy applications to boost the generated output voltage of renewable energy sources. In this paper, a high-gain dc–dc converter comprising a voltage multiplier cell (VMC) and a common ground with continuous input current and low-voltage stress across semiconductor devices is proposed. The converter produces a voltage gain of about ten times compared to the conventional boost converter at a duty ratio of 50% by utilizing switched capacitors and switched inductors. The simultaneous operation of both the switches with the same gate pulse offers easy and simple control of the proposed converter with a wide range of operations. The boundary operation of the converter is analyzed and presented in both modes, i.e., continuous conduction mode (CCM) and discontinuous conduction mode (DCM). Ideal and nonideal analysis of the converter is carried out by integrating real models of passive elements and semiconductor devices by using PLECS software. The simulation is also used to calculate the losses and hence the working efficiency of the converter. The performance of the converter analyzed in the steady state is compared with various similar converters based on the voltage boosting capability and switching stresses. A hardware prototype is also developed to confirm and validate the theoretical analysis and simulation of the proposed converter. [ABSTRACT FROM AUTHOR]

A dual‐source switched‐capacitor multilevel inverter (SCMLI) topology utilizing 13 switches, one diode, and two switched capacitors is presented in this paper. The proposed topology is capable of operating in both asymmetrical and symmetrical modes. In asymmetrical mode, it produces 19‐level output voltage, whereas, in symmetrical mode, it produces 13‐level output voltage. The capacitor voltages are self‐balanced in the presented SCMLI topology. A detailed comparative analysis has been carried to show the advantages of the proposed topology in terms of the number of switches, number of capacitors, number of sources, and boosting of the converter with the recently published multilevel inverter topologies. The nearest level control (NLC)‐based algorithm is used for generating switching signals for the IGBTs present in the circuit. The reliability assessment is carried to assess the life span of the proposed SCMLI. Experimental results are obtained for different loading conditions using a laboratory hardware prototype. The fluke‐based power analyzer is used to record the inverter voltage and corresponding THD values at different modulation indexes. The efficiency of the proposed inverter is 97.35% for 200‐W load. [ABSTRACT FROM AUTHOR]

Transformer oil is conventionally used as an insulating liquid for the purpose of insulation and cooling in power transformers. The rise in the power demand has put stress on the existing insulation system used for power transmission. Nanotechnology provides an advanced approach to upgrade the conventional insulation system by producing nano-oil with enhanced dielectric characteristics. The aim of the study is to present the influence of area volume effect on the dielectric performance of mineral oil and its nanofluids. In this paper, nanofluids are prepared by dispersing two different concentrations of SiO2 nanoparticles in base transformer oil using a two-step method. The effect of area and volume is investigated on nanofluids in the laboratory using coaxial electrode configurations under different test conditions. The AC breakdown voltage and maximum electric stress is determined for the pure oil and nanofluids. The results show that the addition of SiO2 nanoparticles significantly improves the dielectric characteristics of transformer oil. Moreover, the breakdown phenomenon is also discussed to analyze the effect of nanoparticle, stressed area, and stressed volume on the dielectric strength of insulating oil. Nanofluids could be an alternative to mineral oil. [ABSTRACT FROM AUTHOR]

Nowadays the utilization of Electric Vehicles (EVs) has greatly increased. They are attaining greater attention due to their impacts on the grid at the distribution level. However, due to the increased need for electricity, EVs are also used to serve the load in the instance of electrical failure in the distribution systems. This paper presents a new approach to a service restoration method for a low-voltage distribution network at the time of a power outage using existing EVs available in a parking place. The objective function formulated here was a constrained linear optimization model. It aimed to develop priority-based scheduling of the residential user appliances while meeting all the operational constraints if the EV's power was in a deficit at the hour of the outage. Weight factors were assigned to various residential appliances to decide their priority while scheduling. To substantiate the proposed methodology, a day load profile of a 20 kVA distribution transformer feeding eight residential users is considered. This was tested during an hour-long power outage scenario in the MATLAB and LINGO platforms, with four EVs available during the outage period. This method restored the maximum power to the residential appliances. [ABSTRACT FROM AUTHOR]

Power electronics is a core subject in electrical and electronics engineering at the undergraduate level. The rapid growth in the field of power electronics requires necessary changes in the curricula and practica for power electronics. The proposed next-generation power electronics teaching laboratory changes the learning paradigm for this subject and is for the first time used for teaching purposes in Pakistan. The proposed controller hardware-in-the-loop (CHIL) laboratory enabled students to design, control, and test power converters without the fear of component failure. CHIL setup allowed students to directly validate the physical controller without the need for any real power converter. This allowed students to obtain more repeatable results and perform extreme digital controller testing of power converters that are otherwise not possible on real hardware. Furthermore, students could start learning power electronics concepts with hardware from the beginning on a safe, versatile, fully interactive, and reconfigurable platform. The proposed laboratory meets the accreditation board for engineering and technology (ABET) student outcome criterion K such that students can continue with the same hardware and software toolset for graduate and research purposes. The knowledge and skills acquired during undergraduate years can help students create new solutions for power electronics systems and develop their expertise in the field of power electronics. The results obtained from the survey indicated that the majority of the students were satisfied with the laboratory setup. They also expressed appreciation over the provision of a high-level graphical language "LabVIEW" for the digital controllers compared to conventional low-level text-based languages such as VHDL, Verilog, C, or C++. [ABSTRACT FROM AUTHOR]

Renewable energy is now the world's most reliable and sustainable solution to environmental pollution, the energy crisis, and social sustainability. In order to regulate renewable energies and ensure the sustainable development of renewable energy in China, a regulatory framework is imperative. Electricity demand and supply forecasting have become one of the most important topics for research on sustainable solutions to energy issues. This article analyzes the Chinese electric power industry structure by looking at historical electricity demand and supply data. This study shows how a divergent set of energy policies can facilitate the application of renewable energy to China's electric power sector. The analysis shows that in 2020, China's electricity generation and consumption increased by 0.9 and 1.3 percent annually, respectively, producing capacity of power plants increased by 5.6 GW, coal consumption of power supply decreased by 4.3 g/kWh, power generation projects investment increased by 51.6 billion RMB, power grid projects investment decreased by 1.8 billion RMB, and newly installed generation capacity increased by 769 GW year on year (YOY). During the first three quarters of 2021, the generation, consumption and coal consumption of electricity increased respectively by 10.7 and 12.9%, the generating capacity of power plants increased by 9.4 GW, the average coal consumption of power supply decreased by 0.9 g/kWh, power generation projects investment increased by 1.8 billion RMB, while power grid projects investment decreased by 0.3 billion RMB and new generation capacity added around 20.16 GW compared to the same period last year. Furthermore, domestic power consumption grew by 6.0% and 7%, respectively, in 2020–2021. The paper examines China's current climate, developments, and renewable energy targets between 2020 and 2021. This research aims to identify the main risks associated with China's renewable energy development and propose a few policy measures for risk management. [ABSTRACT FROM AUTHOR]

Under partial shading conditions (PSC), most traditional maximum power point tracking (MPPT) techniques may not adopt GP (global peak). These strategies also often take a considerable amount of time to reach a full power point (MPP). Such obstacles can be eliminated by the use of metaheuristic strategies. This paper shows, in partial shading conditions, the MPPT technique for the photovoltaic system using the Bat Algorithm (BA). Simulations have been performed in the MATLAB®/Simulink setting to verify the efficacy of the proposed method. In MPPT applications, the results of the simulations emphasize the precision of the proposed technique. The algorithm is also simple and efficient, on a low-cost microcontroller, it could be implemented. Hardware in Loop (HIL) validation is performed, with a Typhoon HIL 402 setup. [ABSTRACT FROM AUTHOR]

This paper discusses and analyses the work done in developing a Nature‐inspired optimization algorithm in a solar PV system for tracking the global maximum power point (GMPP) in partial shaded condition (PSC). Partial shading is a situation where PV panels connected in series do not receive the same solar radiation, thus exhibiting different I‐V characteristics for the individual panel. Under PSC, solar PV system output exhibits multiple local maximum power point (MPP) and a unique global MPP on the Power‐Voltage curve. Conventional maximum power point tracking (MPPT) techniques such as Perturb and Observe (P&O), incremental conductance (IC), and Hill Climbing (HC) method can only track MPP under uniform insolation conditions. It may fail to track GMPP in case of PSC and get trapped in local MPP, thereby causing loss of power that could have been tapped if the operation would have been at GMPP. To overcome the problem posed by PSC, researchers are applying the nature‐inspired optimization algorithm for tracking GMPP under partial shading conditions with fairly good results. Nature‐inspired optimization algorithm offers the advantage of solving multivariable nonlinear objective functions with constraints by exploiting the search space and can converge quickly to GMPP. Some of the most recent and famous nature‐inspired algorithms have been discussed and compared here. Simulation and hardware results of some of them are also taken in order to compare them effectively. Simulation results show that the Most Valuable Player Algorithm (MVPA) takes the least time to track the MPP in all the cases, and the MPP tracked is also comparable to the maximum while PSO with differentially perturbed velocity (PSO_DV) takes the maximum time to track the MPP in all cases. [ABSTRACT FROM AUTHOR]

Microgrids are hybrid power systems that consist of several distributed generation resources and local loads that can supply electrical power to remote or specific areas. The integration of microgrids with the utility network is one of the most recent technologies developed in countries like Egypt. One area of study is how the integration of smart microgrids and utility systems can be used to solve power quality problems such as voltage sags, increased use of distributed generators, deep energy, and power loss. This paper is aimed at investigating a possible solution to some common and dangerous power quality issues associated with the integration of smart microgrids and utility systems such as voltage fluctuation and total harmonic distortion (THD) at different solar irradiance and load conditions. This study used a MATLAB and Simulink code developed to model and analyze smart microgrid and utility system integration and the power quality issue at different loads. This study focuses on five scenarios of voltage analysis and two scenarios of THD at different irradiance and load conditions. The results show that using an integrated smart microgrid and utility system will reduce the voltage drop percentage with high solar irradiance and will increase it with low solar irradiance at both high and low loads. Additionally, THD decreases with increasing solar irradiance and increases with decreasing solar irradiance at both high and low loads. [ABSTRACT FROM AUTHOR]

Inclusion of bypass diodes at the output terminal of the PV array mitigates the effect of partial shading (PS) but causes multiple peaks of power at the output. The conventional hill climbing and perturb and observe algorithms cannot track the optimal point during partial shading phenomena for multiple peaks corresponding to the different shading pattern on the Power-Voltage (P-V) curve. Fuzzy logic controller and artificial neural network-based methods for Maximum Power Point Tracking (MPPT) provide satisfactory results but at the cost of increased memory and computational burden. Recent work to incorporate exploration and exploitation phenomena of nature-inspired algorithms to track optimal power point have shown encouraging results by preventing convergence to local maxima and posing less burden on the processor. However, due to performance variation between different algorithms of this category newer algorithms with improved performances are still a requirement. In this paper, a novel most valuable player algorithm (MVPA) has been used to track the optimal operation point for extracting maximum power from a solar PV system. The algorithm's performance is compared with the commonly employed particle swarm optimization (PSO) and the recently proposed Jaya algorithm's modified form. It is observed that the proposed algorithm outperformed both the algorithms with a considerable improvement in terms of tracking speed, power tracking efficiency, robustness, faster decision for convergence after tracking the maximum power and lesser number of power fluctuations for different shading patterns. [ABSTRACT FROM AUTHOR]

Conventional PI controllers are vulnerable to changes in parameters and are difficult to tune. In this work, an artificial neural network (ANN) based controller is developed for the robust operation of a single-phase modified packed U-cell five-level inverter (MPUC-5) for solar PV application under variable insolation conditions. An MPUC-5 is a converter with a main and an auxiliary dc link of equal magnitude; although five-level operation is also still feasible with different voltages also. The maximum power point (MPP) of a PV array changes with the variation in the solar insolation. This results in a variable voltage at the output of the boost converter while maintaining the load line at the MPP. Consequently, the fundamental value of the output of the MPUC-5 also tends to change. Thus, it is required to produce angles that commit to an ac output voltage with a constant fundamental value and constrained to a minimum total harmonic distortion along with a third-order harmonic mitigation as per the grid codes, irrespective of the change in the dc-link voltages. A genetic algorithm is employed for this purpose. A large dataset is prepared for two-angle and four-angle operation of MPUC-5 under various dc-link voltages and constraints with which an ANN-based controller is trained. A neural network with a hidden layer is trained with the backpropagation technique; and once a correlation is developed, the network can be operated for a wide range of operating conditions. The robustness of the controller is verified through simulation in MATLAB/Simulink environment and validated by experimental emulation in an hardware in loop environment. [ABSTRACT FROM AUTHOR]

The acceleration time of induction machines (IMs) is essential for proper protection-relay settings of the machine to prevent voltage sags in local power areas. In this paper, mathematical modeling of IMs' speed-time characteristics during no-load direct startup has been presented. Unlike the approaches presented in the literature, the proposed approach includes the bearing losses, in which two expressions of the speed-time characteristics of IMs during no-load direct startup are derived. The first expression was derived based on the Kloss equation used for representing the torque, and the second expression was derived based on the torque expression determined from the Thevenin equivalent circuit of the machine. The derived expressions' accuracy was validated using laboratory measurement and computer simulation approaches. The results obtained show a good agreement between the measured and simulated speed-time characteristics of two IMs. Finally, the proposed formulations can provide a simple analytical base to enable accurate IM modeling. [ABSTRACT FROM AUTHOR]

The flashover performance of contaminated insulators highly depends on the type of pollutant and its present concentration. In this paper, important agricultural salts (NaCl, K2SO4, NaHCO3, CaSO4, KHCO3, MgSO4, NH4), 2Fe(SO4)2, and 6H2O (ferrous ammonium sulphate, dust, and urea) at different concentrations, and biological contaminants, such as algae and fungi, were taken as pollutants, and the AC flashover behavior of a porcelain-cap-and-pin-type insulator polluted with these two different pollutants was investigated. The experiment was carried out by a semi-natural method, wherein the insulator was first polluted artificially; thereafter, natural fog was applied to measure the wet flashover voltage. Test results indicated that the flashover voltages were affected by both soluble salts and non-soluble components deposited on the insulator surface. In the case of the thickly contaminated layers, non-soluble deposits greatly reduced the flashover voltage. Moreover, by using regression analysis, four empirical models based on different variables were developed. The empirical models developed in the present work represented a good degree of relation in predicting the flashover voltage of naturally contaminated insulators. [ABSTRACT FROM AUTHOR]

Nanotechnology provides an effective way to upgrade the thermophysical characteristics of dielectric oils and creates optimal transformer design. The properties of insulation materials have a significant effect on the optimal transformer design. Ester-based nanofluids (NF) are introduced as an energy-efficient alternative to conventional mineral oils, prepared by dispersing nanoparticles in the base oil. This study presents the effect of nanoparticles on the thermophysical properties of pure natural ester (NE) and synthetic ester (SE) oils with temperature varied from ambient temperature up to 80 °C. A range of concentrations of graphene oxide (GO) and TiO2 nanoparticles were used in the study to upgrade the thermophysical properties of ester-based oils. The experiments for thermal conductivity and viscosity were performed using a TC-4 apparatus that follows Debby's concept and a redwood viscometer apparatus that follows the ASTM-D445 experimental standard, respectively. The experimental results show that nanoparticles have a positive effect on the thermal conductivity and viscosity of oils which reduces with an increase in temperature. [ABSTRACT FROM AUTHOR]

The feasibility of power transfer enhancement, through simultaneous AC–DC power transmission in a two-terminal transmission network, has been proposed earlier by the authors, and the concept is well established. To meet the increase in demand for electricity, a new technique is proposed in this article to increase the use of existing transmission lines in addition to independent control of AC and DC power flow. This paper extends the concept to a three-terminal transmission network by considering a power tapping from the middle of the line. DC is also superimposed in the already existing three-terminal AC transmission system. In the proposed topology, a multi-terminal simultaneous AC–DC system is used, which is integrated with a zig-zag transformer and more than two voltage source converter (VSC) stations. Each terminal may represent an area of the power system. Anyone/two-terminal(s) may act as sending end, whereas the remaining two/one terminal(s) may act as receiving end. Power can flow in either direction through each segment of the transmission system. At sending end, VSC converts a part of AC to DC and injects it into the neutral of the zig-zag transformer. On receiving terminal, DC power is tapped from neutral of zig-zag transformer and fed to VSC for conversion back to AC. The concept is verified in the digital simulation software PSCAD/EMTDC. [ABSTRACT FROM AUTHOR]

Solid-state transformers (SSTs) have emerged as a superior alternative to conventional transformers and are regarded as the building block of the future smart grid. They incorporate power electronics circuitry and high-frequency operation, which allows high controllability and enables bi-directional power flow, overcoming the limitations of conventional transformers. This paper presents a detailed analysis of the solid-state transformer, expounding the fundamentals, converter topologies, applications, and future challenges of the SST in a systematic manner. The paper discusses the necessity of improved replacement of the low-frequency transformers (LFTs) and presents the configuration of SST. It presents SST fundamentals in individual stages and explores its origin and evolution. The basic topologies, their specifications, and control strategies are also described. The applications of SST as a replacement of LFTs are discussed along with recent applications. The future challenges for real-time implementation of SSTs are explored, and research directions are proposed. [ABSTRACT FROM AUTHOR]

Because of the rapid increase in the depletion rate of conventional energy sources, the energy crisis has become a central problem in the contemporary world. This issue opens the gateway for exploring and developing renewable energy sources to fulfill the exigent energy demand. Solar energy is an abundant source of sustainable energy and hence, nowadays, solar photovoltaic (PV) systems are employed to extract energy from solar irradiation. However, the PV systems need to work at the maximum power point (MPP) to exploit the highest accessible power during varying operating conditions. For this reason, maximum power point tracking (MPPT) algorithms are used to track the optimum power point. Furthermore, the efficient utilization of PV systems is hindered by renowned partial shading conditions (PSC), which generate multiple peaks in the power-voltage characteristic of the PV array. Thus, this article addresses the performance of the newly developed jellyfish search optimization (JSO) strategy in the PV frameworks to follow the global maximum power point (GMPP) under PSC. [ABSTRACT FROM AUTHOR]

In this paper, a twelve-band hysteresis control is applied to a recent thirteen-level asymmetrical inverter topology by employing a robust proportional-integral (PI) controller whose parameters are decided online by genetic algorithm (GA). The asymmetrical inverter topology can generate thirteen levels of output voltage incorporating only ten switches and exhibits boosting capability. A 12-band hysteresis current control strategy is applied to ensure the satisfactory operation of the inverter. It is designed to provide a sinusoidal line current at the unity power factor. The tuning of the PI controller is achieved by a nature inspired GA. Comparative analysis of the results obtained after application of the GA and the conventional Ziegler–Nichols method is also performed. The efficacy of the proposed control on WE topology is substantiated in the MATLAB Simulink environment and was further validated through experimental/real-time implementation using DSC TMS320F28379D and Typhoon HIL real-time emulator (Typhoon-HIL-402). [ABSTRACT FROM AUTHOR]

High gain DC-DC converters are getting popular due to the increased use of renewable energy sources (RESs). Common ground between the input and output, low voltage stress across power switches and high voltage gain at lower duty ratios are desirable features required in any high gain DC-DC converter. DC-DC converters are widely used in DC microgrids to supply power to meet local demands. In this work, a high step-up DC-DC converter is proposed based on the voltage lift (VL) technique using a single power switch. The proposed converter has a voltage gain greater than a traditional boost converter (TBC) and Traditional quadratic boost converter (TQBC). The effect of inductor parasitic resistances on the voltage gain of the converter is discussed. The losses occurring in various components are calculated using PLECS software. To confirm the performance of the converter, a hardware prototype of 200 W is developed in the laboratory. The simulation and hardware results are presented to determine the performance of the converter in both open-loop and closed-loop conditions. In closed-loop operation, a PI controller is used to maintain a constant output voltage when the load or input voltage is changed. [ABSTRACT FROM AUTHOR]

An interconnection of electric power networks enables decarbonization of the electricity system by harnessing and sharing large amounts of renewable energy. The highest potential renewable energy areas are often far from load centers, integrated through long-distance transmission interconnections. The transmission interconnection mitigates the variability of renewable energy sources by importing and exporting electricity between neighbouring regions. This paper presents an overview of regional and global energy consumption trends by use of fuel. A large power grid interconnection, including renewable energy and its integration into the utility grid, and globally existing large power grid interconnections are also presented. The technologies used for power grid interconnections include HVAC, HVDC (including LCC, VSC comprising of MMC-VSC, HVDC light), VFT, and newly proposed FASAL are discussed with their potential projects. Future trends of grid interconnection, including clean energy initiatives and developments, UHV AC and DC transmission systems, and smart grid developments, are presented in detail. A review of regional and global initiatives in the context of a sustainable future by implementing electric energy interconnections is presented. It presents the associated challenges and benefits of globally interconnected power grids and intercontinental interconnectors. Finally, in this paper, research directions in clean and sustainable energy, smart grid, UHV transmission systems that facilitate the global future grid interconnection goal are addressed. [ABSTRACT FROM AUTHOR]

In this paper, the analysis and discussion are presented for two prominent types of wireless charging, namely inductive power transfer (IPT) and capacitive power transfer (CPT). The paper presents a comparative analysis between CPT and IPT. A comparison between different geometries of plates used in CPT is performed and it is shown that rectangular-shaped plates have the best power transfer efficiency. The effect of foreign particles between the capacitor plates of a single-module as well as a double-module CPT system on capacitance and energy transfer density are also investigated in the paper. The case of ceramic coating on the capacitor plates and its effects on the capacitance and energy transfer density with varying thickness are analyzed and presented. Different naturally occurring particles such as water, sand and wet sand have been simulated between the capacitor plates of the CPT system and their respective impact on power transfer is analyzed and the simulation results are demonstrated. [ABSTRACT FROM AUTHOR]

Various power generation technologies, such as wind turbines and solar power plants, have been increasingly installed in renewable energy projects as a result of rising demand and ongoing efforts by global researchers to mitigate environmental effects. The sole source of energy for such generation is nature. The incorporation of the green unit into the power grid also results in volatility. The stabilization of frequencies is critical and depends on the balance of supply and demand. An efficient monitoring scheme called Load Frequency Monitoring (LFM) is introduced to reduce the frequency deviation from its natural state. Specific energy storage systems may be considered to improve the efficiency of the control system. The storage system contributes to the load rate, peak rushing, black start support, etc., in addition to high energy and rapid responsive features. A detailed study of different power storage systems, their current business scenario, and the application of LFM facilities, as well as their analysis and disturbance, is presented in this paper. According to the literature analysis, the current approaches can be divided into two categories: grid and load scale structures. This article also distinguishes between the organized aggregate system and the uncoordinated system control scheme, both of which have advantages and disadvantages in terms of technology. [ABSTRACT FROM AUTHOR]

In this article, the UXE-Type inverter is considered for eleven-level operation. This topology exhibits a boosting capability along with reduced switches and one source. An algorithm that utilizes the redundant states to control the voltage-balance of the auxiliary direct current (DC)-link is presented. The proposed control algorithm is capable of maintaining the voltages of each capacitor at V d c / 4 resulting in a successful multilevel operation for all values of load. The inverter is also compared with 11-level inverters. The modulation of the inverter is performed by employing nearest level control and ant colony optimization based selective harmonic elimination. The maximum inverter efficiency is 98.1% and its performance is validated on an hardware-in-the-loop platform. [ABSTRACT FROM AUTHOR]

This paper presents three new and improved non-isolated topologies of quadratic boost converters (QBC). Reduced voltage stress across switching devices and high voltage gain with single switch operation are the main advantages of the proposed topologies. These topologies utilize voltage multiplier cells (VMC) made of switched capacitors and switched inductors to increase the converter's voltage gain. The analysis in continuous conduction mode is discussed in detail. The proposed converter's voltage gain is higher than the conventional quadratic boost converter, and other recently introduced boost converters. The proposed topologies utilize only a single switch and have continuous input current and low voltage stress across switch, capacitors, and diodes, which leads to the selection of low voltage rating components. The converter's non-ideal voltage gain is also determined by considering the parasitic capacitance and ON state resistances of switch and diodes. The efficiency analysis incorporating switching and conduction losses of the switching and passive elements is done using PLECS software (Plexim, Zurich, Switzerland). The hardware prototype of the proposed converters is developed and tested for verification. [ABSTRACT FROM AUTHOR]

As the applications of power electronic converters increase across multiple domains, so do the associated challenges. With multilevel inverters (MLIs) being one of the key technologies used in renewable systems and electrification, their reliability and fault ride-through capabilities are highly desirable. While using a large number of semiconductor components that are the leading cause of failures in power electronics systems, fault tolerance against switch open-circuit faults is necessary, especially in remote applications with substantial maintenance penalties or safety-critical operation. In this paper, a fault-tolerant asymmetric reduced device count multilevel inverter topology producing an 11-level output under healthy conditions and capable of operating after open-circuit fault in any switch is presented. Nearest-level control (NLC) based Pulse width modulation is implemented and is updated post-fault to continue operation at an acceptable power quality. Reliability analysis of the structure is carried out to assess the benefits of fault tolerance. The topology is compared with various fault-tolerant topologies discussed in the recent literature. Moreover, an artificial intelligence (AI)-based fault detection method is proposed as a machine learning classification problem using decision trees. The fault detection method is successful in detecting fault location with low computational requirements and desirable accuracy. [ABSTRACT FROM AUTHOR]