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In this paper, simulation, and performance comparison of a switched reluctance motor (SRM) for a classical asymmetric bridge converter and an improved reduced switch converter topology are provided. In an improved converter, switches are shared by adjacent phases, and one diode is connected in series with each phase winding to avoid reversal of current. Double-phase magnetization is adopted for the analysis of both converter topologies. The performance of both converter topologies are demonstrated using a proportional plus integral (PI) based hysteresis current controller and dynamic load on a 7.5 kW, 8/6 pole, 4- phase SRM. Improved reduced switch converter topology uses half the number of switches as compared to the classical asymmetric bridge converter. According to the simulation results, an improved converter offers superior starting and average torque with less speed settling time. An improved converter reduces the most serious problem of torque ripple in SRM by 50% compared to the classical topology. The reduced switches lower the winding losses and improve the efficiency of the drive system.

The whole world is now widely using green energy compared to fossil because of the depletion of fossil fuels, the rising temperature of the earth, and changing weather conditions, all these things are becoming a big threat to the life of the earth. This study proposed a stand-alone hybrid renewable energy system using different types of batteries. This model includes photovoltaic arrays, wind turbines, diesel generators, converters, and batteries. Lead-acid and lithium-ion batteries have been compared for the selection of optimal battery based on hybrid renewable energy system and sustainable development requirements. The purpose of this study is to find the optimal configuration, and techno-economic characteristics, using the hybrid optimization of multiple energy resources technique. The results of Lithium-ion and Lead Acid have been compared and it is found that the best configuration is photovoltaic arrays/wind turbines/ diesel generators /Battery/converter with lithium-ion Batteries. The net present cost and cost of energy are found to be 1.64M and 0.144$ respectively, for the selected study location. The carbon dioxide emission for configuration with LI batteries is 107314 kg/year as against the LA batteries which have 351288 kg/year. The results show LI batteries are technically as well as economically better than the LA batteries.

The multilevel inverter (MLI) is a power electronic circuit applied for power and voltage applications. It has the advantages of minimum total harmonic distortion (THD), less voltage stress on the switching devices, fewer switching losses, and a smaller size of passive filters. These high-level inverters are applied in applications like AC drives, FACTS, and in the field of renewable energy. There are various inverters for such applications. A PUC multilevel inverter contains fewer components, fewer switching losses, and easy-to-balance voltage on capacitor sides. In this paper, the performance of the transformer less seven-level inverter and the five-level inverter is analyzed. The sinusoidal pulse width modulation (PWM) has been used with this PUC-based structure. The new control strategy is designed to reduce harmonic contents and low filter ratings. A relative analysis is carried out to focus on the superiority of the recently developed packed U-cell topology. The performance of packed U-cell, seven-level, and five-level inverters has been compared. These types of inverters consist of two power switches and a single capacitor to make this inverter effective. The PUC 7 inverter contains a lot of sensors and other control components to balance the voltage on the capacitor side by one-third of the input voltage, so the structure becomes complex. While PUC 5 is sensor less technology, there is no voltage balancing issue and no complexity found in this inverter. In PUC5, the only level is low, but it is very easy to interface with grids and other devices. So it is the most preferable as compared to PUC7. The performance analyses of these inverters have been verified through simulation.

With an increasing prominence of electric vehicles (EVs) in sustainable transportation, it demands a robust and steady charging infrastructure. The widespread adoption of EVs into the transportation ecosystem hinges on continuous and reliable operation of charging stations (CSs). This paper delves its focus on reliability and resilience of EV chargers using various fault-tolerant (FT) techniques. It also analyzes the EV chargers’ failure scenarios such as hardware malfunctions and power outages which can disrupt the charging process. To evaluate these strategies, advanced FT algorithms and system reconfiguration protocols has been analyzed. Also, Total Harmonic Distortion (THD) act as key performance indicator which indicates the reduction from 60% to -40% with diminished current and voltage variations and enhanced grid stability. The extensive real-world research and simulations on charging scenarios reflects the key performance metrics with 95% charging efficiency and fault recovery times of 0.8 seconds at 180 Volts. This research evaluates the compatibility and cost-effectiveness of these strategies with diverse EV models. Ultimately, the findings highlight the effectiveness and practicality of FT techniques for EV chargers providing valuable insights for infrastructure developers and ensures a reliable EV charging fostering consumer trust and accelerating the global transition to electric mobility.

Effective microgrid control for system recovery and restoring normal operation necessitates fast event detection and implementation of remedial action (if need arises). However, fast and reliable event detection in microgrids is challenging because of low observability and inconsistencies in measurements. A novel technique is proposed in the present work for the real-time event detection and to identify the various emerging abnormalities in the microgrid. The continuous energy signature using TKEO (Teager-Kaiser Energy Operator) of the continuous varying voltage and frequency signal are extracted through μPMU. REII (Robust Event Identification Index) is constructed from these energy signatures and based on its abrupt post-event deviation from the nominal values an event is flagged in the proposed method. The proposed method is data–driven and only depends on the real-time inputs through μPMUs thus it automatically adapts the uncertainties associated with the intermittent sources of energy in the microgrid under different operating conditions. The traditional event detection techniques fail in identification of abnormalities for a microgrid connected to the transmission systems and equipped with multiple DERs such as PVDG, WG etc. To address this challenge, an integrated microgrid with multiple DERs viz. PVDG, WG and a SG (Synchronous Generator) is first developed in this work. The complexity of simultaneous operation of a static generator i.e. PVDG along with a rotor-based generator such as WG and SG is handled by the modeling the dynamic controllers of PVDG and WG for their frequency and voltage control. The simulation results depict the efficiency, accuracy and robustness of the proposed technique in terms of estimation time, event accuracy and applicability in all types of events. Moreover, the presented methodology is also compared with the four AI/ML based methods to highlight the superiority of the method.

This article introduces the design procedure of a 1-phase full-bridge inverter for grid-connected applications. The main idea of modelling a full-bridge inverter is that it plays a vital role in solar energy systems. The dynamic equations are formed with the state-space averaging (SSA) technique. A Small-signal averaged model of a full-bridge inverter is constructed by assuming grid current and capacitor voltage as the two states. The voltage and current controller scheme are based on the frequency domain approach. The voltage controller is designed to maintain fixed voltage, and the current controller is used for controlling grid current. The LCL filter is connected to the inverter to provide better harmonic elimination and filter size minimization. This filter is formed by parallel-series combination of inductance and capacitance to improve the overall system performance. Here, a passive damping method is incorporated to make a balanced steady system. Finally, a proportional integral and proportional resonant (PI-PR) controller is introduced to provide better stability and tracking. The detailed analysis of PR controller is explored and demonstrated by MATLAB simulation.

The performance of power networks is enhanced by the penetration of solar energy, which helps to equate continuously the generation and demand power imbalance. However, the time margin that grids must adapt to unforeseen frequency fluctuations and restore generation-demand equivalency is reduced by these linkages. Consequently, it exerts the stability and performance of the power grid at risk. Thus, it becomes vital to assess real-time system data and to recognize and implement suitable remedies to maintain a healthy system performance. In order to improve grid stability in power networks that have solar energy penetration, this manuscript suggests a data driven integrated framework. The proposed approach is a two-step framework wherein the first stage assesses impending transient instability in the system through novel Instability Evaluation (IE). Step two involves creating and deploying a Decision Boundary based Control (DBC) to stabilize an unstable system following an emergency control strategy. An IE module employing short-synchronized movement data is presented for evaluating post-disturbance transient stability (TS). In the initial cycles following the fault initiation, the IE projects the impending transient instability. Next, an innovative DBC creates an emergency remedial system for unstable processes that determines the nature, magnitude and location of the remedial action. The DBC assesses pertinent action sets that it implements to sustain system stability using a proposed Decision Assisted Inference (DAI) technique. The simulation investigations validate the aptness of suggested analysis on the performance of power system with and without PV and topological variations.

Electric vehicle charging stations (EVCS) that are based on DC microgrids are presented in this research. The system comprises a solar photovoltaic system (SPVS), storage battery (SB), electric vehicle (EV) and grid. The adaptive interaction artificial neural network (AI-ANN)-based vehicle to grid (V2G) and grid to vehicle (G2V) power management controller (PMC) is suggested for DC microgrid based EVCS. This EVCS is suitable for the residential building and offices where EV may be parked. This EVCS provides the facility to manage the power of the building in addition to charge the EVIt has two different modes of operation. The first mode uses the EV as a power source. In the second mode, the EV functions as a load. This controller is developed to acquire electrical power from the solar photovoltaic system (SPVS), storage battery, EV and grid respectively. If the solar photovoltaic system (SPVS) and storage battery power are insufficient to meet the demand, power is extracted from electric vehicle (V2G). If the solar photovoltaic system (SPVS), storage battery and EV are not sufficient to meet up demand, then deficit power is obtained from the grid (G2V). ANN based power management controller (PMC) Also provides a consistent DC bus voltage and reduces overshoot from 9.6 % to 0 %., settling time from 1.18 sec. to 0.52 sec. and rise time from 0.27 sec. to 0.25 sec. of DC bus voltage compared to conventional controller. The suggested power management controller tested for two different modes i.e., V2G and G2V using MATLAB Simulink software.

This article explores the development and applications of a Cu3N/GCE-based sensor using differential pulse voltammetry (DPV) for selective uric acid (UA) detection in clinical analysis. The sensor achieves a limit of detection (LOD) of 2.57 × 10−8 M and a quantification limit (QL) of 8.102 × 10−8 M, demonstrating its capability to precisely quantify minute UA concentrations. With rapid responsiveness and reusability over 25 days, it offers cost-effective monitoring of UA levels, even in complex sample matrices. Cu3N also exhibits high efficiency in degrading methylene blue (MB), achieving 87.7% degradation under optimized conditions, suggesting its potential as a photocatalyst for environmental remediation, particularly in dye degradation processes. Overall, Cu3N-based technologies show promise in sensitive UA detection for clinical diagnostics, environmental remediation, and industrial catalysis, highlighting its versatility and broad applicability across scientific and practical domains.

There are many renewable energy sources available, especially wind energy, but it is not being fully utilized. In the wind energy industry, Wind Power Potential (WPP) is essential since it is critical to the development, operation, and optimization of wind power plants. WPP plays a significant role in the wind energy project life cycle, impacting site selection, project viability, technology choices, and ultimate success. This means that specific WPP for certain places need to be determined for development in wind industry.The goal of this study is to conduct a statistical comparison and analysis of the efficacy of various numerical methods, including the method of moments (MoM), the energy pattern factor method (EPFM), the maximum likelihood method (MLM), the energy density method (EDM), the energy pattern factor method of Sathyajith (EPFMS), Rayleigh's distribution (Rayl), and the novel energy pattern factor method (NEPFM). These methods are compared for different sites of Andhra Pradesh India. The NEPFM is considered the most effective approach for assessing the wind energy density in the regions of Visakhapatnam, Amaravati, and Tirupati. Conversely, the MLM (Modified Logarithmic Model) technique has demonstrated superior performance in evaluating the wind energy potential specifically for the Rajamahendravaram site. The Rayleigh distribution, also known as Rayl., was utilized as the primary approach for calculating the probability density of the geographical sites of Visakhapatnam, Rajamahendravaram, and Amaravati. Additionally, the energy pattern factor method was employed to analyze the site of Tirupati. The Rayleigh distribution is found to be the most suitable statistical model for estimating the cumulative density of site locations in Visakhapatnam and Amaravati. Similarly, the innovative energy pattern factor technique is recommended for analyzing the cumulative density of site locations in Rajamahendravaram and Tirupati.

Vanadium nitride-poly (0-methoxy aniline)- poly(3,4-ethylene dioxythiophene) (VN-POMA-PEDOT) hybrid was synthesized via ammonolysis and chemical oxidative polymerization technique using VN-POMA-PEDOT/GCE with electrocatalytic activity has two dimensional VN hierarchical porosity with POMA-PEDOT structure created VN-POMA-PEDOT modified GCE working electrode. Donor-acceptor behavior and double-layer growth enable enhanced electrochemical performance and catalytic activity of mebendazole (MBZ). This work investigated the electrochemical sensing conduct of a VN-POMA-PEDOT hybrid composite towards MBZ. The detection limit (DL) and quantification limit (QL) were determined to be 2.192 × 10−9 μM μA−1 and 5.245 × 10−9 M μA−1. Estimation of anti-interference ability, long-term stability, and reproducibility revealed that the prepared VN-POMA-PEDOT electrode is appropriate for the electrochemical sensing finding of MBZ in real analysis, such by way of anti-helminthic drug milk. The VN-POMA-PEDOT achieved 98.9% efficiency in the photocatalytic degradation of methylene blue (MB) within 50 min with degradation rate 8.3 × 10−3 min−1. The suppleness of this method was confirmed by the hybrid morphology VN-POMA-PEDOT, which shows an enormously superior and enhanced photocatalytic presentation of MB.

Abstract Avoiding financial losses requires preventing catastrophic oil‐filled power transformer breakdowns. Continuous online transformer monitoring is needed. The authors use paper insulation to evaluate transformer health for continuous online transformer monitoring. The study suggests a new artificial intelligence method for estimating paper insulation residual life in oil‐immersed power transformers. The four artificial intelligence models use backpropagation‐based neural networks to predict paper insulation lifespan. Four primary transformer insulating paper failure indices—degree of polymerisation, 2‐furfuraldehyde, carbon monoxide, and carbon dioxide—form the basis of these models. Each model, including the backpropagation‐based neural networks, estimates paper insulation life using one failure index, along with moisture and temperature data. Optimisation techniques enhance hidden layer neurons and epoch count for improved performance. Results are validated against literature‐based life models, establishing a precise input–output correlation. This method accurately predicts the remaining useable life of power transformer paper insulation, enabling utilities to take proactive measures for safe and efficient transformer operation.

This paper introduces a novel approach to enhance the control algorithm for a single-phase shunt active power filter(SAPF) by integrating a new technique into a 5-level cascaded multilevel inverter (MLI) with Photo Voltaic (PV) array integration. Due to the integration of non-linear loads in the grid, such as computers, variable speed drives, and other solid-state equipment, non-sinusoidal currents are drawn, introducing harmonics that distort the voltage and current waveforms. It is essential to mitigate these harmonics, and integrating Shunt Active Power Filters (SAPFs) gives the grid the ability to inject active power. In order to improve power quality and achieve active and reactive power balance, this integration requires an imperative control method. This work extracts the fundamental component of the load current and efficiently handles grid distortions such as DC offset, phase shift, and harmonics using a phase lock loop (PLL) based on a Modified Notch Filter Second Order Generalised Integrator (MNFSOGI). Using MATLAB/Simulink software modelling, experimental validation utilising the OPAL-RT real-time data simulator verifies the proposed system's operation. The control algorithm, which has been painstakingly designed and verified, allows feed-forward current estimate from the PV array, synchronization template obtained from the grid voltage signal, and estimation of both fundamental and non-linear load currents. The performance of the MNFSOGI-PLL is additionally compared with that of the conventional E-PLL, MAF-PLL and DSOGI-PLL techniques. By attaining power balance in multilevel inverters under distorted grid conditions, this research substantially contributes to improving power quality. According to simulation and experimental results, the Total Harmonic Distortion (THD) of the grid current is found to be 1.50% in normal grid and 3.96% in distorted grid which is less than 5%, by IEEE-1547 standards. Moreover, the limitations of the presented multilevel inverter system are highlighted by the disadvantages of traditional 2-level inverters, which include increased harmonics, restricted voltage levels, and poorer power quality.

This paper proposes a new multi-objective stochastic market settlement model for a coupled energy and reactive power ancillary service, named as MOSMS-CERP model, for a prospective grid integration of wind energy. It is a comprehensive two-stage scenario-based stochastic optimization model to incorporate the system uncertainties associated with wind power generations along with load demands. In this multi-objective framework, two competing objective functions as the expected total payment functions for energy and reactive power ancillary services are simultaneously minimized while satisfying all the system operating constraints including technical as well as market driven constraints. An efficient hybrid payment strategy is designed for energy market as well as reactive power ancillary service market for creating a competitive and fair market auction. A pay-as-bid payment mechanism is adopted for all thermal generating units whereas a pay-at-MCP (Market Clearing Price) is adopted for all hydro-powered generating units and wind farms for the energy market settlement. However, the ancillary service market of reactive power is settled using uniform price payment mechanism. It is a unique market settlement model, which combines three significant complexities such as stochastic nature, multi-objective constrained optimization, and meta-heuristics. The proposed MOSMS-CERP model is examined by applying five well-known, recent, and advanced meta-heuristic methods such as Non-dominated Sorting Genetic Algorithms (NSGA-II & I-NSGA-III), Multi-Objective Keshtel Algorithm (MOKA), Hybrid Fuzzy Multi-Objective Evolutionary Algorithm (HFMOEA), and Multi-Objective Particle Swarm Optimization (MOPSO) for producing the sets of Pareto-optimal solutions. A fair statistical comparison is carryout for these five meta-heuristic methods based on No Free Launch Theorem. All simulations are conducted on a standard IEEE 24-bus RTS with a doubly fed induction generator (DFIG) based wind farm on a DIgSILENT Power Factory software platform. The obtained simulation results ensure the robustness and effectiveness of the proposed MOSMS-CERP model.

Voltage stability assessment (VSA) is invariably an essential step in the satisfactory operation of power system. For this, several Voltage Stability Indices (VSIs) based on 1) Existence of solution for voltage equations, 2) Maximum power transfer (MPT) through a line, 3) MPT theorem/maximum loadability limit, 4) P-V curve, 5) Energy function and 6) Jacobian matrix can be found in the literature. VSIs assist in identifying weak lines and buses, voltage collapse proximity, maximum loadability, voltage stability margin, and prompting countermeasures against voltage instability. This article examines 78 different VSIs based on their concepts, assumptions, thresholds, and formulae. Additionally, mathematical proofs for 29 indices have been elaborated upon so that researchers may familiarize themselves with the underlying concepts of the other existing indices and find newer indices in modern power systems composed of intermittent power generation and dynamic loads. Also, a novel qualitative comparative analysis of VSIs is tabulated. Further, a novel framework has been proposed to aid researchers in shortlisting VSIs for a particular application. This article intends to serve as a meaningful reference to various researchers and individuals working in domains like Distributed Generation (DG) placing and rating, Voltage Stability Assessment (VSA), and power system planning.

In this paper, a fractional order active (P) and reactive (Q) power controller of doubly fed induction generator (DFIG) is developed and it performance is demonstrated using hardware in loop (HIL) testing with Opal RT OP4510 real-time platform. The DFIG in this paper is modelled using real-time State Space Nodal (SSN) based solver. The Rotor Side Converter (RSC) and doubly fed induction machine are implemented using Opal-RT’s ARTEMiS – nodal admittance-based SSN solver which uses smaller time steps than the conventional algorithms to facilitate real-time hardware-in-loop performance analysis of the system. The MATLAB-based FOMCON platform is utilized for designing the proposed fractional order controller. The proposed controller is developed and tested for various dynamic conditions. The performance of conventional proportional integral control and proposed fractional order control using Opal-RT’s OP4510 real-time platform is compared. The experimental results demonstrate improved efficiency with the use of proposed fractional order controller.

Hydrothermal scheduling is a significant concern in the field of power system economics that seeks to reduce the overall cost of generation by optimizing the hourly output of generators. However, this scheduling process suffers from a non-linear and complex problem due to set of uncertainty constraints from hydro and thermal units. Hence $\vartheta $ -constraint method has been proposed in which ramp constraints are considered that supply a feasible power region of the thermal units and minimize the uncertainty constraints. In addition, the existing solution techniques of hydro and thermal unit, commitment problems have not considered thermal constraints and their losses. This results in the frequent swapping of hydro units which decreases the energy conversion efficiency by a percentage at each repetition and increases the cost of the system. Therefore, a novel Chaotic geometric programming and chaotic approximation approach has been proposed that balance water discharge level without frequency swapping based on Armijo’s rule and Hazen Williams’s rule. Further, dynamic indexing is performed to increase the energy conversion efficiency. These balanced values are given to multiple-wave neural networks, to solve the hydro and thermal unit commitment problems. Furthermore, the cost of the system is minimized using Enhanced Ebola Optimization Search (EEOS) Algorithm in which the Srate parameter has been modified, thereby normalizing the hydrothermal scheduling problem for matured convergence. The proposed mathematical model has been implemented in the MATLAB R2022b on a 2.10 GHz, AMD Ryzen 5 3500U with Raden Vega Mobile Gfx processor, 8 GB RAM, 64 bit personal computer, and the results obtained show better performance than the previous approaches in terms of mean, median and standard deviation.

Due to the nonlinear relation between the generated power and voltage of photovoltaic (PV) arrays, there is a need to stimulate PV arrays to operate at maximum possible power. Maximum power can be tracked using the maximum power point tracker (MPPT). Due to the presence of several peaks on the power–voltage (P–V) characteristics of the shaded PV array, conventional MPPT such as hill climbing may show premature convergence, which can significantly reduce the generated power. Metaheuristic optimization algorithms (MOAs) have been used to avoid this problem. The main shortcomings of MOAs are the low convergence speed and the high ripples in the waveforms. Several strategies have been introduced to shorten the convergence time (CT) and improve the accuracy of convergence. The proposed technique sequentially uses a recent optimization algorithm called Mexican Axolotl Optimization (MAO) to capture the vicinity of the global peak of the P–V characteristics and move the control to a fuzzy logic controller (FLC) to accurately track the maximum power point. The proposed strategy extracts both the benefits of the MAO and FLC and avoids their limitations with the use of the high exploration involved in the MOA at the beginning of optimization and uses the fine accuracy of the FLC to fine-track the MPP. The results obtained from the proposed strategy show a substantial reduction in the CT and the highest accuracy of the global peak, which easily proves its superiority compared to other MPPT algorithms.

Reliable estimation of solar irradiance is required for many solar energy applications such as photovoltaics, water heating, cooking, solar microgrids, etc. Deep Learning techniques have shown outstanding behaviour for analysing complex datasets efficiently with high accuracy. Multi-Layer Perceptron (MLP), Long-Short Term Memory (LSTM), and Gated Recurrent Unit (RGU) techniques are found to be the most competitive techniques in the literature for solar irradiance forecasting. Therefore, in this study, a comparative analysis of those models is carried out using eleven years of NASA satellite data for training and testing. The grid search technique is used to optimize the networks architectures to ensure the best performance of the models for forecasting daily global solar irradiance. The results show that all models have similar accuracy with a mean square error close to 0.017 kWh/m2/day. However, the speed of training varies between 17 and 208 seconds for each model where GRU has shown higher speed than LSTM despite of containing more layers due to their computational complexity. The MLP is found to be the most efficient model due to using a low number of parameters 49,281 as compared to 1,025,793 for GRU. The study is of importance for reliable solar irradiance forecasting for any location worldwide.

Solar energy penetration in power grids helps to maintain power balance between generation and demand, thus enhances power grid performance. However, these integrations reduce grids’ time margin to respond against sudden frequency changes and re-establishing generation-demand equivalency. Thereby, it poses challenges to the performance and stability of the power system. It therefore becomes increasingly important to comprehend the real-time system data, identify and initiate necessary remedies to sustain the healthy system’s operation. This article presents a data-driven unified methodology for enhancing grid stability in solar energy-penetrated power network. The proposed method is a two-stage unified framework that incorporates Prompt Instability Evaluation (PIE) to evaluate impending system transient instability in first stage. In the second stage for a system unstable operation a Decision Assisted Adaptive Control (DAAC) is developed and implemented for corrective emergency control. A novel PIE is presented to perform a post-disturbance transient stability assessment using short-synchronized moving data. The PIE assesses the upcoming transient instability within the first few cycles following fault inception. Next, a novel DAAC is proposed to design an emergency remedial scheme for identifying location (where), magnitude, and type in real-time for unstable operations. The DAAC utilizes a novel Decision Rule Based Inference (DRBI) to evaluate suitable action sets that may be deployed by the DAAC to sustain system stability. The simulation results demonstrate the suitability of the proposed study on the system’s performance in the absence/presence of solar energy with topological variations.

Increasing greenhouse gases & air pollution are a global threat. Global forums are aggressively emphasizing on reducing the dependence on non-renewable resources. Battery Electric vehicle are among the initial initiative to reduce dependency on fossil fuels, and this demands more research to understand the energy requirements of a vehicle under different driving conditions. The performance of an Electric Vehicle depends on varying drive conditions and the Power Electronic Controller is primarily responsible for its sustainable operation. In this paper, a novel mathematical model is proposed to analyze the performance of an electric vehicle under different driving conditions. The model is simulated at different driving speeds keeping other longitudinal, lateral, and vertical parameters fixed. Rolling resistance forces, aerodynamic drag force, gradient force, total driving force, driving torque, and power requirements at different speeds have been calculated under standard driving conditions. The rolling resistance increases by 2.16 times with a change in the vehicle speed from 40 kmph to 120 kmph. The aerodynamic drag force increases ten times with a 10-degree gradient. The battery operating temperature is critical in vehicular performance, a hybrid Pneumatic-Liquid Thermal Management System is proposed to maintain battery operating temperature. Performance of the proposed model is simulated and found to be in line with the existing standards. This study concludes that road conditions, tyre pressure, velocity of travel, wind velocity, and temperature significantly influence the performance of an electric vehicle.

Efficient energy extraction in photovoltaic (PV) systems relies on the effective implementation of Maximum Power Point Tracking (MPPT) techniques. Conventional MPPT techniques often suffer from slow convergence speeds and suboptimal tracking performance, particularly under dynamic variations of environmental conditions. Smart optimization algorithms (SOA) using metaheuristic optimization algorithms can avoid these limitations inherent in conventional MPPT methods. The problem of slow convergence of the SOA is avoided in this paper using a novel strategy called Swarm Size Reduction (SSR) utilized with a Particle Swarm Optimization (PSO) algorithm, specifically designed to achieve short convergence time (CT) while maintaining exceptional tracking accuracy. The novelty of the proposed MPPT method introduced in this paper is the dynamic reduction of the swarm size of the PSO for improved performance of the MPPT of the PV systems. This adaptive reduction approach allows the algorithm to efficiently explore the solution space of PV systems and rapidly exploit it to identify the global maximum power point (GMPP) even under fast fluctuations of uneven solar irradiance and temperature. This pioneering ultra-fast MPPT method represents a significant advancement in PV system efficiency and promotes the wider adoption of solar energy as a reliable and sustainable power source. The results obtained from this proposed strategy are compared with several optimization algorithms to validate its superiority. This study aimed to use SSR with different swarm sizes and then find the optimum swarm size for the MPPT system to find the lowest CT. The output accentuates the exceptional performance of this innovative method, achieving a time reduction of as much as 75% when compared with the conventional PSO technique, with the optimal swarm size determined to be six.

This paper aims to develop the recently introduced Spilt-Source Inverter (SSI) topology to improve its boosting characteristics. New SSI topologies with high voltage gain are introduced in this paper. The proposed converters square the basic SSI’s boosting factor by utilizing an additional inductor, capacitor, and two diodes. Thus, the proposed converters are called Quadratic-Boost (or Square-Boost) SSIs (QBIs or SBIs). Four different QBI topologies are presented. One with continuous input current (CC-QBI), and the other draws a discontinuous input current (DC-QBI) but with reduced capacitor voltage stresses. This paper also introduces the small-signal model of the CC-QBI using state variables perturbance. Based on this model, the closed-loop voltage and current control approach of the dc-boosting factor are designed. Moreover, a modified space vector modulation (MSVM) scheme is presented to reduce the input current ripples. To evaluate the performance of the proposed topologies, a comparative study between them and the other counterpart from different perspectives is introduced. It can be found that the CC-QBI topology has superior boosting characteristics when operating with low input voltage compared with their counterparts. It has a higher boosting capability, lower capacitor voltages, and semiconductor stresses, especially when high voltage gains are required. These merits make the proposed topologies convenient to the Photovoltaic and Fuel-Cell systems. Finally, the feasibility of the suggested topology and the introduced mathematical model is verified via simulation and experimental results, which show good accordance with the theoretical analysis.

Freshwater resources are being rapidly depleted because of the increased demand resulting from exponential world population growth and the effects of climate change, especially in arid and semiarid regions (e.g., Saudi Arabia). The present study aimed to examine the changes in growth and inflorescence production of snapdragon (Antirrhinum majus L.) plants in response to irrigation with saline or magnetized water, in addition to application of inorganic and/or organic soil amendments. Three different water types—tap water, magnetized saline water, and nonmagnetized saline water—were used to irrigate A. majus plants with or without soil amendments consisting of ferrous sulfate (Fe2SO4) and/or peatmoss. Irrigation with magnetized saline water adversely affected vegetative growth, inflorescence production, mineral contents, and survival rates of A. majus plants as compared with irrigation with tap water or magnetized saline water. Nevertheless, compared with unmagnetized saline water treatment, magnetizing nonmagnetized saline water before irrigation significantly improved water characteristics and plant growth and survival. Moreover, the addition of inorganic or organic soil amendments enhanced the growth of A. majus plants regardless of irrigation water type. Interestingly, the combination of irrigating with magnetized saline water and soil amendments (Fe2SO4 and peatmoss) significantly enhanced the growth of A. majus plants to a level that was comparable to that of control plants irrigated with tap water without soil amendments. Magnetization improved water quality and increased plants’ ability to absorb water and nutrients from soil solution. The utilization of magnetized saline water for irrigating food and forage crops either alone or in combination with soil amendments has potential benefits that warrant further research.

This work primarily focuses on electrical characteristics of a hybrid power system (HPS) incorporating renewable energy generation (REG) (HPSREG). The major components of HPSREG are the resources coordinated with multi-unit of photovoltaic cells, multi-unit of wind turbine generators, a diesel engine generator (DEG), energy storage system (ESS) with diverse nature and an electric vehicle (EV). The performance characteristics of HPSREG are determined by constant generation of power from the various sources as well as varying load perturbations. As the variation in load demand will introduce fluctuation in frequency and power with constant generation. In few of overcome the frequency and power deviation under both the above-mentioned generation and load demand conditions, proper control technique is required. In order to control the deviation in frequency and power, an integration in the environment of fractional order (FO) calculus for proportional–integral–derivative (PID) controller and fuzzy controller, termed with FO-Fuzzy PID controller tuned with quasi-opposition based harmonic search (QOHS) algorithm has been proposed. The results acquired with the proposed FO-Fuzzy-PID controller are then analyzed along with FO-PID and PID controller route for quantify effectiveness for the same under the considered cases to determine the effectiveness of the algorithm undertaken. Sensitivity investigation is also conducted in order to show the strength of the technique under study of differences in HPSREG parameters of magnitude.

The sizing problem of the hybrid energy system (HES) is a crucial issue especially in rural communities because any wrong results can mislead the decision makers for building the new HES. Due to the intermittent nature of the renewable energy sources (RES) such as wind and PV, there will be a need for a high storage system and/or a standby diesel engine, which increase the investment, required, and increases the cost of energy (CoE). The use of smart grid concepts like the demand response (DR) using dynamic tariff can improve the system performance, enhance the stability, reduces the size and investments of HES components, reduces the customers' bills, and increases the energy providers' profits. The DR strategy will allow the customers to share the responsibility of the HES stability with the energy providers to maintain the stability of the HES. The DR strategies should be selected to ensure the balance between the available RES and the load requirements. In this article, a novel DR strategy is introduced to model the required change in the tariff with the battery state of charge and its charging/discharging power. The novel DR strategy is used in the sizing of the HES based on techno-economic objectives using three different soft computing optimization techniques. This article introduces modeling and simulation of the smart grid integrated with hybrid energy systems to supply a standalone load for a rural site in the north of Saudi Arabia. The sizing of the HES is built based on minimizing the CoE and the loss of load probability. The novel DR strategy introduced in this article reduced the size of the HES compared to the fixed load technique by 20.66%. The results obtained from this novel strategy proved its superiority in the sizing and operation stage of the HES.

Technology advancement in the last few decades allows large penetration of renewable energy resources in the distribution network (DN). The integration of such resources has shown a substantial impact on DN through power loss reduction and improved network reliability. Besides this, the existing protection system has encountered coordination challenges due to the bidirectional power flow, different types and capacity of generation sources, and changes in fault levels due to network operating modes (grid-connected or islanded). Such conditions may cause the relays to malfunction and imperil the effectiveness of the existing protection scheme. Therefore, an efficient and robust protection coordination scheme is imperative to avoid network reliability and stability issues to the grid. This review paper presents a comparative analysis of various protection techniques implemented to alleviate the impact of integrated resources into DN. Moreover, a comparison of classical and modified protection approaches in terms of advantages, shortcomings, and implementation costs is presented. The prime objective of this study is to highlight the prominence of utilizing user-defined programmable relays for modern DNs. Moreover, recommendations are presented by considering the application of user-defined relay characteristics that can be proved as a robust protection scheme to cope with the protection challenges in existing and future power systems developments.

In this paper, the optimal designing of passive power filter (PPF) is formulated as a multi-objective optimization (MOO) problem under several constraints of system's performance indices (PIs) such as individual as well as total harmonic distortion (THD) in the line current and the point of common coupling's (PCC) voltage, distribution line's ampacity under harmonic currents overloading, steady-state voltage profile, load power factor (PF) and a few associated with the filter itself. The optimal design parameters of a third-order damped filter are simultaneously determined for achieving maximum PF at the PCC while keeping system's other indices such as total demand distortion (TDD) in the line current, total voltage harmonic distortion (TVHD) at the PCC and total filter cost (FC) incurred at a minimum by obtaining a best-compromised solution using the newly proposed multi-objective Pareto-based firefly algorithm (pb-MOFA). A novel MOO approach inspired by the modified firefly algorithm and Pareto front is established in order to deal with PPF design problems. The extension of MOFA is considered for producing the Pareto optimal front and various conclusions are drawn by analysing the trade-offs among the objectives. The efficiency and accuracy of the proposed pb-MOFA, in solving the concerned MOO problem, is validated by comparing an obtained solution and three computed PIs viz. convergence metric (CM), generational distance (GD) and diversity metric (DM) with those obtained from popular multi-objective Pareto-based PSO (pb-MOPSO), non-dominated sorting genetic algorithm (NSGA-II) and recently introduced multi-objective slime mould algorithm (MOSMA). The need for true Pareto front (TPF) is served by the one obtained by Monte Carlo method. At last, the impacts of different background voltage distortion (BVD) levels and load-side's nonlinearity levels (NLLs) on filter performance are analysed.

A hybrid energy system (HES) is a perfect option for supplying electric energy to remote areas. A HES normally uses renewable energy sources such as wind and PV. Owing to the intermittent nature of these sources, HES should have batteries and/or conventional energy sources. HES proposed in this study is having wind, PV, batteries, and diesel generators. The design and operation of HES are considerably improved with the use of smart grid concepts. This study introduced a fuzzy logic controller to implement a new demand response strategy (DRS) where the electricity tariff is determined based on the state of charge of the battery, the charging/discharging power from the battery, and the previous response from the customers. A modified cuckoo search (MCS) optimization algorithm is introduced for sizing HES components for the lowest cost of energy (CoE) and loss of load probability (LOLP). A multiobjective function consisting of the CoE and LOLP is used to get the optimal design of HES. The MCS reduces the number of times that the optimization algorithm executes the objective function. The continuous reduction of the swarm size proposed in this paper enhances the exploration in the beginning and enhances exploitation at the final stage. The MCS is compared with 10 state-of-the-art optimization algorithms. The results from using MCS reduced the convergence time to 25-63% of the time needed by other optimization algorithms and the DRS introduced in this study reduced the CoE by 34% compared with the flat-rate pricing.

Nowadays, neural networks (NN) are being utilized in different control problems because of their excellent ability to model any nonlinear process. NN is suitable for the process having a wide range of operating conditions. In this work, the neural network-based internal model control (NN-IMC) scheme has been considered as a secondary controller for the load frequency control (LFC) problem in the restructured electricity market in order to meet Poolco and bilateral transactions. The proposed control scheme has been implemented on a 75-bus, 15-generator power system. The test system is divided into four areas. It is seen that area frequency errors have been eliminated at a steady state in all cases, and Gencos/Discos shared the increase in demand as per their involvement in the frequency regulation market. The results show that the NN-IMC control scheme has good performance and improves system responses effectively. Further, the performance of the NN-IMC control scheme has also been compared with the fractional-order proportional-integral-derivative (FO-PID) control scheme It is observed that the performance of the FO-PID controller is superior to the NN-IMC scheme in terms of settling time and similar to the NN-IMC control scheme in terms of maximum overshoots/undershoots. The compliance of the NN-IMC scheme has also been checked with NERC standards. It is seen that the NN-IMC scheme also satisfied the CPS1 and CPS2 control standards.

There is a growing interest to increase the grid integration of large-scale wind power farms (WPF). As most WPFs are located in remote areas where abundant wind resources are available, these sites are lacking communication infrastructures and network coverage which present major obstacles in enabling reliable data transmission between WPFs and their control centers. With the absence of unified communication network architecture, different vendors and manufacturers are developing their own monitoring and control solutions according to their needs. There is a knowledge gap related to the design of WPF communication networks, where the assumptions of available articles do not represent the complete monitoring data from WPF subsystems including wind turbines, meteorological towers and substations. This work aims to design a wireless network architecture for the grid integration of cyber physical wind energy system based on the IEC 61400-25 standard. The proposed architecture consists of four layers: a wind farm layer, a data acquisition layer, a communication network layer and an application layer. Wireless communication technologies outperform conventional wired-based solutions by offering lower costs, greater flexibility and easier deployment. Based on IEC 61400-25 standard, a wireless turbine area network is proposed for collecting sensing data from wind turbine parts, and connected to a wireless farm area network developed for communication between the remote control center and wind turbines. The network performance of the proposed wireless wind turbine internal network (includes the number of sensor nodes, data types and data size) is evaluated considering different wireless technologies (ZigBee, WiFi and WiMAX) in view of end-to-end delay, wireless channel capacity, and data loss. The simulation results show that wireless-based solutions can meet the delay requirements of the IEEE 1646 standard. This work contributes for building a redundant wireless communication infrastructure for remote monitoring of WPFs with scalable coverage and capacity.

An effective MPPT approach plays a significant role in increasing the efficiency of a PV system. Solar energy is a rich renewable energy source that is supplied to the earth in surplus by the sun. Solar PV systems are designed to utilize sunlight in order to meet the energy needs of the user. Due to unreliable climatic conditions, these PV frames have a non-linear characteristic that has a significant impact on their yield. Moreover, PSCs also affect the performance of PV systems in yielding maximum power. A significant progression in solar PV installations has resulted in rapid growth of MPPT techniques. As a result, a variety of MPPT approaches have been used to enhance the power yield of PV systems along with their advantages and disadvantages. Thus, it is essential for researchers to appraise developed MPPT strategies appropriately on regular basis. This study is novel because it provides an in-depth assessment of the current state of MPPT strategies for PV systems. On account of novelty, the authors analyzed the successive growth in MPPT strategies along with working principles, mathematical modeling, and simplified flow charts for better understanding by new learners. Moreover, the taxonomy and pro and cons of conventional and AI-based MPPT techniques are explored comprehensively. In addition, a comparative study based on key characteristics of PV system of all MPPT algorithms is depicted in a table, which can be used as a reference by various researchers while designing PV systems.

Solar photovoltaic (PV) panel parameter estimation is vital to manage solar-based microgrid operations, for which several techniques have been developed. Solar cell modeling using metaheuristic algorithms is found to be one of the accurate techniques. However, it requires experimental datasets, which may not be available for most of the industrial modules. Therefore, this study proposed a new model to estimate the solar parameters for two types of PV panels using manufacturer datasheets only. In addition, two optimization techniques called particle swarm optimization (PSO) and genetic algorithm (GA) were also investigated for solving this problem. The predicted results showed that GA is more accurate than PSO, but PSO is faster. The new model was tested under different solar radiation conditions and found to be accurate under all conditions, with an error which varied between 7.6212 × 10−4 under standard testing conditions and 0.0032 at 200 W/m2 solar radiation. Further comparison of the proposed method with other methods in the literature showed its capability to compete with other models despite not using experimental datasets. The study is of significance for the sustainable energy management of newly established commercial PV micro grids.

In this study, the economic and environmental benefits of stand-alone and grid integration are thoroughly analyzed with different system configurations of a PV/Wind/Diesel/Battery based hybrid energy system (HES) for five different climatic regions using hybrid optimization model for electric renewables (HOMER). A detailed techno-economic study of optimized hybrid systems is further examined by integrating the grid-connected option. The environmental benefits of HESs are discussed. The sensitivity of various sell-back price to the national grid is also investigated. Additionally, the barriers and opportunities of installing such projects in the off-grid regions are discussed. Results indicate that the cost of energy (COE, $/kWh) and the net present cost (NPC, $) of the stand-alone hybrid PV/Diesel/Battery for the Rajshahi region are slightly lower compared to other areas, considering the cost and environmental emissions. The same system in Chattogram shows great potential both financially and environmentally, over the other climatic zones. The grid-connected HES with the sell-back option offers significant cost-benefits (0.07$/kWh), even over the grid tariff (0.10$/kWh). Similar revenues can be attained with the grid-connected PV/Battery-based system as substantial amount of excess energy could be supplied to the grid facilities. In the grid integrated HES, around 45,582 kg-CO2/yr could be saved compared to grid only system, whereas this amount is 32,905 kg-CO2/yr over the stand-alone hybrid PV/Diesel/Battery one.

The continually increasing fossil fuel prices, the dwindling of these fuels, and the bad environmental effects which mainly contribute to global warming phenomena are the main motives to replace conventional transportation means to electric. Charging electric vehicles (EVs) from renewable energy systems (RES) substantially avoids the side effects of using fossil fuels. The higher the increase in the number of EVs the greater the challenge to the reliability of the conventional power system. Increasing charging connections for EVs to the power system may cause serious problems to the power system, such as voltage fluctuations, contingencies in transmission lines, and loss increases. This paper introduces a novel strategy to not only replace the drawbacks of the EV charging stations on the power system’s stability and reliability, but also to enhance the power system’s performance. This improvement can be achieved using a smart demand side management (DSM) strategy and vehicle to grid (V2G) concepts. The use of DSM increases the correlation between the loads and the available generation from the RES. Besides this, the use of DSM, and the use of V2G concepts, also helps in adding a backup for the power system by consuming surplus power during the high generation period and supplying stored energy to the power system during shortage in generation. The IEEE 30 bus system was used as an example of an existing power system where each load busbar was connected to a smart EV charging station (SEVCS). The performance of the system with and without the novel DSM and V2G concepts was compared to validate the superiority of the concepts in improving the performance of the power system. The use of modified particle swarm optimization in optimal sizing and optimal load flow reduced the cost of energy and the losses of the power system. The use of the smart DSM and V2G concepts substantially improved the voltage profile, the transmission line losses, the fuel cost of conventional power systems, and the stability of the power system.

Power system demand forecasting is a crucial task in the power system engineering field. This is due to the fact that most system planning and operation activities basically rely on proper forecasting models. Entire power infrastructures are built essentially to provide and serve the consumption of energy. Therefore, it is very necessary to construct robust and efficient predictive models in order to provide accurate load forecasting. In this paper, three techniques are utilized for short-term load forecasting. These techniques are deep neural network (DNN), multilayer perceptron-based artificial neural network (ANN), and decision tree-based prediction (DR). New predictive variables are included to enhance the overall forecasting and handle the difficulties caused by some categorical predictors. The comparison among these three techniques is executed based on coefficients of determination R2 and mean absolute error (MAE). Statistical tests are performed in order to verify the results and examine whether these models are statistically different or not. The results reveal that the DNN model outperformed the other models and was statistically different from them.

A fractional order PID (FOPID) control technique for automatic generation control (AGC) in a multi-area power system is presented in this study. To create a reliable controller, a variety of control strategies were used. The load frequency control (LFC) problem in a power system implementing different power transactions, such as bilateral and Poolco transactions, are investigated here. Because any control scheme’s performance is only as good as its parameters, the parameters of the designed control scheme were determined using the big bang big crunch (BBBC) algorithm. Furthermore, in this work, the effect of a superconductive magnetic energy storage (SMES) unit is addressed in the given test (two and four area) systems. When confronted with a fluctuation in immediate load, the SMES unit is thought to follow the initial drop in frequency and tie-line power in order to increase LFC. It is evident that the performance of an FOPID control scheme is improved in the presence of an SMES unit and it provides frequency, tie-line power, change in generation with reduced oscillations and settling time.

There is an increasing demand for power production day by day all over the globe; thus, hybrid frameworks have an essential role in producing sufficient power for the desirable load due to increasing power demand. The proposed hybrid renewable energy (HRE) systems are used to provide power in different areas to conquer the intermittence of wind and solar resources. The HRE system incorporates more than one renewable energy (RE) system. In this research article, the optimum power generation of different combinations of RE using different Maximum Power Point Tracking (MPPT) control methods is presented. The Fuel Cell (FC), FC–Photovoltaic (PV), FC–Wind (W), and FC–PV–W systems are developed to examine different MPPT controllers. The results show that the FC–PV–W HRE system produces the maximum power as compared to the FC, FC–PV, and FC–W systems. The FC–PV–W HRE system produces increased power compared to 94.24% from the FC system, 37.17% from the FC–PV hybrid system, and 15.8% from the FC–W hybrid framework with a Perturb and Observe (P&O) controller and, similarly, 74.57% from the FC system, 10.3% from the FC-PV hybrid system, and 31.64% from the FC-W hybrid system using a fuzzy logic (FL) controller, indicating that the best combination is the FC-PV-W hybrid system using an FL controller, which is useful for maximum power generation with reduced oscillations.

Substations are becoming increasingly reliant on international electrotechnical commission (IEC)-61850-enabled devices. However, device compatibility with these standards does not guarantee interoperability when devices are taken from different manufacturers. If interoperability of multivendor devices can be achieved, then power utilities will be in a position to implement multivendor devices in substations. The study here presents the development and testing of a digital substation test platform that incorporates devices from different manufacturers. The process bus communication and protection operation of the intelligent electronic devices (IEDs) were tested to validate device interoperability. The testbed was tested for two IED process bus communications, generic object-oriented substation event (GOOSE) and sampled measured value (SMV). The GOOSE is travelling between IED to IED with an end-to-end (ETE) delay of 2 ms and the SMV read by the IEDs are the same as the injected real-time substation inputs 220 kV and 1 kA. Three IED protection studies (overcurrent, earth fault, and overvoltage) were performed, and IED response curves were obtained. In addition, data monitoring and client–server communications were studied using installed software tools. The testbed configuration in this study has faced some real-time challenges regarding differences in device edition, device firmware, and ethernet switch due to its multivendor approach. All the mentioned configuration issues were resolved in this study with successful testing and validation of the testbed. The study of this testbed will provide solutions to the problems associated with a multivendor system faced by substation engineers and will help them in opting for multivendor installations. This system can be extended in the future by installing more multivendor devices with complex network topology and a SCADA system.

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.

This paper demonstrates the investigation of the acquired outcomes from consistent information observing a 467.2 kWp solar photovoltaic (SPV) framework commissioned on the roofs of three separate high-rise buildings, which are located at the location of 26.9585° N and 80.9992° E. Onside real-time performance for this system was investigated for three years, 2018–2020; this system contains 1460 SPV panels of 320 Wp each, having 20 PV panels per string, 09 DC/AC power conditioning units (PCU), and a SCADA (supervisory control and data acquisition) system for monitoring the other necessary parts of a grid-interactive SPV system. The outcomes of the different buildings are compared with each other to analyze the power output at the same input conditions. Hardware components of the plants with approximately the same ratings (P2 ~ 108.8 kWp + P3 ~ 128 kWp) are compared (with P1 ~ 230.4 kWp). Simulation modeling of the year 2020 in PVsyst tool for generated energy, Performance Ratio (PR), and Capacity Utilization Factor (CUF) are carried out additionally and compared with the installed rooftop grid-interactive SPV system of 467.2 kWp (~P1 + P2 + P3) at the site. Numerous performance parameters such as array efficiency, inverter efficiency, system efficiency, Performance Ratio (PR), and Capacity Utilization Factor (CUF) of the plant are evaluated and compared with already installed systems in different regions of the world. These points demonstrate great feedback to framework architects, workers, designers, and energy suppliers regarding the genuine limit and plausibility of the framework they can offer to clients. Moreover, one of the environmental benefits of the SPV plant is that the 467.2 kWp PV framework reduces the tremendous measure of CO2, SO2, and NOX that is discharged into the air.

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.

Studying and analyzing the challenges that the renewable energy sector faces can help evaluate the risks and improve the planning. This research is done by considering the challenges in the implementation of sustainable generation of electricity through RESs in India, based on factors, including technical, financial, involvement, support, and others. The triangular fuzzy number (TFN) method, based on fuzzy logic concept, is used to analyze the challenges in this study. In general, TFN comprises of three numbers, likewise Gaussian fuzzy numbers, trapezoidal fuzzy numbers also exist. The classified sets of numbers are denotations to decision-makers’ perspective or a choice towards the criterion preference. Although alternatives are many to design a fuzzy set depending on elements count, the TFNs are the ones considered as actual representations of a fuzzy number. On the other hand, cases the Gaussian or trapezoidal, are manifestations of fuzzy intervals. Another argument is that the membership function shape corresponding to the number of fuzzy set elements is largely dependent on the study. The challenges identified along with analysis in this paper will help the industry, governments, and policymakers focus and tackle essential issues to facilitate further the deployment of RESs in India towards a more sustainable country.

The use of hybrid renewable energy systems (HRES) has become the best option for supplying electricity to sites remote from the central power system because of its sustainability, environmental friendliness, and its low cost of energy compared to many conventional sources such as diesel generators. Due to the intermittent nature of renewable energy resources, there is a need however for an energy storage system (ESS) to store the surplus energy and feed the energy deficit. Most renewable sources used battery storage systems (BSS), a green hydrogen storage system (GHSS), and a diesel generator as a backup for these sources. Batteries are very expensive and have a very short lifetime, and GHSS have a very expensive initial cost and many security issues. In this paper, a system consisting of wind turbines and a photovoltaic (PV) array with a pumped hydro energy storage (PHES) system as the main energy storage to replace the expensive and short lifetime batteries is proposed. The proposed system is built to feed a remote area called Dumah Aljandal in the north of Saudi Arabia. A smart grid is used via a novel demand response strategy (DRS) with a dynamic tariff to reduce the size of the components and it reduces the cost of energy compared to a flat tariff. The use of the PHES with smart DRS reduced the cost of energy by 34.2%, and 41.1% compared to the use of BSS and GHSS as an ESS, respectively. Moreover, the use of 100% green energy sources will avoid the emission of an estimated 2.5 million tons of greenhouse gases every year. The proposed system will use a novel optimization algorithm called the gradually reduced particles of particle swarm optimization (GRP-PSO) algorithm to enhance the exploration and exploitation during the searching iterations. The GRP-PSO reduces the convergence time to 58% compared to the average convergence time of 10 optimization algorithms used for comparison. A sensitivity analysis study is introduced in this paper in which the effect of ±20% change in wind speed and solar irradiance are selected and the system showed a low effect of these resources on the Levelized cost of energy of the HRES. These outstanding results proved the superiority of using a pumped-storage system with a dynamic tariff demand response strategy compared to the other energy storage systems with flat-rate tariffs.

This paper proposes an Exponentially Varying Whale Optimization Algorithm (EVWOA) to solve the single-objective non-convex Cogeneration Units problem. This problem seeks to evaluate the optimal output of the generator unit to minimize a CHP system’s fuel costs. The nonlinear and non-convex characteristics of the objective function demands a powerful optimization technique. The traditional Whale Optimization Algorithm (WOA) is improved by incorporating four different acceleration functions to fine-tune its performance during exploration and exploitation phases. Among the four variants of the proposed WOA, the emphasis is laid on the EVWOA which uses the exponentially varying acceleration function (EVAF). The proposed EVWOA is tested on six different small-scale to large-scale systems. The results obtained for these six test systems, followed by a statistical study highlight the supremacy of EVWOA for finding the best optimal solution and the convergence traits.

From a residential point of view, home energy management (HEM) is an essential requirement in order to diminish peak demand and utility tariffs. The integration of renewable energy sources (RESs) together with battery energy storage systems (BESSs) and central battery storage system (CBSS) may promote energy and cost minimization. However, proper home appliance scheduling along with energy storage options is essential to significantly decrease the energy consumption profile and overall expenditure in real-time operation. This paper proposes a cost-effective HEM scheme in the microgrid framework to promote curtailing of energy usage and relevant utility tariff considering both energy storage and renewable sources integration. Usually, the household appliances have different runtime preferences and duration of operation based on user demand. This work considers a simulator designed in the C++ platform to address the domestic customer’s HEM issue based on usages priorities. The positive aspects of merging RESs, BESSs, and CBSSs with the proposed optimal power sharing algorithm (OPSA) are evaluated by considering three distinct case scenarios. Comprehensive analysis of each scenario considering the real-time scheduling of home appliances is conducted to substantiate the efficacy of the outlined energy and cost mitigation schemes. The results obtained demonstrate the effectiveness of the proposed algorithm to enable energy and cost savings up to 37.5% and 45% in comparison to the prevailing methodology.

In the above article [1], a minor typo error should be corrected via this correction article. The unit of filter’s capac-itances, in Table 8, is microfarad (symbolized $\mu $ F) as per the obtained results.

Halophytic plants can adapt to grow and thrive in highly saline conditions. Suaeda species are annual halophytes with high salt tolerance and are most suitable in the restoration of salinized or contaminated saline land and as food, forage, medicine, and bioenergy. In this study, we comprehensively analyzed the different metabolic responses of Suaeda species under salt and drought stress at ambient and elevated CO2 conditions. Seedlings of Suaeda species were treated with 500 mM NaCl and 5% of polyethylene glycol under elevated CO2 stress conditions for 24 h. Then, widely untargeted metabolites were detected by gas chromatography–mass spectrometry. Different metabolites involved in amino acid metabolism, glycolysis, photorespiration, and tricarboxylic acid cycle were quantitatively determined after stress treatments. A total of 61 primary metabolites were annotated. Different treatments increased the contents of certain metabolites, such as amino acids, sugars, and organic acids, as well as some antioxidants, such as quininic acid, kaempferol, and melatonin. These substances may be correlated with osmotic tolerance, increased antioxidant activity, and medical and nutritional value in the species. This study suggests that various metabolites differentially accumulated in C4Suaeda species under varying stress conditions. Furthermore, this work provides new insights into the key secondary metabolite pathway involved in stress tolerance.

In this study, optimal decision-making process in photovoltaic (PV) system location selection in Saudi Arabia is described. First, to identify the criteria that influence the decision of selecting a suitable location for the PV system, the geographical information system (GIS)-based multi-criteria decision making (MCDM) approach is used. Next, to assess the weights of the criteria that present different aspects of the investigated locations, four major criteria and 11 sub-criteria are proposed, and analytic hierarchy process (AHP) is applied to develop comparison decision matrix. Finally, the order preference by similarity to ideal solution (TOPSIS) technique evaluates and classifies 17 cities (such as Riyadh, Jeddah) in Saudi Arabia. The result shows that Tabuk city in the northern of Saudi Arabia is the best location. Among the 17 cities, the performance score of seven cities is above or equal 80%, and Tabuk city has the highest score with 87%. This analytical approach could contribute as an early planning to locate suitable sites for the selection of PV system region in Saudi Arabia.

This work introduces multi-objective water cycle algorithm (MOWCA) to find the accurate location and size of distributed energy resource (DERs) considering different load models for two seasons (winter, and summer). The impact of uncertainties produced from load and renewable energy resource (RES) such as wind turbine (WT) and photovoltaic (PV) on the performance of the radial distribution system (RDS) are covered as this is closer to the real operation condition. The point estimate method (PEM) is applied for modeling the RES uncertainties. An optimization technique is implemented to find the multi-objective optimal allocation of RESs in RDSs considering uncertainty effect. The main objectives of the work are to maximize the technical, economic and environmental benefits by minimizing different objective functions such as the dissipated power, the voltage deviation, DG cost and total emissions. The proposed multi-objective model is solved by using multi-objective water cycle algorithm (MOWCA), considering the Pareto criterion with nonlinear sorting based on fuzzy mechanism. The proposed algorithm is carried out on different IEEE power systems with various cases.