Search

The integration of renewable energy sources into isolated microgrids introduces significant power fluctuations due to their intermittent nature. This study addresses the need for advanced power smoothing methods to enhance the stability of isolated networks. An innovative adaptive strategy is presented, combining photovoltaic solar generation with vehicle-to-grid technology, utilizing an enhanced adaptive moving average filter with fuzzy logic control. The primary objective is to dynamically optimize the time frame of the Li-ion battery energy storage system for immediate power stabilization, leveraging the high energy density and rapid response capabilities inherent in electric vehicle batteries. The methodology encompasses data acquisition from photovoltaic panels, definition of fuzzy logic control rules, and implementation of the proposed method within a computer-controlled system connected to a bidirectional three-phase inverter. Experimental results highlight the proposed method's superiority over conventional moving averages and ramp-rate filters. [ABSTRACT FROM AUTHOR]

This research focuses on the development and simulation analysis of heat-dissipating fins made of copper, integrated into photovoltaic panels, with the aim of mitigating temperature increases during operation. This initiative arises from evidence that solar panels experience a reduction in energy efficiency when operating at temperatures higher than standard test conditions. The photovoltaic panel was simulated both without fins and with fins under standard test conditions and extreme conditions. The simulation consists of the following steps: design, meshing, selection of physical models and materials, assignment of boundary conditions, validation of the simulation, and interpretation of the results. During validation, results obtained via simulation were compared experimentally, yielding a mean absolute percentage error of 0.28%. It was concluded that the fins with the greatest heat dissipation relative to their area are those of 40 mm height; with this height, the temperature of the photovoltaic panel is reduced by 2.64 K, which represents an efficiency increase of 1.32%. Furthermore, it was concluded from the analyzed data that the efficiency of the fins increases at high temperatures. [ABSTRACT FROM AUTHOR]

In this paper, experimental results about the performance of a Takagi–Sugeno Fuzzy Controller (TSFC) for an EV3 Ballbot Robotic System (EV3BRS) are reported. The physical configuration of the EV3BRS has the form of an inverted pendulum mounted on a ball. The EV3BRS is an underactuated robotic system with four outputs and two control torques. In this work, following the Takagi–Sugeno (TS) fuzzy control design methodology, the Parallel Distributed Compensation (PDC) approach is used in the design of the TSFC. The EV3BRS's TS Fuzzy Model (TSFM) design comes from linearization of the nonlinear model around two operation points near the upright position of EV3BRS's body. The Linear Matrix Inequality (LMI) approach was used to obtain the feedback gains for every local linear controller, guaranteeing, via a conservative stability condition, the global asymptotic stability of the overall fuzzy control system. The main goal of the control task consists of maintaining the EV3BRS's body at its upright position. Measurement and control data from and to the EV3BRS are transferred via telecontrol and telemetry. The appropriate performance of the controller design is corroborated via experimentation. [ABSTRACT FROM AUTHOR]

Effective thermal management is essential for ensuring the safety, performance, and longevity of lithium-ion batteries across diverse applications, from electric vehicles to energy storage systems. This paper presents a thorough review of thermal management strategies, emphasizing recent advancements and future prospects. The analysis begins with an evaluation of industry-standard practices and their limitations, followed by a detailed examination of single-phase and multi-phase cooling approaches. Successful implementations and challenges are discussed through relevant examples. The exploration extends to innovative materials and structures that augment thermal efficiency, along with advanced sensors and thermal control systems for real-time monitoring. The paper addresses strategies for mitigating the risks of overheating and propagation. Furthermore, it highlights the significance of advanced models and numerical simulations in comprehending long-term thermal degradation. The integration of machine learning algorithms is explored to enhance precision in detecting and predicting thermal issues. The review concludes with an analysis of challenges and solutions in thermal management under extreme conditions, including ultra-fast charging and low temperatures. In summary, this comprehensive review offers insights into current and future strategies for lithium-ion battery thermal management, with a dedicated focus on improving the safety, performance, and durability of these vital energy sources. [ABSTRACT FROM AUTHOR]

This paper provides a thorough exploration of the evolution and contemporary trends in electrical-distribution networks, with a focus on smart grids in the context of Industry 4.0. Beginning with the traditional components of electrical grids, the study highlights the transition towards sustainable energy sources and the integration of renewables. Key trends include economic operation, the application of distributed energy resources, and the significance of photovoltaic solar energy. The paper unfolds in seven sections, examining smart-electrical-network architecture, sustainable technology progression, energy efficiency, carbon-emission-reduction challenges, future perspectives, and concluding insights. Each section delves into specific layers and aspects, such as data management, electrical infrastructure, automation, and consumer interaction. The intricate role of smart meters and their impact on energy management is explored, providing a comprehensive overview of the current state and future directions of electrical-distribution networks. [ABSTRACT FROM AUTHOR]

The efficiency and dynamics of hybrid electric vehicles are inherently linked to effective energy management strategies. However, complexity is heightened due to uncertainty and variations in real driving conditions. This article introduces an innovative strategy for extended-range electric vehicles, grounded in the optimization of driving cycles, prediction of driving conditions, and predictive control through neural networks. First, the challenges of the energy management system are addressed by merging deep reinforcement learning with strongly convex objective optimization, giving rise to a pioneering method called DQL-AMSGrad. Subsequently, the DQL algorithm has been implemented, allowing temporal difference-based updates to adjust Q values to maximize the expected cumulative reward. The loss function is calculated as the mean squared error between the current estimate and the calculated target. The AMSGrad optimization method has been applied to efficiently adjust the weights of the artificial neural network. Hyperparameters such as the learning rate and discount factor have been tuned using data collected during real-world driving tests. This strategy tackles the "curse of dimensionality" and demonstrates a 30% improvement in adaptability to changing environmental conditions. With a 20%-faster convergence speed and a 15%-superior effectiveness in updating neural network weights compared to conventional approaches, it also highlights an 18% reduction in fuel consumption in a case study with the Nissan Xtrail e-POWER system, validating its practical applicability. [ABSTRACT FROM AUTHOR]

The reliance on imported fuels for electricity generation and internal transportation in insular electrical systems has historically posed a significant challenge due to their geographic isolation. The vulnerability of insular ecosystems to pollution has driven the need to transition toward renewable energy sources. Despite their inherent variability, wind and solar energy have gained acceptance. Integrating these renewable technologies into insular grids presents technical challenges that impact the quality of the power supply, particularly with the increasing presence of electric vehicles. Nevertheless, the batteries of these vehicles provide an opportunity to enhance network performance. This article introduces an innovative power smoothing technique that utilizes electric vehicle batteries to optimize self-consumption and reduce power fluctuations. The proposed method is an enhanced version of the ramp-rate energy smoothing method, incorporating adaptability through real-time control of the ramp-rate using fuzzy logic. It employs an aggregated model of lithium-ion batteries with a bidirectional power electronic converter. Experimental validation is carried out in the Micro-Grid Laboratory of the University of Cuenca, Ecuador. Experimental results demonstrate a significant 14% reduction in energy generation variability, resulting in a more stable electrical supply profile. Additionally, there is a marginal improvement in energy delivery, with an additional injection of 0.23 kWh compared to scenarios without the participation of electric vehicle batteries in power smoothing tasks. These findings support the effectiveness of the proposed approach in optimizing the integration of intermittent renewable generators and electric vehicle charging in insular energy systems. [ABSTRACT FROM AUTHOR]

The integration of renewable resources in isolated systems can produce instability in the electrical grid due to its intermintency. In today's microgrids, which lack synchronous generation, physical inertia is substituted for inertia emulation. To date, the most effective approach remains the frequency derivative control technique. Nevertheless, within this method, the ability to provide virtual drooping is often disregarded in its design, potentially leading to inadequate development in systems featuring high renewable penetration and low damping. To address this issue, this paper introduces an innovative design and analysis of virtual inertia control to simultaneously mimic droop and inertia characteristics in microgrids. The dynamic frequency response without and with renewable energy sources penetration is comparatively analyzed by simulation. The proposed virtual inertia control employs a derivative technique to measure the rate of change of frequency slope during inertia emulation. Sensitivity mapping is conducted to scrutinize its impact on dynamic frequency response. Finally, the physical battery storage system of the University of Cuenca microgrid is used as a case study under operating conditions. [ABSTRACT FROM AUTHOR]

Burgers' equation is used to describe wave phenomena in hydrodynamics and acoustics. It was derived originally as a prototype to provide analytic insight into the nature of turbulence and its modeling, and has found applications in the study of shock waves and wave transmission. Burgers' equation is not globally controllable, and under certain conditions it can be neutrally stable. In this study, we explore the adaptive backstepping boundary control (BBC) methodology on a modified Burgers' equation with unknown parameters, but constant, for the reactive and convective (nonlinear) terms, with Robin and Neumann boundary conditions (BCs), where this latter BC is actuated by the control signal. The nominal controller is designed from a linear partial differential equation (PDE), and under the assumption that this nominal controller also achieves stabilization for the modified Burgers' equation, then its adaptive version is proposed for the control of such nonlinear PDE systems. Simulation results show convergence near the ideal values for the parametric estimates while the estimation error converges to zero. [ABSTRACT FROM AUTHOR]

Ascochyta blight, caused by Ascochyta fabae, poses a significant threat to faba bean and other legumes worldwide. Necrotic lesions on stems, leaves, and pods characterize the disease. Given the economic impact of this pathogen and the potential involvement of secondary metabolites in symptom development, a study was conducted to investigate the fungus's ability to produce bioactive metabolites that might contribute to its pathogenicity. For this investigation, the fungus was cultured in three substrates (Czapek-Dox, PDB, and rice). The produced metabolites were analyzed by NMR and LC-HRMS methods, resulting in the dereplication of seven metabolites, which varied with the cultural substrates. Ascochlorin, ascofuranol, and (R)-mevalonolactone were isolated from the Czapek-Dox extract; ascosalipyrone, benzoic acid, and tyrosol from the PDB extract; and ascosalitoxin and ascosalipyrone from the rice extract. The phytotoxicity of the pure metabolites was assessed at different concentrations on their primary hosts and related legumes. The fungal exudates displayed varying degrees of phytotoxicity, with the Czapek-Dox medium's exudate exhibiting the highest activity across almost all legumes tested. The species belonging to the genus Vicia spp. were the most susceptible, with faba bean being susceptible to all metabolites, at least at the highest concentration tested, as expected. In particular, ascosalitoxin and benzoic acid were the most phytotoxic in the tested condition and, as a consequence, expected to play an important role on necrosis's appearance. [ABSTRACT FROM AUTHOR]

This study presents a 100% renewable and diversified system taking advantage of the available energy potential of renewable energies in Mexico with a view to a planned energy transition in cooperation with the environment. The processes of change that are experienced worldwide in favor of the planet make us reflect and propose alternatives that break traditional schemes in the production of energy (for which reason Mexico cannot deviate from its current model). It is here that this research becomes a transcendental and important reference for decision-making and the transformation of the energy sector in Mexico. The current electrical system relies on fossil fuels that need to be replaced by renewable energy sources (and it is necessary to satisfy growing demands in the long term). The methodological process is carried out with the use of the 100% renewable energy market design tool EnergyPLAN, which puts the concept of intelligent energy into practice by 2050. Finally, after analyzing the results, it is concluded that a good energy mix for 2050 is 30% solar photovoltaic, 25% wind, 14.5% hydraulic, 13.8% CSP plants, and 16.7% other technologies. Surpluses may be sold to the United States and Central America through interconnection points. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) and renewable energy resources (RERs) are widely integrated into electrical systems to reduce dependency on fossil fuels and emissions. The energy management of microgrids (MGs) is a challenging task due to uncertainty about EVs and RERs. In this regard, an improved version of the RUNge Kutta optimizer (RUN) was developed to solve the energy management of MGs and assign the optimal charging powers of the EVs for reducing the operating cost. The improved RUN optimizer is based on two improved strategies: Weibull flight distribution (WFD) and a fitness–distance balance selection (FDB) strategy, which are applied to the conventional RUN optimizer to improve its performance and searching ability. In this paper, the energy management of MGs is solved both at a deterministic level (i.e., without considering the uncertainties of the system) and while considering the uncertainties of the system, with and without a smart charging strategy for EVs. The studied MG consists of two diesel generators, two wind turbines (WTs), three fuel cells (FCs), an electrical vehicle charging station and interconnected loads. The obtained results reveal that the proposed algorithm is efficient for solving the EM of the MG compared to the other algorithms. In addition, the operating cost is reduced with the optimal charging strategy. [ABSTRACT FROM AUTHOR]

As the world's population continues to grow and the demand for energy increases, there is an urgent need for sustainable and efficient energy systems. Renewable energy sources, such as wind and solar power, have the potential to play a significant role in meeting this demand, but their intermittency can make integration into existing energy systems a challenge. Moreover, the development of sustainable energy systems has become even more critical in recent years, due to a confluence of events, including the decline in fuel prices, geopolitical conflicts, and the recent COVID-19 pandemic. The decrease in fuel prices has led to a decline in investment in renewable energy and has slowed the transition to sustainable energy systems. Additionally, geopolitical conflicts and pandemics have highlighted the need for resilient and self-sufficient energy systems that can operate independently of external factors. Also, energy storage technologies play a critical role in achieving this goal by providing reliable backup power and enabling microgrids to operate independently of the larger power grid. As such, developing efficient and effective energy storage technologies is essential for creating sustainable energy systems that can meet the demands of modern society while mitigating the impact of external factors. In this regard, this work provides an overview of microgrids' latest energy storage technologies, including their applications, types, integration strategies, optimization algorithms, software, and uncertainty analysis. Energy storage technologies have a wide range of applications in microgrids, including providing backup power and balancing the supply and demand of energy. Different energy storage techniques have been discussed, including batteries, flywheels, supercapacitors, pumped hydro energy storage, and others. Moreover, integration strategies of energy storage in microgrids, models, assessment indices, and optimization algorithms used in the design of energy storage systems are presented in detail. The capabilities of software used in energy storage sizing are explored. Further, uncertainty analysis in modeling energy storage devices is presented and discussed. This state-of-the-art technology has been prepared to demonstrate the effectiveness of energy storage technologies in microgrids, providing valuable insights for future developments in the field. [ABSTRACT FROM AUTHOR]

This article aimed to introduce a novel application of a hybrid particle swarm optimizer and gravitational search algorithm (HPSOGSA) that can be used for optimal control of offshore wind farms' voltage source converter connected to HVDC transmission lines. Specifically, the algorithm was used to design fractional-order proportional-integral-derivative (FOPID) controller parameters designed to minimize the system's objective function based on an integral squared error. The proposed FOPID controller was applied to improve offshore wind farm performance under different transient conditions, and its results were compared with a PI controller that was designed using a genetic algorithm and grey wolf optimization algorithm. The fault ride-through capabilities of the proposed control strategy were also evaluated. The findings suggest that the HPSOGSA-based FOPID controller outperformed the other two methods, significantly enhancing offshore wind farm operations. The control strategy was thoroughly tested using MATLAB/Simulink under various operating scenarios. [ABSTRACT FROM AUTHOR]

Modeling the photovoltaic (PV) generating unit is one of the most important and crucial tasks when assessing the accurate performance of the PV system in power systems. The modeling of the PV system refers to the assigning of the optimal parameters of the PV's equivalent circuit. Identifying these parameters is considered to be a complex optimization problem, especially with the deviation of the solar irradiance and the ambient temperature. In this regard, this paper proposes a novel hybrid multi-population gorilla troops optimizer and beluga whale optimization (HMGTO-BWO) model to evaluate the optimal parameters of the PV cell/panel; it is based on a multi-population strategy to improve its diversity and to avoid the stagnation of the conventional GTO. The BWO explorative and exploitative powers, which are based on synchronized motion and Lévy flight, are used. The suggested HGTO-BWO is implemented to minimize the root mean square error (RMSE) between the simulated and measured data for each cell/panel represented by a double diode model (DDM) and triple diode model (TDM). The proposed HGTO-BWO is investigated according to the standard and CEC-2019 benchmark functions, and the obtained results are compared with seven other optimization techniques in terms of statistical analysis, convergence characteristics, boxplots, and the Wilcoxon rank sum test. The minimum obtained RMSE values of the PVW 752 cell were 2.0886 × 10−4 and 1.527 × 10−4 for the DDM and TDM, respectively. Furthermore, the minimum fetched fitness value for the STM6-40/36 modules was 1.8032 × 10−3. The obtained results proved the effectiveness and preference of the suggested HGTO-BWO in estimating the parameters of the PV modules. [ABSTRACT FROM AUTHOR]

This paper establishes an accurate and reliable study for estimating the lithium-ion battery's State of Charge (SoC). An accurate state space model is used to determine the parameters of the battery's nonlinear model. African Vultures Optimizers (AVOA) are used to solve the issue of identifying the battery parameters to accurately estimate SoC. A hybrid approach consists of the Coulomb Counting Method (CCM) with an Adaptive Unscented Kalman Filter (AUKF) to estimate the SoC of the battery. At different temperatures, four approaches are applied to the battery, varying between including load and battery fading or not. Numerical simulations are applied to a 2.6 Ahr Panasonic Li-ion battery to demonstrate the hybrid method's effectiveness for the State of Charge estimate. In comparison to existing hybrid approaches, the suggested method is very accurate. Compared to other strategies, the proposed hybrid method achieves the least error of different methods. [ABSTRACT FROM AUTHOR]

Nowadays, energy decarbonization due to integrating renewable energy sources presents important challenges to overcome. The intermittent nature of photovoltaic systems reduces power quality by producing voltage variations and frequency deviations in electrical system networks, especially in weak and isolated distribution systems in developing countries. This paper presents a power smoothing method for improving the low-pass filter and moving average for grid-connected photovoltaic systems. This novel method includes state-of-charge monitoring control of the supercapacitor's energy storage system to reduce the fluctuations of photovoltaic power at the point of common coupling. A case study for a microgrid in a high-altitude city in Ecuador is presented with exhaustive laboratory tests using real data. This research aims to improve energy power quality in electrical distribution systems to cope with the growth of renewable penetration. The results demonstrate significant power quality and stability improvements achieved through the proposed method. For instance, the power smoothing method effectively reduced power fluctuations by 16.7% with the low-pass filter, 14.05% with the ramp-rate filter, and 9.7% with the moving average filter. [ABSTRACT FROM AUTHOR]

Different fungal species belonging to the Colletotrichum genus cause anthracnose disease in a range of major crops, resulting in huge economic losses worldwide. Typical symptoms include dark, sunken lesions on leaves, stems, or fruits. Colletotrichum spp. have synthesized, in vitro, a number of biologically active and structurally unusual metabolites that are involved in their host's infection process. In this study, we applied a one strain many compounds (OSMAC) approach, integrated with targeted and non-targeted metabolomics profiling, to shed light on the secondary phytotoxic metabolite panels produced by pathogenic isolates of Colletotrichum truncatum and Colletotrichum trifolii. The phytotoxicity of the fungal crude extracts was also assessed on their primary hosts and related legumes, and the results correlated with the metabolite profile that arose from the different cultural conditions. To the best of our knowledge, this is the first time that the OSMAC strategy integrated with metabolomics approaches has been applied to Colletotrichum species involved in legume diseases. [ABSTRACT FROM AUTHOR]

This paper establishes a study for an accurate parameter modeling method for lithium-ion batteries. A precise state space model generated from an equivalent electric circuit is used to carry out the proposed identification process, where parameter identification is a nonlinear optimization process problem. The African vultures optimization algorithm (AVOA) is utilized to solve this problem by simulating African vultures' foraging and navigating habits. The AVOA is used to implement this strategy and improve the quality of the solutions. Four scenarios are considered to take the effect of loading, fading, and dynamic analyses. The fitness function is selected as the integral square error between the estimated and measured voltage in these scenarios. Numerical simulations were executed on a 2600 mAhr Panasonic Li-ion battery to demonstrate the effectiveness of the suggested parameter identification technique. The proposed AVOA was fulfilled with high accuracy, the least error, and high closeness with the experimental data compared with different optimization algorithms, such as the Nelder–Mead simplex algorithm, the quasi-Newton algorithm, the Runge Kutta optimizer, the genetic algorithm, the grey wolf optimizer, and the gorilla troops optimizer. The proposed AVOA achieves the lowest fitness function level of the scenarios studied compared with relative optimization algorithms. [ABSTRACT FROM AUTHOR]

In this paper, from linear operator, semigroup and Sturm–Liouville problem theories, an abstract system model for the convection–diffusion (C–D) equation is proposed. The state operator for this abstract system model is here defined as given in the form of the Sturm–Liouville differential operator (SLDO) plus an integral term of the same SLDO. Our aim is to achieve the trajectory tracking task in the presence of external disturbances to the C–D equation invoking the regulator problem theory, where the state from a finite-dimensional exosystem is the state to the feedback law. In this context, the regulator (Francis) equations, established from the abstract system model for the C–D equation, here are solved; i.e., the state feedback regulator problem (SFRP) for the C–D system has a solution. Our proposal is validated via numerical simulation results. [ABSTRACT FROM AUTHOR]

The state of Jordan's energy independence is critical, with the country relying almost entirely on imported oil and gas. Consequently, energy availability is considered to be the most significant challenge faced by Jordan's industrial sectors. Between 2014–2019, the Jordan generated over 12,000 kilo-tons of waste and residue each year. The available quantities of agricultural residues and animal wastes produced in Jordan were approximately 1284.2 kilo-tons/year and 10,909.6 kilotons/year, respectively, of which an estimated 42% could be used as a source of biogas. Two options for utilizing biomass resources have been reviewed in this paper: thermal treatment (direct combustion) and as a source of biogas. The quantity of biogas that can be produced in Jordan from a variety of biomass feedstocks is estimated to be 816.2 million cubic meters (MCM), which is equivalent to a yearly power output of 960.9 GWh, representing approximately 5.1% of the total electricity consumed by Jordan in 2019 (18,853 GWh). Assuming a thermal efficiency of 70%, biogas can generate as much as 4.8 TWh of heat energy. Alternatively, the direct combustion of various biomasses can provide Jordan with 2316.7 GWh of electricity. These findings may lead to the development of a long-term strategic plan for the intelligent utilization of available biomass feedstocks for electrical generation and/or as a source of biogas. This would consequently raise the proportion of sustainable energy derived from biomass in Jordan's energy mix. This work aims to assess the technical, economic, and environmental aspects associated with incorporating biomass resources into Jordan's energy network. [ABSTRACT FROM AUTHOR]

Fungal phytotoxins can be defined as secondary metabolites toxic to host plants and are believed to be involved in the symptoms developed of a number of plant diseases by targeting host cellular machineries or interfering with host immune responses. As any crop, legumes can be affected by a number of fungal diseases, causing severe yield losses worldwide. In this review, we report and discuss the isolation, chemical, and biological characterization of fungal phytotoxins produced by the most important necrotrophic fungi involved in legume diseases. Their possible role in plant–pathogen interaction and structure–toxicity relationship studies have also been reported and discussed. Moreover, multidisciplinary studies on other prominent biological activity conducted on reviewed phytotoxins are described. Finally, we explore the challenges in the identification of new fungal metabolites and their possible applications in future experiments. [ABSTRACT FROM AUTHOR]

For the time being, renewable energy source (RES) penetration has significantly increased in power networks, particularly in microgrids. The overall system inertia is dramatically decreased by replacing traditional synchronous machines with RES. This negatively affects the microgrid dynamics under uncertainties, lowering the microgrid frequency stability, specifically in the islanded mode of operation. Therefore, this work aims to enhance the islanded microgrid frequency resilience using the virtual inertia frequency control concept. Additionally, optimal model predictive control (MPC) is employed in the virtual inertial control model. The optimum design of the MPC is attained using an optimization algorithm, the African Vultures Optimization Algorithm (AVOA). To certify the efficacy of the proposed controller, the AVOA-based MPC is compared with a conventional proportional–integral (PI) controller that is optimally designed using various optimization techniques. The actual data of RES is utilized, and a random load power pattern is applied to achieve practical simulation outcomes. Additionally, the microgrid paradigm contains battery energy storage (BES) units for enhancing the islanded microgrid transient stability. The simulation findings show the effectiveness of AVOA-based MPC in improving the microgrid frequency resilience. Furthermore, the results secure the role of BES in improving transient responses in the time domain simulations. The simulation outcomes are obtained using MATLAB software. [ABSTRACT FROM AUTHOR]

This paper proposes a new, metaheuristic optimization technique, Artificial Gorilla Troops Optimization (GTO), for a hybrid power system with photovoltaic (PV) and wind energy (WE) sources, solving the probabilistic optimum power flow (POPF) issue. First, the selected algorithm is developed and evaluated such that it applies to solve the classical optimum power flow (OPF) approach with the total fuel cost as the objective function. Second, the proposed algorithm is used for solving the POPF, including the PV and WE sources, considering the uncertainty of these renewable energy sources (RESs). The performance of the suggested algorithm was confirmed using the standard test systems IEEE 30-bus and 118-bus. Different scenarios involving different sets of the PV and WE sources and fixed and variable loads were considered in this study. The comparison of the obtained results from the suggested algorithm with other algorithms mentioned in this literature has confirmed the efficiency and performance of the proposed algorithm for providing optimal solutions for a hybrid power system. Furthermore, the results showed that the penetration of the PV and WE sources in the system significantly reduces the total cost of the system. [ABSTRACT FROM AUTHOR]

Since renewable energy sources (RESs) have an intermittent nature, conventional secondary frequency control, i.e., load frequency control (LFC), cannot mitigate the effects of variations in system frequency. Thus, this paper proposes incorporating ultralocal model (ULM) control into LFC to enhance microgrid (µG) frequency stability. ULM controllers are regarded as model-free controllers that yield high rejection rates for disturbances caused by load/RES uncertainties. Typically, ULM parameters are set using trial-and-error methods, which makes it difficult to determine the optimal values that will provide the best system performance and stability. To address this issue, the African vultures optimization algorithm (AVOA) was applied to fine-tune the ULM parameters, thereby stabilizing the system frequency despite different disturbances. The proposed LFC controller was compared with the traditional secondary controller based on an integral controller to prove its superior performance. For several contingencies, the simulation results demonstrated that the proposed controller based on the optimal ULM coupled with LFC could significantly promote RESs into the µG. [ABSTRACT FROM AUTHOR]

The present research produces a new technique for the optimum operation of an isolated microgrid (MGD) based on an enhanced block-sparse adaptive Bayesian algorithm (EBSABA). To update the proportional-integral (PI) controller gains online, the suggested approach considers the impact of the actuating error signal as well as its magnitude. To reach a compromise result between the various purposes, the Response Surface Methodology (RSMT) is combined with the sunflower optimization (SFO) and particle swarm optimization (PSO) algorithms. To demonstrate the success of the novel approach, a benchmark MGD is evaluated in three different Incidents: (1) removing the MGD from the utility (islanding mode); (2) load variations under islanding mode; and (3) a three-phase fault under islanding mode. Extensive simulations are run to test the new technique using the PSCAD/EMTDC program. The validity of the proposed optimizer is demonstrated by comparing its results with those obtained using the least mean and square root of exponential method (LMSRE) based adaptive control, SFO, and PSO methodologies. The study demonstrates the superiority of the proposed EBSABA over the LMSRE, SFO, and PSO approaches in the system's transient reactions. [ABSTRACT FROM AUTHOR]

Storage systems and demand-response programs will play a vital role in future energy systems. Batteries, hydrogen or pumped hydro storage systems can be combined to form hybrid storage facilities to not only manage the intermittent behavior of renewable sources, but also to store surplus renewable energy in a practice known as 'green' storage. On the other hand, demand-response programs are devoted to encouraging a more active participation of consumers by pursuing a more efficient operation of the system. In this context, proper scheduling tools able to coordinate different storage systems and demand-response programs are essential. This paper presents a stochastic mixed-integer-lineal-logical framework for optimal scheduling of isolated microgrids. In contrast to other works, the present model includes a logical-based formulation to explicitly coordinate batteries and pumped hydro storage units. A case study on a benchmark isolated microgrid serves to validate the developed optimization model and analyze the effect of applying demand-response premises in microgrid operation. The results demonstrate the usefulness of the developed method, and it is found that operation cost and fuel consumption can be reduced by ~38% and ~82% by applying demand-response initiatives. [ABSTRACT FROM AUTHOR]

Integrating renewable energy sources (RESs) into modern electric power systems offers various techno-economic benefits. However, the inconsistent power profile of RES influences the power flow of the entire distribution network, so it is crucial to optimize the power flow in order to achieve stable and reliable operation. Therefore, this paper proposes a newly developed circle search algorithm (CSA) for the optimal solution of the probabilistic optimal power flow (OPF). Our research began with the development and evaluation of the proposed CSA. Firstly, we solved the OPF problem to achieve minimum generation fuel costs; this used the classical OPF. Then, the newly developed CSA method was used to deal with the probabilistic power flow problem effectively. The impact of the intermittency of solar and wind energy sources on the total generation costs was investigated. Variations in the system's demands are also considered in the probabilistic OPF problem scenarios. The proposed method was verified by applying it to the IEEE 57-bus and the 118-bus test systems. This study's main contributions are to test the newly developed CSA on the OPF problem to consider stochastic models of the RESs, providing probabilistic modes to represent the RESs. The robustness and efficiency of the proposed CSA in solving the probabilistic OPF problem are evaluated by comparing it with other methods, such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and the hybrid machine learning and transient search algorithm (ML-TSO) under the same parameters. The comparative results showed that the proposed CSA is robust and applicable; as evidence, an observable decrease was obtained in the costs of the conventional generators' operation, due to the penetration of renewable energy sources into the studied networks. [ABSTRACT FROM AUTHOR]

This paper proposes a novel hybrid optimization technique based on a machine learning (ML) approach and transient search optimization (TSO) to solve the optimal power flow problem. First, the study aims at developing and evaluating the proposed hybrid ML-TSO algorithm. To do so, the optimization technique is implemented to solve the classical optimal power flow problem (OPF), with an objective function formulated to minimize the total generation costs. Second, the hybrid ML-TSO is adapted to solve the probabilistic OPF problem by studying the impact of the unavoidable uncertainty of renewable energy sources (solar photovoltaic and wind turbines) and time-varying load profiles on the generation costs. The evaluation of the proposed solution method is examined and validated on IEEE 57-bus and 118-bus standard systems. The simulation results and comparisons confirmed the robustness and applicability of the proposed hybrid ML-TSO algorithm in solving the classical and probabilistic OPF problems. Meanwhile, a significant reduction in the generation costs is attained upon the integration of the solar and wind sources into the investigated power systems. [ABSTRACT FROM AUTHOR]

Worldwide, due to the abrupt growth of population, the load demand has been rising dramatically in the last few years. This led to an increase in branch overloads, voltage deviations, and power losses. These problems may result in line outages or the occurrence of blackouts. Flexible AC transmission system (FACTS) devices can be installed in the power system to ensure increased power flow capability and flexible voltage control to address these issues. In this paper, one of the most used FACTS is utilised. It is called Static VAR Compensator (SVC). This controller is one of the most commonly used shunt FACTS controllers due to its low cost in comparison to others, ease of operation, and integration into the power grid. Two Optimization algorithms are combined to form a hybrid optimization approach: Cuckoo Search (CS) and Antlion Optimization (ALO). This hybrid approach employs the exploration of ALO to adjust the optimum allocation and size for SVCs in the power system. This study proposes the IEEE 57 bus scheme as a fairly large structure, with the 50 and 41 branch outages considered the worst-case scenarios for line outages in this system. The simulation results show that the proposed methodology balances exploring the research space and exploiting the best existing solutions compared to some of the other introduced approaches in the literature. [ABSTRACT FROM AUTHOR]

The forward method for modeling electric vehicles is one of the most suitable for estimating energy consumption in different imposed driving cycles. However, a detailed description of the methodology used for the development of electric vehicle models is necessary and is scarce in the current literature. To fill this gap, this study focuses on highlighting the intrinsic characteristics through a theoretical study with a mathematical model, complemented by demonstrative simulations in Matlab/Simulink. The results show that the forward method can be estimated more accurately based on the energy consumption of the electric vehicle. Moreover, this paper aims to be explicitly descriptive for the development of more complex electric vehicle models to incorporate real driving cycles, being able to size the drivetrain of the vehicle itself or develop ecological routes. [ABSTRACT FROM AUTHOR]

This paper presents a novel metaheuristic optimization algorithm inspired by the geometrical features of circles, called the circle search algorithm (CSA). The circle is the most well-known geometric object, with various features including diameter, center, perimeter, and tangent lines. The ratio between the radius and the tangent line segment is the orthogonal function of the angle opposite to the orthogonal radius. This angle plays an important role in the exploration and exploitation behavior of the CSA. To evaluate the robustness of the CSA in comparison to other algorithms, many independent experiments employing 23 famous functions and 3 real engineering problems were carried out. The statistical results revealed that the CSA succeeded in achieving the minimum fitness values for 21 out of the tested 23 functions, and the p-value was less than 0.05. The results evidence that the CSA converged to the minimum results faster than the comparative algorithms. Furthermore, high-dimensional functions were used to assess the CSA's robustness, with statistical results revealing that the CSA is robust to high-dimensional problems. As a result, the proposed CSA is a promising algorithm that can be used to easily handle a wide range of optimization problems. [ABSTRACT FROM AUTHOR]

Growing demand, interconnection of multiple systems, and difficulty in upgrading existing infrastructures are limiting the capabilities of conventional computational tools employed in power system analysis. Recent studies manifest the importance of efficiently solving well- and ill-conditioned Power-Flow cases in a modern power-system paradigm. While the well-conditioned cases are easily solvable using standard methods, the ill-conditioned ones suppose a challenge for such solvers. In this regard, methods based on the Continuous Newton's principle have demonstrated their ability to address ill-conditioned cases with acceptable efficiency. This paper demonstrates that the approaches proposed so far do not extract the best numerical properties of such solvers. To fill this gap, an optimization framework is proposed by which the parameters involved in the two-stage Runge–Kutta-based solvers are appropriately set, so that the stability and convergence order of the numerical mapping are maximized. By using the developed optimization technique, three solvers with quadratic, cubic, and 4th order of convergence are developed. The new proposals are tested on a variety of large-scale ill-conditioned cases. Results obtained were promising, outperforming other conventional and robust approaches. [ABSTRACT FROM AUTHOR]

Nowadays, the penetration level of renewable energy sources (RESs) has increased dramatically in electrical networks, especially in microgrids. Due to the replacement of conventional synchronous generators by RESs, the inertia of the microgrid is significantly reduced. This has a negative impact on the dynamics and performance of the microgrid in the face of uncertainties, resulting in a weakening of microgrid stability, especially in an islanded operation. Hence, this paper focuses on enhancing the dynamic security of an islanded microgrid using a frequency control concept based on virtual inertia control. The control in the virtual inertia control loop was based on a proportional-integral (PI) controller optimally designed by the Manta Ray Foraging Optimization (MRFO) algorithm. The performance of the MRFO-based PI controller was investigated considering various operating conditions and compared with that of other evolutionary optimization algorithm-based PI controllers. To achieve realistic simulations conditions, actual wind data and solar power data were used, and random load fluctuations were implemented. The results show that the MRFO-based PI controller has a superior performance in frequency disturbance alleviation and reference frequency tracking compared with the other considered optimization techniques. [ABSTRACT FROM AUTHOR]

Bioenergy is one of the most important renewable-energy sources worldwide, accounting for more than two-thirds of the renewable-energy mix. Biomass accounted for 13–14% of the primary energy consumption in 2018, and by 2050, it is expected to account for 50% of the global primary energy consumption. This article studies the biomass potential in Iraq. The potential of this country to be one of the leading producers of bioenergy is discussed, remarking on the importance of agricultural crop waste. Nowadays, Iraq generates a great quantity of biomass every year. Unfortunately, instead of contributing to the energy industry and economic progress, these wastes are burned directly, potentially causing a slew of environmental issues. Based on earlier studies, the theoretical energy potential of Iraq agricultural wastes is assessed. It is concluded that 10 million tons of dry agricultural leftovers can create 115 PJ of energy per year. According to the findings of this study, 10 million heads of cattle in Iraq could generate 72 million m3 of biogas per day, with a total potential power of 946 TJ per year from animal wastes, mainly cattle dung. On the other hand, bioenergy potential is heavily reliant on the geographical distribution, availability, and accessibility of real waste. Wasit, Qadisiyah, and Mosul are the most feasible locations for this agricultural waste potential. This might lead to the development of a long-term economic plan for the successful and sustainable utilization of important accessible waste for bioenergy generation. [ABSTRACT FROM AUTHOR]

Renewable sources on islands seem to be the most attractive option to decarbonize and lower the price of electricity; currently, most islands do so by replacing their diesel generators with wind or solar sources, along with energy storage. The Galapagos Islands are no exception. This study presents a techno-economic analysis of hybrid renewable systems in the Galapagos Islands, considering the repowering of its renewable sources and reduction in the penetration of diesel generators. This study uses EnergyPlan software, where the best option is chosen based on technical, economic, and environmental indicators. Finally, several sensitivity analyses are done. The results show that by increasing the capacity of current wind and photovoltaic systems, the total annual cost reduces by 20% and 10.31%, respectively; this is a specific result of this study. Moreover, there is a reduction in CO2 emissions produced by diesel generators, up to 38.96%. [ABSTRACT FROM AUTHOR]

The power fluctuations produced by electric vehicles represent a drawback in large-scale residential applications. In addition to that, short power peaks could pose a risk to the stability of the electrical grid. For this reason, this study presents a feasibility analysis for a residential system composed of electric vehicle chargers. The objective is focused on smoothing the power fluctuations produced by the charge by a supercapacitor through adequate energy control; in addition, selfconsumption is analyzed. Data sampling intervals are also analyzed; the modeling was performed in Matlab software. The results show that there are errors of up to 9% if the data are measured at different sampling intervals. On the other hand, if the supercapacitor is considered, the system saves 59.87% of the energy purchased from the utility grid per day, and the self-consumption of electricity by prosumers can increase up to 73%. Finally, the hydrokinetic/supercapacitor/grid system would save up to 489.1 USD/year in the cost of purchasing electricity from the grid and would increase by 492.75 USD/year for the sale electricity. [ABSTRACT FROM AUTHOR]

This paper presents an analysis and optimization of an isolated hybrid renewable power system to operate in the Alrashda village in the Dakhla Oasis, which is situated in the New Valley Governorate in Egypt. The proposed hybrid system is designed to integrate a biomass system with a photovoltaic (PV), wind turbine (WT) and battery storage system (Bat). Four different cases are proposed and compared for analyzing and optimizing. The first case is a configuration of PV and WT with a biomass system and battery bank. The second case is the integration of PV with a biomass system and battery bank. The third case is WT integrated with biomass and a battery bank, and the fourth case is a conventional PV, WT, and battery bank as the main storage unit. The optimization is designed to reduce component oversizing and ensure the dependable control of power supplies with the objective function of reducing the levelized cost of energy and loss of power supply probability. Four optimization algorithms, namely Heap-based optimizer (HBO), Franklin's and Coulomb's algorithm (CFA), the Sooty Tern Optimization Algorithm (STOA), and Grey Wolf Optimizer (GWO) are utilized and compared with each other to ensure that all load demand is met at the lowest energy cost (COE) for the proposed hybrid system. The obtained results revealed that the HBO has achieved the best optimal solution for the suggested hybrid system for case one and two, with the minimum COE 0.121171 and 0.1311804 $/kWh, respectively, and with net present cost ( NPC ) of $3,559,143 and $3,853,160, respectively. Conversely, STOA has achieved the best optimal solution for case three and four, with a COE of 0.105673 and 0.332497 $/kWh, and an NPC of $3,103,938 and $9,766,441, respectively. [ABSTRACT FROM AUTHOR]

The paper proposes a real-time model for electric vehicles (EVs) controlled load charging. The proposed demand-side management (DSM) of EVs is implemented based on queuing analysis with a nonhomogeneous arrival rate and charging service periods dataset. An electric vehicle model is used which is based on a statistical survey to represent the uncontrolled demand of the EVs. A probability distribution for the time at which EVs are plugged and the corresponding value of the state of charges (SOCs) are considered. The preferences of individual EVs have been fully exploited through a set of instructions to fulfill the needs of the vehicles' owners. The designated preferences include the owner setting for both, charging price preferences (OPR), and the maximum estimated parking time duration (EPTD). The quasi-static time-series (QSTS) simulation is used to simulate real-time scenarios of the 24-h simulation period. The IEEE 123 nodes radial test feeder is analyzed with different daily load curves, EV charging scenarios, and wind power penetrations. The results show the effectiveness of the proposed DSM in avoiding excessive levels of charging with/without penetration of non-dispatchable wind power generation. The proposed DSM enables the EVs to charge with low tariff rates either at excessive renewable power generation or late evening hours with available committed bulk power plants and light loading conditions. [ABSTRACT FROM AUTHOR]

Featured Application: This work proposes a common framework for developing robust Power-Flow solvers that may find multiple applications in power system analysis tools. This paper presents a novel Power-Flow solution paradigm based on the structure of the members of the Runge–Kutta family. Solution approaches based on the introduced solution paradigm are intrinsically robust and can achieve high-order convergences rates. It is demonstrated that some well-known Power-Flow solution methods are in fact special cases of the developed framework. Explicit and embedded formulations are discussed, and two novel solution methodologies based on the Explicit Heun and Embedded Heun–Euler's methods are developed. The introduced solution techniques are validated in the EU PEGASE systems, considering different starting points and loading levels. Results show that the developed methods are quite reliable and efficient, outperforming other robust and standard methodologies. On the basis of the results obtained, we can affirm that the introduced solution paradigm constitutes a promising framework for developing novel Power-Flow solution techniques. [ABSTRACT FROM AUTHOR]

Featured Application: Agrifood industries may benefit from investing in energy efficiency and diversifying into new business models (distributed cogeneration and biochar production) through waste gasification, while promoting circular economy and environmental sustainability. This research work presents a techno-economic analysis of a biomass gasification plant fueled with residues from the olive oil and almond industries for combined heat and power generation in the agrifood sector. The experimental plant consists of a downdraft fixed bed gasifier, a producer gas cleaning and cooling system and a spark-ignition engine–generator set as a power generation unit, which generates about 10–12 kW of rated electric power. With an average consumption between 13–14 kg/h of exhausted olive pomace pellets as feedstock, the producer gas volumetric flow rate was 31 Nm3/h (vol. %: 19.2 H2, 12.9 CO, 1.9 CH4, 19.2 CO2, 46.7 N2). The average cold gas efficiency was nearly 63%. This work also addresses the characterization and potential application of the carbonaceous solid residue (biochar), discharged from the gasifier at 1.7 kg/h. Finally, an economic feasibility analysis was developed, wherein the payback period ranges between 5–9 years. [ABSTRACT FROM AUTHOR]

Recently, fast uptake of renewable energy sources (RES) in the world has introduced new difficulties and challenges; one of the most important challenges is providing economic energy with high efficiency and good quality. To reach this goal, many traditional and smart algorithms have been proposed and demonstrated their feasibility in obtaining the optimal solution. Therefore, this paper introduces an improved version of Bonobo Optimizer (BO) based on a quasi-oppositional method to solve the problem of designing a hybrid microgrid system including RES (photovoltaic (PV) panels, wind turbines (WT), and batteries) with diesel generators. A comparison between traditional BO, the Quasi-Oppositional BO (QOBO), and other optimization techniques called Harris Hawks Optimization (HHO), Artificial Electric Field Algorithm (AEFA) and Invasive Weed Optimization (IWO) is carried out to check the efficiency of the proposed QOBO. The QOBO is applied to a stand-alone hybrid microgrid system located in Aswan, Egypt. The results show the effectiveness of the QOBO algorithm to solve the optimal economic design problem for hybrid microgrid power systems. [ABSTRACT FROM AUTHOR]

This paper presents an improved multi-objective probabilistic Reactive Power Planning (RPP) in power systems considering uncertainties of load demand and wind power generation. The proposed method is capable of simultaneously (1) reducing the reactive power investment cost, (2) minimizing the total active power losses, (3) improving the voltage stability, and (4) enhancing the loadability factor. The generators' voltage magnitude, the transformer's tap settings, and the output reactive power of VAR sources are taken into account as the control variables. To solve the probabilistic multi-objective RPP problem, the ε-constraint method is used. To test the effectiveness of the proposed approach, the IEEE 30-bus test system is implemented in the GAMS environment under five different conditions. Finally, for a better comprehension of the obtained results, a brief comparison of outcomes is presented. [ABSTRACT FROM AUTHOR]

Feeding biomass-fueled gas engines (BFGEs) with olive tree pruning residues offers new opportunities to decrease fossil fuel use in road vehicles and electricity generation. BFGEs, coupled to radial distribution systems (RDSs), provide renewable energy and power that can feed electric vehicle (EV) charging stations. However, the combined impact of BFGEs and EVs on RDSs must be assessed to assure the technical constraint fulfilment. Because of the stochastic nature of source/load, it was decided that a probabilistic approach was the most viable option for this assessment. Consequently, this research developed an analytical technique to evaluate the technical constraint fulfilment in RDSs with this combined interaction. The proposed analytical technique (PAT) involved the calculation of cumulants and the linearization of load-flow equations, along with the application of the cumulant method, and Cornish-Fisher expansion. The uncertainties related to biomass stock and its heating value (HV) were important factors that were assessed for the first time. Application of the PAT in a Spanish RDS with BFGEs and EVs confirmed the feasibility of the proposal and its additional benefits. Specifically, BFGEs were found to clearly contribute to the voltage constraint fulfilment. The computational cost of the PAT was lower than that associated with Monte-Carlo simulations (MCSs). [ABSTRACT FROM AUTHOR]

In Spain, the COVID-19 pandemic has impacted the various regions of the country differently. The availability of reliable and up-to-date information has proved to be fundamental for the management of this health crisis. However, especially during the first wave of the pandemic (February–August 2020), the disparity in the recording criteria and in the timing of providing these figures to the central government created controversy and confusion regarding the real dimension of the pandemic. It is therefore necessary to have objective and homogeneous criteria at the national level to guide health managers in the correct recording and evaluation of the magnitude of the pandemic. Within this context, we propose using Benford's Law as an auditing tool to monitor the reliability of the number of daily COVID-related deaths to identify possible deviations from the expected trend. [ABSTRACT FROM AUTHOR]

In recent studies, the competitiveness of the Newton-S-Iteration-Process (Newton-SIP) techniques to efficiently solve the Power Flow (PF) problems in both well and ill-conditioned systems has been highlighted, concluding that these methods may be suitable for industrial applications. This paper aims to tackle some of the open topics brought for this kind of techniques. Different PF techniques are proposed based on the most recently developed Newton-SIP methods. In addition, convergence analysis and a comparative study of four different Newton-SIP methods PF techniques are presented. To check the features of considered PF techniques, several numerical experiments are carried out. Results show that the considered Newton-SIP techniques can achieve up to an eighth order of convergence and typically are more efficient and robust than the Newton–Raphson (NR) technique. Finally, it is shown that the overall performance of the considered PF techniques is strongly influenced by the values of parameters involved in the iterative procedure. [ABSTRACT FROM AUTHOR]

With the development of electronic infrastructures and communication technologies and protocols, electric grids have evolved towards the concept of Smart Grids, which enable the communication of the different agents involved in their operation, thus notably increasing their efficiency. In this context, microgrids and nanogrids have emerged as invaluable frameworks for optimal integration of renewable sources, electric mobility, energy storage facilities and demand response programs. This paper discusses a DC isolated nanogrid layout for the integration of renewable generators, battery energy storage, demand response activities and electric vehicle charging infrastructures. Moreover, a stochastic optimal scheduling tool is developed for the studied nanogrid, suitable for operators integrated into local service entities along with the energy retailer. A stochastic model is developed for fast charging stations in particular. A case study serves to validate the developed tool and analyze the economical and operational implications of demand response programs and charging infrastructures. Results evidence the importance of demand response initiatives in the economic profit of the retailer. [ABSTRACT FROM AUTHOR]

In this work, a neural super-twisting algorithm is applied to the design of a controller for a flywheel energy storage system (FESS) emulator. Emulation of the FESS is achieved through driving a Permanent Magnet Synchronous Machine (PMSM) coupled to a shaft to shaft DC-motor. The emulation of the FESS is carried out by controlling the velocity of the PMSM in the energy storage stag and then by controlling the DC-motor velocity in the energy feedback stage, where the plant's states of both electrical machines are identified via a neural network. For the neural identification, a Recurrent Wavelet First-Order Neural Network (RWFONN) is proposed. For the design of the velocity controller, a super-twisting algorithm is applied by using a sliding surface as the argument; the latter is designed based on the states of the RWFONN, in combination with the block control linearization technique to the control of the angular velocity from both machines in their respective operation stage. The RWFONN is trained online using the filtered error algorithm. Closed-loop stability analysis is included when assuming boundedness of the synaptic weights. The results obtained from Matlab/Simulink validate the performance of the proposal in the control of an FESS. [ABSTRACT FROM AUTHOR]