Search

Climate change is a global issue that requires collective action to address. One of the most pressing concerns is reducing emissions resulting from combustion processes. The use of renewable energy sources and green energy has become a trend worldwide. Solar energy is one of the most promising sources due to its abundance and simplicity of implementation. The city of Aswan, located in the South of Egypt, has a high solar radiation that makes it ideal for utilizing solar power. The current study investigates the optimal design for a sustainable building electricity system at the Aswan Campus of the Arab Academy for Science, Technology & Maritime Transport (AASTMT) in Egypt. The campus has four electricity sources: a utility grid, PV panels, batteries, and a diesel generator, along with a weather station. Experimental investigations have been carried out in this research paper to study the performance characteristics of solar power. Moreover, HOMER pro software is used to model various configurations of the campus grid, including different photovoltaic (PV) panel types and tracking systems. The simulations are compared with real-world data collected from a weather station on campus. Additionally, CO2 and NO2 emissions are measured to assess the environmental impact of different scenarios. The total net cost over the life cycle is also calculated for different cases. The results demonstrate that the addition of a PV renewable system can reduce traditional grid usage by 38% and emissions by 50%. A decrease in the Levelized Cost of Energy (LOCE) from USD 0.0647 to USD 0.0535 is reported. Moreover, the difference in NCP cost between dual-axis tracking and fixed zero angle is USD 143,488. The dual degree tracker for PV panels can further enhance energy production by 30% more, compared with fixed panels, while reducing carbon dioxide emissions by more than 20%. The simulation results reveal that tracking systems provide greater energy generation, and that a cost–benefit analysis may prioritize fixed panels in some cases. The results from the HOMER software simulations closely match those of the experimental data, which is that the total presentation error does not exceed 8%, demonstrating the software's effectiveness for optimizing renewable energy systems. This study demonstrates that a comprehensive analysis and optimization of a building's energy sources can significantly reduce costs, lower emissions, and promote the use of renewable energy, particularly solar power. [ABSTRACT FROM AUTHOR]

In response to the escalating global energy crisis, the motivation for this research has been derived from the need for sustainable and efficient energy solutions. A gap in existing renewable energy systems, particularly in terms of stability and efficiency under variable environmental conditions, has been recognized, leading to the introduction of a novel hybrid system that combines photovoltaic (PV) and wind energy. The innovation of this study lies in the methodological approach that has been adopted, integrating dynamic modeling with a sophisticated control mechanism. This mechanism, a blend of model predictive control (MPC) and particle swarm optimization (PSO), has been specifically designed to address the fluctuations inherent in PV and wind power sources. The methodology involves a detailed stability analysis using Lyapunov's theorem, a critical step distinguishing this system from conventional renewable energy solutions. The integration of MPC and PSO, pivotal in enhancing the system's adaptability and optimizing the maximum power point tracking (MPPT) process, improves control efficiency across key components like the doubly fed induction generator (DFIG), rectifier-sourced converter (RSC), and grid-side converter (GSC). Through rigorous MATLAB simulations, the system's robust response to changing solar irradiance and wind velocities has been demonstrated. The key findings confirm the system's ability to maintain stable power generation, underscoring its practicality and efficiency in renewable energy integration. Not only has this study filled a crucial gap in renewable energy control systems, but it has also set a precedent for future research in sustainable energy technologies. [ABSTRACT FROM AUTHOR]

This paper outlines the development of a high-performance maximum power point tracking (MPPT)-based solar irradiance estimator for photovoltaic (PV) systems. The suggested estimator is constructed around a simple current–voltage-based algebraic equation that hinges on the operation of the PV system at its maximum power point (MPP). In the realm of MPP operation, the overall system is driven by a nonlinear MPPT controller. To achieve this function, we integrated a hybrid incremental conductance integral backstepping (H-INC-IBS) controller to effectively regulate the PV system. This controller was specially chosen for its powerful potency in maximizing the dynamics of the PV system, leading to heightened robustness against changing environmental conditions. The simulation results are provided to showcase the suitability of the proposed estimator. Furthermore, the estimator was verified under experimental conditions, highlighting its soundness and practicality. Through evaluations and comparisons with the conventional irradiance estimator, this paper aimed to emphasize the superiority of the proposed solar irradiance estimator in providing more accurate estimations of solar irradiance for PV systems operating under MPPT supervision. [ABSTRACT FROM AUTHOR]

A photovoltaic emulator (PVE) is a specialized device designed to mimic the static and dynamic properties of a solar panel. It serves as a crucial tool for testing and validating PV systems. Despite the unpredictable variations in real environmental conditions, the PVE offers a controlled environment, facilitating the smooth implementation and testing of PV subsystems. The PVE consists of two essential components: the reference PV model and the PVE power electronics controller. For simplicity, both these elements must be inherently straightforward. Numerous advanced methods have been proposed in the literature to efficiently integrate the PV model into the PVE system. However, these approaches often involve complex iterative computations of intricate equations related to solar panels, making them less practical. To address these limitations, this paper introduces a novel PVE that aims to simplify the implementation and integration of the PV model. The proposed system employs a simple and non-iterative approach to provide the reference to the PVE, using a straightforward explicit model of the solar panel. In comparison to existing works, the proposed PVE stands out for its high flexibility and simplicity as it does not require the complex iterative computations of implicit equations related to the solar panel model. The overall PVE system is implemented with a basic proportional-integral (PI) controller and a DC-DC buck power electronic converter. It is then validated using a 200 W solar panel through a series of experiments, including the EN 50530 Test. The results of these experiments demonstrate that the proposed PVE efficiently reproduces the static and dynamic characteristics of a solar panel. [ABSTRACT FROM AUTHOR]

In this paper, multiple virtual synchronous machines (VISMAs) with fuzzy proportional integral derivative (FPID) controllers optimized by differential evolution (DE) are proposed to maintain frequency stability in the grid in the presence of renewable penetration, such as wind and solar photovoltaic (PV) systems, residential loads, and industrial loads, by reducing the area control error in the objective function. Simulations are conducted using MATLAB/Simulink, and in the optimization process, the integral of the time-weighted absolute error (ITAE) is used as the objective function. In the work to obtain optimized values of renewable energy sources (RESs), fuzzy membership functions, controller gain parameters, and loads for system modeling, differential evolution and genetic algorithm (GA) methods are applied and the results were compared. It was shown that better results were achieved while FPID controllers were optimized by DE in the presence of multiple VISMAs than DE in the presence of single VISMAs and GA in multiple VISMAs. Moreover, the study is compared to integral control methods in which, compared to all controllers, the proposed controller reduces undershoot by 0.0674 Hz more than a single VISMAs, in which it is improved approximately by 97.82%. Similarly, the proposed controller improves the system settling time, rise time, and overshoot by more than 99.5% compared to the classical integral controller. To examine the robust operation of the system under the proposed controller, the system was run under a wide range of disturbances and uncertainties using random load perturbation of ± 20%, in which the proposed controller retains the system frequency by reducing or damping the system oscillation. [ABSTRACT FROM AUTHOR]

In this study, a fuzzy proportional integral derivative controller (FPID) was adjusted using the differential evolution (DE) method to enhance the automated generation control (AGC) of a three-zone reheat-type power system. The objective function used in this study was an integral of the time-weighted absolute error (ITAE). In the optimization, the gain control parameters of the proportional integral (PI), the integral (I), and FPID were optimized and compared to improve the limitations drawn by the controller over a few parameters. To demonstrate that FPID controllers with IPFC produce better and more accurate optimization results than integral and PI controllers optimized by DE, the interline power flow control (IPFC) of a flexible AC transmission system (FACTS) device with suitable connections and control parameter optimization was used. Also, the particle swarm optimization (PSO) PID with IPFC was compared with the proposed DEFPID + IPFC, and better results were achieved by using the DE technique. Similarly, to demonstrate the suggested technology's strong control capacity, random load changes were applied to the system in various conditions, and it was demonstrated that the suggested control unit easily tolerated random load perturbations and returned the system to a stable functioning state. [ABSTRACT FROM AUTHOR]

In this article, the problem of voltage and frequency stability in a hybrid multi-area power system including renewable energy sources (RES) and electric vehicles has been investigated. Fractional order systems have been used to design innovative controllers for both load frequency control (LFC) and automatic voltage regulator (AVR) based on the combination of fractional order proportional-integral and proportional-integral-derivative plus double derivative (FOPI–PIDD2). Here, the dandelion optimizer (DO) algorithm is used to optimize the proposed FOPI–PIDD2 controller to stabilize the voltage and frequency of the system. Finally, the results of simulations performed on MATLAB/Simulink show fast, stable, and robust performance based on sensitivity analysis, as well as the superiority of the proposed optimal control strategy in damping frequency fluctuations and active power, exchanged between areas when faced with step changes in load, the changes in the generation rate of units, and the uncertainties caused by the wide changes of dynamic values. [ABSTRACT FROM AUTHOR]

This article presents a new cascaded control strategy to control the power flow in a renewable-energy-based microgrid operating in grid-connected mode. The microgrid model is composed of an AC utility grid interfaced with a multi-functional grid interactive converter (MF-GIC) acting as a grid-forming converter, a photovoltaic (PV) power-generation system acting as grid-feeding distributed generation unit, and various sensitive/non-sensitive customer loads. The proposed control strategy consists of a fractional order PI (FO-PI) controller to smoothly regulate the power flow between the utility grid, distributed generation unit, and the customers. The proposed controller exploits the advantages of FO (Fractional Order) calculus in improving the steady-state and dynamic performance of the renewable-energy-based microgrid under various operating conditions and during system uncertainties. To tune the control parameters of the proposed controller, a recently developed evaporation-rate-based water-cycle algorithm (ERWCA) is utilized. The performance of the proposed control strategy is tested under various operating conditions to show its efficacy over the conventional controller. The result shows that the proposed controller is effective and robust in maintaining all the system parameters within limits under all operating conditions, including system uncertainties. [ABSTRACT FROM AUTHOR]

Improving photovoltaic systems in terms of temporal responsiveness, lowering steady-state ripples, high efficiency, low complexity, and decreased tracking time under various circumstances is becoming increasingly important. A particle-swarm optimizer (PSO) is frequently used for maximum power-point tracking (MPPT) of photovoltaic (PV) energy systems. However, during partial-shadowing circumstances (PSCs), this technique has three major drawbacks. The first problem is that it slowly converges toward the maximum power point (MPP). The second issue is that the PSO is a time-invariant optimizer; therefore, when there is a time-variable shadow pattern (SP), it adheres to the first global peak instead of following the dynamic global peak (GP). The third problem is the high oscillation around the steady state. Therefore, this article proposes a hybrid PSO-PID algorithm for solving the PSO's three challenges described above and improving the PV system's performance under uniform irradiance and PSCs. The PID is designed to work with the PSO algorithm to observe the maximum voltage that is calculated by subtracting from the output voltage of the DC-DC boost converter and sending the variation to a PID controller, which reduces the error percentage obtained by conventional PSO and increases system efficiency by providing the precise converter-duty cycle value. The proposed hybrid PSO-PID approach is compared with a conventional PSO and bat algorithms (BAs) to show its superiority, which has the highest tracking efficiency (99.97%), the lowest power ripples (5.9 W), and the fastest response time (0.002 s). The three aforementioned issues can be successfully solved using the hybrid PSO-PID technique; it also offers good performance with shorter times and faster convergence to the dynamic GP. The results show that the developed PID is useful in enhancing the conventional PSO algorithm and solar-system performance. [ABSTRACT FROM AUTHOR]

Fault location is one of the main challenges in the distribution network due to its expanse and complexity. Today, with the advent of phasor measurement units (PMU), various techniques for fault location using these devices have been proposed. In this research, distribution network fault location is defined as an optimization problem, and the network fault location is determined by solving it. This is done by combining PMU data before and after the fault with the power system status estimation (PSSE) problem. Two new objective functions are designed to identify the faulty section and fault location based on calculating the voltage difference between the two ends of the grid lines. In the proposed algorithm, the purpose of combining the PMU in the PSSE problem is to estimate the voltage and current quantities at the branch point and the total network nodes after the fault occurs. Branch point quantities are calculated using the PMU and the governing equations of the π line model for each network section, and the faulty section is identified based on a comparison of the resulting values. The advantages of the proposed algorithm include simplicity, step-by-step implementation, efficiency in conditions of different branch specifications, application for various types of faults including short-circuit and series, and its optimal accuracy compared to other methods. Finally, the proposed algorithm has been implemented on the IEEE 123-node distribution feeder and its performance has been evaluated for changes in various factors including fault resistance, type of fault, angle of occurrence of a fault, uncertainty in loading states, and PMU measurement error. The results show the appropriate accuracy of the proposed algorithm showing that it was able to determine the location of the fault with a maximum error of 1.21% at a maximum time of 23.87 s. [ABSTRACT FROM AUTHOR]

This paper addresses the active torque ripple compensation of a permanent magnet brushless direct-current motor (PMBLDC) drive with a new pulse width modulation (PWM) technique and advance angle method. Torque ripple is a well-known problem in BLDC motors which is produced by a discrepancy between the stator current and the back electromotive force (back-emf) waveforms. The advanced angle method proposed in this paper generates a maximum torque in the PM BLDC motor by decreasing the displacement between the phase voltage and phase current in proportion to the load. Further, a simple and comprehensive PWM-PWM logic is proposed in this paper to reduce the torque ripples. The test results show that the BLDC motor drive achieves good steady-state performance while maintaining a quick dynamic response. The performance of the PWM-PWM logic and advance angle method, have been tested and compared with the practical results for the characteristics of DC bus voltage, DC bus current, electromagnetic torque, shaft torque, mechanical torque, phase voltage, phase current and PWM signal. [ABSTRACT FROM AUTHOR]

In this article, a distributed control scheme to compensate for voltage harmonics in islanded microgrids is presented, where each distributed generation (DG) source has a primary control level and a secondary control level. In addition to the voltage and current control loops, the primary control level of DGs includes virtual impedance control loops in the main and harmonic components, which are responsible for dividing the power of the main component and the non-main component (harmonic) between the DGs of the microgrid, respectively. For coordinated operation between the inverters when facing the islanding phenomenon, it is very beneficial to use a droop controller structure. Here, the traditional droop controller is modified in such a way that the power is proportionally divided between the DGs, which causes accurate voltage regulation at the output of the DGs. By presenting a model for the inverter connected to the nonlinear load, a harmonic droop controller is designed. Through the droop controller related to each harmonic, the harmonic voltages are calculated and added to the reference voltage, which improves the quality of the output voltage. Then, the inverter voltage control loop is modified with resistive impedance in the presence of nonlinear loads in such a way that, when combined with the harmonic droop controller, the total harmonic distortion (THD) of the output voltage is significantly reduced. Lastly, the proposed method is implemented on the microgrid through MATLAB software, and the results show the ability of the proposed method to reduce voltage harmonics in the parallel operation of inverters. [ABSTRACT FROM AUTHOR]

Due to cutting-edge innovations in industry and academia, research is more centered around multilevel inverters (MLIs), which play a significant role in different high/medium voltage and high-power applications when contrasted with traditional inverters. Relative analysis of the reduced DC source count and switch inverter topologies highlight its significant benefits, which include control complexity, switch count, source count, reliability, efficiency, cost, voltage stress, total harmonic distortion (THD), and power quality. When switched-capacitor technology is deployed, it is seen that with the assistance of 14 switches, a 53 level result is accomplished, and the THD is just around 1.41%, which is kept up with as per the IEEE 519-2014 norms. Whenever cascaded MLI topology is employed, the inversion efficiency is more prominent, and is about 99.06%. Hence, this review focuses on a few of the late-evolved MLIs utilized, and the benefits and drawbacks for different topologies are examined. To assist with current modern research in this field and the decision of the proper inverter for various applications, a novel topology of an MLI can be planned later on. Different setups of MLIs have been exhaustively covered and reviewed. [ABSTRACT FROM AUTHOR]

Most grid-connected DC/AC inverters use traditional proportional–integral (PI) controllers in a synchronous frame. In addition to poor disturbance rejection capabilities, these PI controllers also exhibit steady-state errors for sinusoidal reference signals. To address these drawbacks, this article investigates the use of a high-order controller in the stationary frame and then compares it with the standard PI controller. The effectiveness of the high-order controller in the stationary frame has been examined by providing an infinite gain at a resonance frequency. In this work, the design of high-order and PI controllers and tuning instructions are given. Furthermore, both high-order and PI current-controlled two-level and three-level neutral point clamped (NPC) inverters are compared. Various operational conditions are used for the comparison. The high-order controller reduced the total harmonic distortion (THD) of the injected current by 1.15% for the two-level inverter in normal conditions as compared to the PI controller and 0.9% for the three-level NPC inverters. Furthermore, it reduced the THD in balanced abnormal conditions by 0.5% for the two-level inverter and 0.18% for the three-level NPC inverters. However, the dq controller has a lower THD during unbalance and short circuit conditions. [ABSTRACT FROM AUTHOR]

Renewable energy and electric vehicle technology are the two pillars for achieving a sustainable future. Floating solar power plants use PV modules on water infrastructure to save the land and increase module efficiency. Furthermore, the reduction in evaporation saves water. Electric vehicles are one of the fastest-growing markets and the most successful technologies to combat the problem of energy and climate change. This research aims to construct a floating PV system on the lake of the Vellore Institute of Technology (VIT), to analyze electric vehicle performance and greenhouse gas (GHG) emissions when charged using the installed floating PV system. To address this, a 1.5 MWP floating PV system was simulated and analyzed using Helioscope software. When charged from the proposed floating PV plant, electric bikes, scooters, and cars saved CO2 emissions. When charged from a floating PV, E-bike, E-scooter, and E-car Net CO2 emissions became zero in 25.5, 12.1, and 7.7 months, respectively. After the aforementioned time periods, all three electric vehicle types were zero-emission vehicles. The required charge for all three types of vehicles (1,000,000 km) was analyzed using a floating PV system. E-bike, E-scooter, and E-car CO2 emission savings were −8,516,000 g/kWh, −328,000 g/kWh, and 525,600,000 g/kWh, respectively. All three types of electric vehicles can reduce CO2 emissions for nations that rely on renewable energy, but only electric cars save carbon emissions over fixed distances. Through this research, we finally conclude that electric cars reduce CO2 emissions the most compared to other electric vehicles. [ABSTRACT FROM AUTHOR]

This study implemented a bidirectional artificial neuro-fuzzy inference system (ANFIS) to solve the problem of system resilience in synchronized and islanded grid mode/operation (during normal operation and in the event of a catastrophic disaster, respectively). Included in this setup are photovoltaics, wind turbines, batteries, and smart load management. Solar panels, wind turbines, and battery-charging supercapacitors are just a few of the sustainable energy sources ANFIS coordinates. The first step in the process was the development of a mode-specific control algorithm to address the system's current behavior. Relative ANFIS will take over to greatly boost resilience during times of crisis, power savings, and routine operations. A bidirectional converter connects the battery in order to keep the DC link stable and allow energy displacement due to changes in generation and consumption. When combined with the ANFIS algorithm, PV can be used to meet precise power needs. This means it can safeguard the battery from extreme conditions such as overcharging or discharging. The wind system is optimized for an island environment and will perform as designed. The efficiency of the system and the life of the batteries both improve. Improvements to the inverter's functionality can be attributed to the use of synchronous reference frame transformation for control. Based on the available solar power, wind power, and system state of charge (SOC), the anticipated fuzzy rule-based ANFIS will take over. Furthermore, the synchronized grid was compared to ANFIS. The study uses MATLAB/Simulink to demonstrate the robustness of the system under test. [ABSTRACT FROM AUTHOR]

Power systems have recently faced significant challenges due to the increased penetration of renewable energy sources (RES) such as frequency deviation due to fluctuations, unpredictable nature, and uncertainty of this RES. In this paper, a cascaded controller called (1+PD)-PID is proposed to reduce the influence of RES uncertainties on the system and to maintain the system's reliability during fluctuations. The proposed controller is a combination of (1+PD) and PID controllers in order. The output signal of the (1+PD) controller along with the frequency deviation and the power difference between adjacent areas are used as inputs to the PID controller to create the load reference signal. The parameters of the suggested controller are optimally tuned using the African Vulture Optimization Algorithm (AVOA) to ensure the best performance of the controller. A two-area interconnected system with non-reheat thermal power units combined with RES such as solar and wind energy is modeled using MATLAB/Simulink to evaluate the system response. The controller effectiveness is verified by subjecting the studied system to various types of fluctuations such as step load disturbance, variable load perturbation and RES penetration. The obtained simulation results prove that the proposed (1+PD)-PID controller in integration with AVOA offers a significant improvement in the system performance specifications. Moreover, the proposed AVOA-based (1+PD)-PID controller has proven its superiority over other comparable controllers having the least fitness function of 6.01 × 10−5. [ABSTRACT FROM AUTHOR]

In this paper, a stochastic-metaheuristic model is performed for multi-objective allocation of photovoltaic (PV) resources in 33-bus and 69-bus distribution systems to minimize power losses of the distribution system lines, improving the voltage profile and voltage stability of the distribution system buses, considering the uncertainty of PV units' power and network demand. The decision-making variables, including installation location and the size of PVs, are determined optimally via an improved human learning optimization algorithm (IHLOA). The conventional human learning optimization algorithm (IHLOA) is improved based on Gaussian mutation to enhance the exploration capability and avoid getting trapped in local optimal. The methodology is implemented in two cases as deterministic and stochastic without and with uncertainties, respectively. Monte Carol Simulation (MCS) based on probability distribution function (PDF) is used for uncertainties modeling. The deterministic results proved the superiority of the IHLOA compared with conventional HLOA, particle swarm optimization (PSO), to obtain better values of the different objectives and faster convergence speed and accuracy. The results are clear that enhancing the conventional HLOA has increased the algorithm's ability to explore and achieve the optimal global solution with higher convergence accuracy. Moreover, the stochastic results were clear that considering the uncertainties leads to correct and robust decision-making against existing uncertainties and accurate knowledge of the network operator against the exact values of various objectives compared to the deterministic case. [ABSTRACT FROM AUTHOR]

The number of unsecured and portable Internet of Things (IoT) devices in the smart industry is growing exponentially. A diversity of centralized and distributed platforms have been implemented to defend against security attacks; however, these platforms are insecure because of their low storage capacities, high power utilization, single node failure, underutilized resources, and high end-to-end delay. Blockchain and Software-Defined Networking (SDN) are growing technologies to create a secure system and to ensure safe network connectivity. Blockchain technology offers a strong and trustworthy foundation to deal with threats and problems, including safety, privacy, adaptability, scalability, and security. However, the integration of blockchain with SDN is still in the implementation phase, which provides an efficient resource allocation and reduced latency that can overcome the issues of industrial IoT networks. We propose an energy-efficient blockchain-integrated software-defined networking architecture for Industrial IoT (IIoT) to overcome these challenges. We present a framework for implementing decentralized blockchain integrated with SDN for IIoT applications to achieve efficient energy utilization and cluster-head selection. Additionally, the blockchain-enabled distributed ledger ensures data consistency throughout the SDN controller network and keeps a record of the nodes enforced in the controller. The simulation result shows that the proposed model provides the best energy consumption, end-to-end latency, and overall throughput compared to the existing works. [ABSTRACT FROM AUTHOR]

The purpose of this research is to develop an efficient single-phase grid-connected PV system using a better performing asymmetrical multilevel inverter (AMI). Circuit component reduction, harmonic reduction, and grid integration are critical criteria for better inverter efficiency. The proposed inverter's optimized topology requires seven unidirectional switches, three symmetric dc sources, and three diodes to produce an asymmetric fifteen level output; whereas, the same configuration will generate 7, 11, and 15-level output with an appropriate choice of dc source magnitudes. It is possible to reduce inverter losses and boost efficiency by decreasing the number of switches used. The integration of an asymmetric 15-level inverter with a grid-connected solar photovoltaic system is discussed in this article. A grid-connected solar photovoltaic (GCSPV) system is modelled and simulated using an asymmetric 15-level inverter. The dc sources of the 15-level inverter are replaced with PV sources. The results were analyzed with different operating temperatures and solar irradiance conditions. The GCSPV system is controlled by a closed-loop control system using Particle Swarm Optimization (PSO), Harris Hawk Optimization (HHO), and Hybrid Particle Swarm Optimization-Genetic Algorithm (PSOGA) based Proportional plus Integral (PI) controllers. Grid voltage, grid current, grid power, and total harmonic distortion (THD) of grid currents were analyzed. The performance of the 15-level asymmetric inverter was evaluated by comparing the THD of the grid current and the efficiency of the grid-connected photovoltaic system. [ABSTRACT FROM AUTHOR]

Today, the world is confronted with the issue of environmental pollution as a result of our dependence on fossil fuels for our energy needs. Developed and developing countries are therefore adopting different strategies to curb environmental problems. This work is thus designed to probe the effect of renewable energy (RE), information and communication technologies (ICT), government stability, and GDP on ecological footprints (EF) in G11 nations. We utilized the annual data from 1990–2020 and applied robust methodologies to present the findings. The CS-ARDL method shows that renewable energy, ICT, and government stability are essential factors in lowering environmental pollution in G11 countries. Therefore, in light of the findings, this work suggests an increase in the use of RE sources such as solar, wind, and hydropower in the total energy mix of the various countries. RE usage improves air quality and saves the natural environment from further destruction. The countries also need to enhance their communication technologies in the economic sector. Moreover, policymakers must also introduce the required policies that would promote the use of RE in various countries. This will make people adopt clean energy sources at the domestic and commercial levels. [ABSTRACT FROM AUTHOR]

Owing to the various sources of complexity in the electrical power system, such as integrating intermittent renewable energy resources and widely spread nonlinear power system components, which result in sudden changes in the power system operating conditions, the conventional PID controller fails to track such dynamic challenges to mitigate the frequency deviation problem. Thus, in this paper, a fuzzy PI controller is proposed to enhance the automatic generation control system (AGC) against step disturbance, dynamic disturbance, and wind energy disturbance in a single area system. The proposed controller is initialized by using Equilibrium Optimization and proved its superiority through comparison with a classical PI optimized base. Results show that the fuzzy PI controller can reduce the peak-to-peak deviation in the frequency by 30–59% under wind disturbance, compared to a classical PI optimized base. Moreover, a fuzzy PID controller is also proposed and EO initialized in this paper to compare with the PIDA optimized by several techniques in the two-area system. Results show that the fuzzy PID controller can reduce the peak-to-peak deviation in the frequency of area 1 by 30–50% and the deviation of frequency in area 2 by 13–48% under wave disturbance, compared to the classical PIDA optimized base. [ABSTRACT FROM AUTHOR]

Demand-side management (DSM) is a significant component of the smart grid. DSM without sufficient generation capabilities cannot be realized; taking that concern into account, the integration of distributed energy resources (solar, wind, waste-to-energy, EV, or storage systems) has brought effective transformation and challenges to the smart grid. In this review article, it is noted that to overcome these issues, it is crucial to analyze demand-side management from the generation point of view in considering various operational constraints and objectives and identifying multiple factors that affect better planning, scheduling, and management. In this paper, gaps in the research and possible prospects are discussed briefly to provide a proper insight into the current implementation of DSM using distributed energy resources and storage. With the expectation of an increase in the adoption of various types of distributed generation, it is estimated that DSM operations can offer a valuable opportunity for customers and utility aggregators to become active participants in the scheduling, dispatch, and market-oriented trading of energy. This review of DSM will help develop better energy management strategies and reduce system uncertainties, variations, and constraints. [ABSTRACT FROM AUTHOR]

An important issue in power systems is the optimal operation of microgrids with demand-side management. The implementation of demand-side management programs, on the one hand, reduces the cost of operating the power system, and on the other hand, the implementation of such programs requires financial incentive policies. In this paper, the problem of the optimal operation of microgrids along with demand-side management (DSM) is formulated as an optimization problem. Load shifting is considered an effective solution in demand-side management. The objective function of this problem is to minimize the total operating costs of the power system and the cost of load shifting, and the constraints of the problem include operating constraints and executive restrictions for load shifting. Due to the dimensions of the problem, the simultaneous combination of a genetic algorithm and an ABC is used in such a way that by solving the OPF problem with an ABC algorithm and applying it to the structure of the genetic algorithm, the main problem will be solved. Finally, the proposed method is evaluated under the influence of various factors, including the types of production units, the types of loads, the unit uncertainty, sharing with the grid, and electricity prices all based on different scenarios. To confirm the proposed method, the results were compared with different algorithms on the IEEE 33-bus network, which was able to reduce costs by 57.01%. [ABSTRACT FROM AUTHOR]

The performance of a typical solar energy-based system can be improved by accurately modeling the current versus voltage characteristics of the involved solar cells. However, estimating the exact value of parameters related to solar cells is quite challenging. The optimization function, considering the current–voltage characteristics of solar cells, requires the solution of sophisticated non-linear and multi-modal optimization methods. So far, various optimization approaches have been reported. This paper proposes the application of a new hybrid algorithm, i.e., Particle Swarm Optimization and Gravitational Search Algorithm (PSOGSA), which is a combination of two algorithms, i.e., Gravitational Search Algorithm (GSA) and Particle Swarm Optimization (PSO) method. The hybrid PSOGSA algorithm is superior to other algorithms in terms of higher accuracy in searching for optimal solutions and better explorative capability. Moreover, the developed hybrid algorithm is benchmarked using ten standard test functions to verify its efficiency. In this manuscript, monocrystalline and polycrystalline solar cells are considered. The parameter optimization results are obtained using PSOGSA and further compared with those obtained using other algorithms presented in the literature, such as PSO, GSA, MVO, HBO, PO and SCA. The complete error analysis is carried out for the modified single-diode model (MSDM), the modified double-diode model (MDDM), and the modified three-diode model (MTDM) of photovoltaic (PV) cells to prove the superiority of the PSOGSA. Moreover, statistical results are carried out based on Friedman's ranking and Wilcoxon's rank sum test. The comparison results show that the proposed PSOGSA is better than other algorithms in estimating the unknown PV model parameters. [ABSTRACT FROM AUTHOR]