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Renewable-based distributed generation (DG) offers economic and environmental benefits, but integrating it into the distribution network (DN) poses technical challenges. Hosting capacity (HC) defines the maximum amount of DG that can be safely integrated into the DN without violating technical constraints, with various methods available to enhance the DN and increase HC. This article proposes an optimization-based demand response (DR) program using dynamic pricing to enhance the HC. A hierarchical bi-level optimization framework is developed for the DR program, where the upper level optimizes the hosting capacity depending on the optimal load profiles optimized in the lower level. Additionally, DR is combined with smart inverters’ (SIs) optimal Volt/VAR control to further improve the HC. The pelican optimization algorithm (POA) is employed to solve the optimization problem and is compared with four other optimization techniques, demonstrating its good performance. The proposed planning scheme is implemented on the IEEE 33-bus test system and a real DN consisting of 59 buses in Cairo, Egypt. The results show that applying DR alone can increase the average maximum HC value by 29.4%, while combining DR with optimal Volt/VAR control can achieve a 39.6% increase.

Abstract The increasing adoption of electric vehicles (EVs) presents both opportunities and challenges for power networks. While EVs have the potential to reduce carbon emissions, accommodating their growing power demand requires careful planning to prevent overloading and mitigate environmental impacts. This paper introduces an integrated hosting capacity model to facilitate higher EV penetration while maintaining environmental standards. In addition to EV charging stations, the model incorporates transmission lines, reactive power compensators, energy storage systems, and thyristor-controlled series compensators to ensure a reliable power supply. The model aims to maximize EV charging station deployment, minimize greenhouse gas emissions, and optimize net present value through hosting capacity strategies. Three hosting capacity plans are proposed to analyze the impact of prioritizing one of these objectives over the others in network configurations. Accurate EV demand forecasting is critical for this model, and a swarm intelligence forecasting algorithm is proposed to explore various forecasting approaches. The model is complex and involves nonlinear multi-objective optimization. To solve it, a new hybrid optimization algorithm is introduced, combining the features of the Marine Predators Algorithm and the Honey Badger Algorithm. Three hybridization schemes—Series Hybrid Scheme, Population Division Scheme, and Switching Strategy Scheme—are developed to address the optimization challenges effectively. The results show that the first and second hybridization schemes are the most effective for solving the EV load forecasting models, with a robustness of at least 90%. In contrast, the robustness of the third scheme reaches only 30% in some models. Simulation studies on the IEEE 9-bus network and the IEEE 30-bus system validate the model’s effectiveness in integrating EVs while achieving environmental sustainability objectives. The findings show the superiority of the proposed hybrid schemes in solving the hosting capacity model in terms of finding optimal solutions. However, the third scheme required less computing time than the others, with its convergence time being at least 33.3% shorter.

Abstract Within the scope of sustainable development, integrating electric vehicles (EVs) and renewable energy sources (RESs) into power grids offers a number of benefits. These include reducing greenhouse gas emissions, diversifying energy sources, and promoting the use of green energy. Although the literature on hosting capacity (HC) models has grown, there is still a noticeable gap in the discussion of models that successfully handle transmission expansion planning (TEP), demand response (DR), and HC objectives simultaneously. Combining TEP, DR, and HC objectives in one model optimizes resource use, enhances grid stability, supports renewable and EV integration, and aligns with regulatory and market demands, resulting in a more efficient, reliable, and sustainable power system. This research presents an innovative two-layer HC model, including considerations for TEP and DR. The model determines the highest degree of load shifting appropriate for incorporation into power networks in the first layer. Meanwhile, the second layer focuses on augmenting the RES and EVs’ hosting capability and modernizing the network infrastructure. System operators can choose the best scenario to increase the penetration level of EVs and RESs with the aid of the proposed model. The proposed model, which is formulated as a multi-objective mixed-integer nonlinear optimization problem, uses a hierarchical optimization technique to identify effective solutions by combining the particle swarm optimization algorithm and the crayfish optimizer. When compared to traditional methods, the results obtained from implementing the proposed hierarchical optimization algorithm on the Garver network and the IEEE 24-bus system indicated how effective it is at solving the presented HC model. The case studies demonstrated that integrating DR into the HC problem reduced peak load by 10.4–23.25%. The findings also highlighted that DR did not impact the total energy consumed by EVs throughout the day, but it did reshape the timing of EV charging, creating more opportunities for integration during periods of high demand. Implementing DR reduced the number of projects needed and, in some cases, led to cost savings of up to 12.3%.

Abstract Transmission expansion planning (TEP) is a vital process of ensuring power systems' reliable and efficient operation. The optimization of TEP is a complex challenge, necessitating the application of mathematical programming techniques and meta-heuristics. However, selecting the right optimization algorithm is crucial, as each algorithm has its strengths and limitations. Therefore, testing new optimization algorithms is essential to enhance the toolbox of methods. This paper presents a comprehensive study on the application of ten recent meta-heuristic algorithms for solving the TEP problem across three distinct power networks varying in scale. The ten meta-heuristic algorithms considered in this study include Sinh Cosh Optimizer, Walrus Optimizer, Snow Geese Algorithm, Triangulation Topology Aggregation Optimizer, Electric Eel Foraging Optimization, Kepler Optimization Algorithm (KOA), Dung Beetle Optimizer, Sea-Horse Optimizer, Special Relativity Search, and White Shark Optimizer (WSO). Three TEP models incorporating fault current limiters and thyristor-controlled series compensation devices are utilized to evaluate the performance of the meta-heuristic algorithms, each representing a different scale and complexity level. Factors such as convergence speed, solution quality, and scalability are considered in evaluating the algorithms’ performance. The results demonstrated that KOA achieved the best performance across all tested systems in terms of solution quality. KOA’s average value was 6.8% lower than the second-best algorithm in some case studies. Additionally, the results indicated that WSO required approximately 2–3 times less time than the other algorithms. However, despite WSO’s rapid convergence, its average solution value was comparatively higher than that of some other algorithms. In TEP, prioritizing solution quality is paramount over algorithm speed.

As electric vehicles (EVs) become more commonplace, the development and deployment of advanced battery thermal management (BTM) technologies are vital for increasing the sturdiness of EV batteries, ultimately contributing to the sustainable and massive adoption of electric mobility. This study comprehensively evaluates new advancements in BTM systems for EVs, supplemented with a comparative evaluation of various BTM technologies, including active and passive cooling strategies, structure design, and advanced control algorithms, including deep learning methods. The study also scrutinizes the software’s capabilities employed for designing BTM systems. The cross-relevant papers related to BTM systems from 2019 to early 2024, which rely on Scopus and Web of Science databases, are considered. The comparative evaluation explores the strengths and obstacles of different BTM processes, shedding light on their efficacy under varying operational conditions. Additionally, this study discusses the impact of BTM on overall EV efficiency from the perspective of thermal considerations. Insights into current research trends, innovations, and emerging trends in the field are also presented. Ultimately, this state-of-the-art study aims to thoroughly understand the latest BTM for EVs. The findings offer insightful information for scientists, engineers, and professionals pursuing sustainable transportation development and the continuous enhancement of EV technology.

Peer-to-peer (P2P) energy trading has emerged as an innovative approach for selling electricity from prosumer to consumer at the distribution level. This paper is the first to conduct a techno-economic assessment of P2P energy trading in Aswan, Egypt. Different scenarios under different electricity tariffs, which consider photovoltaic systems, energy storage systems, and electric vehicles deployment, are analyzed to assess the performance of P2P trading considering different distributed energy resources (DERs) installations. The variety of these scenarios enables a thorough analysis of P2P trading and a clear comprehension of how P2P trading impacts distribution networks. The study offers new perspectives on the impacts of implementing P2P trading on the distribution network since it uses a real demand profiles. Results show that P2P energy trading can reduce community electricity costs, improve self-consumption by reducing exports to distribution system operator, and rise self-sufficiency compared to home energy management system (HEMS). The distribution network operation limits are not violated in any of the studied scenarios and electricity tariffs. The impacts on the distribution network for P2P trading scenarios and equivalent HEMS are very similar for flat tariff. However, for time of use tariff, P2P trading scenarios with flexible devices result in higher impacts on the distribution network than the equivalent HEMS.

The issue of power quality (PQ) has become increasingly critical in modern power systems due to the growing integration of sensitive electronic devices, electric vehicles, and renewable energy sources. Among the various PQ concerns, harmonics have emerged as a significant contributor to the overheating of power system components. This overheating phenomenon can have detrimental effects, including reduced equipment lifespan, increased maintenance costs, and unexpected failures. This comprehensive review paper provides an in-depth examination of the impact of harmonics on power system components, with a focus on the overheating problems they induce. The paper delves into an analysis of PQ indices and standards, underscoring the necessity for efficient mitigation techniques to address harmonic-related overheating and other PQ concerns. The review explores the key PQ devices employed to mitigate harmonics, such as passive power filters, active power filters, and advanced devices like D-STATCOM, unified PQ conditioners, and dynamic voltage restorers. Furthermore, it investigates the various control algorithms and optimization techniques utilized in the design of these power filters, emphasizing their pivotal role in addressing harmonics and preventing overheating. It also discusses the applicability of artificial intelligence techniques in mitigating power harmonics and improving PQ in modern power grid studies.

At present, the integration of microgrids into power systems presents significant power quality challenges in terms of the rising adoption of nonlinear loads and electric vehicles. Ensuring the stability and efficiency of the electrical network in this evolving landscape is crucial. This paper explores the implementation of cascading Proportional–Integral (PI-PI) and cascading Fractional-Order PI (FOPI-FOPI) controllers for a Distribution Static Compensator (DSTATCOM) in hybrid microgrids that include photovoltaic (PV) systems and fuel cells. A novel hybrid optimization algorithm, WSO-WOA, is introduced to enhance power quality. This algorithm leverages the strengths of the White Shark Optimization (WSO) algorithm and the Whale Optimization Algorithm (WOA), with WSO generating new candidate solutions and WOA exploring alternative search areas when WSO does not converge on optimal results. The proposed approach was rigorously tested through multiple case studies and compared with established metaheuristic algorithms. The findings demonstrate that the WSO-WOA hybrid algorithm significantly outperforms others in optimizing the PI-PI and FOPI-FOPI controllers. The WSO-WOA algorithm showed an improvement in accuracy, surpassing the other algorithms by approximately 7.29% to 14.1% in the tuning of the PI-PI controller and about 8.5% to 21.2% in the tuning of the FOPI-FOPI controller. Additionally, the results confirm the superior performance of the FOPI-FOPI controller over the PI-PI controller in enhancing the effectiveness of the DSTATCOM across various scenarios. The FOPI-FOPI provided controller a reduced settling time by at least 30.5–56.1%, resulting in marked improvements in voltage regulation and overall power quality within the microgrid.

This paper presents a Super-Twisting Algorithm (STA) sliding mode control technique as an alternative to the conventional Proportional-Integral (PI) control system. This application is specifically in the context of a nonlinear Archimedes Wave Swing (AWS) device, which functions as a wave energy converter system (WECS) connected to the power grid. The main goal is dynamic performance enhancement of such wave energy systems under network disturbances. Two STA controllers are essential for the rectifier control system in the grid-connected system to capture the most power from waves while limiting losses in the AWS linear generator. In addition, four STA controllers are incorporated into the inverter control loop to preserve the DC link and common coupling voltages at the preset values. The Honey-Badger Algorithm (HBA) is applied for the optimization of gain parameters of the STA controllers, which are compared to the PI gains adjusted using the coot, the hybrid augmented grey wolf optimizer and cuckoo, and PSO search techniques to assess the worthiness of adopting this new control approach. The grid-connected AWS experiences a variety of faults, including symmetrical and unsymmetrical faults with successful and unsuccessful breaker reclosures. Finally, the system is experimentally validated using the OP4510 real-time simulator. The experimental results reveal that the HBA-STA controllers outperform PI controllers in omitting fluctuations in the regulated variables, making the STA a strong contender as a control method.

Electric mobility (E-Mobility) has expedited transportation decarbonization worldwide. Lithium-ion batteries (LIBs) could help transition gasoline-powered cars to electric vehicles (EVs). However, several factors affect Li-ion battery technology in EVs’ short-term and long-term reliability. Li-ion batteries’ sensitivity and non-linearity may make traditional dependability models unreliable. This state-of-the-art article investigated power fade (PF) and capacity fade (CF) as leading reliability indicators that help analyze battery reliability under various ambient temperatures and discharge C-rates. Trends in LIBs applications for EVs and E-mobility are discussed. Furthermore, qualitative analysis and risk management were conducted to identify the reliable and unreliable zones of battery operation based on these indicators and the degradation circumstances implemented in recent publications. Besides, the influence of degrading circumstances on reliability indicators over the battery’s lifespan, such as a high C-rate at a low temperature throughout the battery's lifetime, has been presented in a comprehensive investigated case study in this work.

The vast diversity of wave energy conversion systems (WECSs) in the literature makes selecting the suitable WECS for wave energy harvest a stubborn process. This work summarizes six of the most widely adopted WECSs used heavily in previous research assessments and practical projects. This includes the Archimedes Wave Swing (AWS), the Wave Dragon (WD), Pelamis Wave Power (PWP), Aquabouy (AB), the Oyster, and the Oscillating Water Column (OWC). The work includes the mathematical modeling of these WECSs and the different projects and prototypes that involve these WECSs. Moreover, the latest research development in each of these WECSs is presented. Also, the wave energy potential in the world is discussed. Besides, the wave energy potential in Egypt, including that of the Mediterranean and the Red Sea, is discussed in detail. Furthermore, the steps required to perform a future feasibility study in Egypt and suggestions for the enhancement of an older study are provided. Finally, some suggestions and required equations are presented to explore the site power density and the most suitable WECS to be utilized in Egypt.

Introduction: At the present time, much attention has been focused on new types of solar cells, called perovskite solar cells. They are highly efficient devices with more than 25% power conversion efficiency. However, perovskite solar cell performance has not yet been fully explored. Objectives: We aimed to mathematically investigate the analytical modeling of current–voltage curves of planar heterojunction perovskite solar cells using Perovich Special Trans Function Theory (STFT). Furthermore, we proposed novel analytical closed-form solutions for short-circuit current and open-circuit voltage of these cells in terms of STFT. We evaluated the safety for laying the theoretical foundation by comparing the accuracy of the proposed expressions by the known methods. Methods: A novel hybrid metaheuristic algorithm, called particle swarm optimization (PSO) – evaporation rate water cycle algorithm (ERWCA), is proposed to determine equivalent circuit parameters of the perovskite solar cell. A novel objective function is introduced for estimating the parameters for that purpose too. Results: It was shown that STFT is very applicable and efficient for representing current–voltage expressions of perovskite solar cells. STFT provides a more accurate solution and requires fewer order members than the solutions provided by the conventional Taylor series. Based on these expressions and numerical calculations, it is verified that the characteristic values ​​of variables (short-circuit current, no-load voltage, efficiency, and fill factor) were not accurately calculated in the literature. Also, parameters of equivalent circuits of these cells were not accurately estimated. The equivalent circuit parameters were determined using the algorithm proposed in this work, which fit the verified values ​​of characteristic quantities much better than the literature. Conclusion: This work lays the foundation for developing the planar-structured perovskite solar cell models, in which the proposed estimation method and expressions are highly effective and provide excellent results.

In this work, a novel multi-port bidirectional converter is proposed for energy storage in electric vehicles (EV). The proposed converter has the ability to work in both bidirectional step-up (boost) and step-down (buck) modes. There are three ports in the proposed structure that the energy can flow between them. The main features of the proposed converter are the low components count, the low peak voltage of the main switches and low losses. Moreover, only two power switches are utilized in the proposed converter, which makes it easy to transfer the power between the sources. The suggested converter can be worked in energy storage system (ESS) due to the ability of step-up and step-down operation principles. Thus, it can charge and discharge the ESS with high voltage conversion ratio. Besides, the low number of components is utilized in the suggested converter to provide a bi-directional feature that leads to a reduction in the overall cost of the system. In order to investigate the effectiveness of the proposed converter, a technical survey, mathematical calculation, and a comparison study with other existing structures have been introduced in this paper. Finally, to validate the performance of the proposed converter, a laboratory prototype is implemented with a 150 W output power rate at 50 kHz switching frequency 20 V and 12 V input voltages, efficiency about 94.11% in step-up mode and 94.46% in step-down mode.

Hybrid renewable energy (HRE) sources have become a popular alternative in isolated and rural locations; because of the increased prices of fossil fuels and their CO2 emissions. This paper investigates the application of recent optimization methods to dedicate the optimal configuration of HRE sources. These HRE sources comprise photovoltaic (PV) panels, wind turbines (WT) with a battery storage system (BSS), and backup diesel generators (DGs). The cost function for the optimization problem has been chosen to be the cost of energy (COE) and the loss of power supply probability of the HRE system. The main function of the optimization algorithms is to minimize this cost function. However, the optimal configuration cannot be accomplished without fulfilling the system reliability and operational constraints. Salp swarm algorithms (SSA), the grey wolf optimizer (GWO), and the improved grey wolf optimizer (IGWO) were employed as optimization techniques. To assess the efficacy of these optimization techniques, they were applied using MATLAB simulation. A comparison of their outcomes was conducted in order to determine which technique was the most effective. Furthermore, a statistical study was carried out to determine the performance stability of each optimization strategy. The results revealed the optimal variables, including the number of PVs, WTs, battery banks (BBs) and the capacity of DGs. Over the course of a year, the optimized configuration was put to the test against a study of its capital and fuel expenses. IGWO showed the best performance among the three techniques, while the statistical results prove the robustness of the SSA algorithm.

This paper presents an integrated generation and transmission expansion planning (G&TEP) model embedding with energy storage systems (ESSs) to reduce G&TEP projects’ cost, enhance power system’s reliability, decrease carbon emission, and increase the penetration of renewable energy systems. ESSs are a key component of modern power grids for their ability to solve many challenges and problems. Electrical system operators and planners often resort to ESSs, which are considered promising technology and are sometimes the only economically viable way to address deficiencies in the planning phase, specifically in the presence of renewable energy sources. However, choosing the right ESS type to solve a problem is still a challenge and essential to cost-effectively integrating ESSs into power systems. In this work, a techno-economic planning model is formulated to decide on a suitable ESS type for the optimal configuration of two power systems, taking into account the technical and economic aspects of ESSs. Three long-term and seven medium-term types of ESSs were tested for this purpose. A hybrid scheme of Runge Kutta optimizer and gradient-based optimizer is applied to solve the problem. The proposed planning model is implemented on the known Garver system and a real system in Egypt (Egyptian west delta network). The numerical results found that pumped hydroelectric storage is the most effective type in achieving N-1 reliability constraints, increasing the use of RESs, and reducing the planning cost. ESSs enhanced systems’ security and reduced the total planning cost by 0.86%–2.35% for the Garver network. The results showed that using ESSs is necessary for some power systems, like the west delta network, to avoid rolling blackouts. The investment and operating costs of ESSs, in the presence of RESs, reach more than 50 % of the total planning cost in some case studies. However, ESSs, in the absence of RESs and reliability constraints, increase the planning cost by 5%–50%. Finally, the hybrid scheme proved its superiority in solving the proposed problem.

Abstract In this paper, the amount of microgrid frequency deviation in the dynamic state can be reduced by improving the frequency controller and implementing a new method. The proposed controller is designed for a microgrid including renewable resources, and the proposed control strategy is such that the controller coefficients are adjusted and optimised at all times by the model predictive control (MPC). The weight parameters of the MPC controller have been optimised by the particle swarm optimisation (PSO) algorithm. The proposed controller is located in the secondary frequency control loop, and by applying a control signal to the sources, the frequency perturbations following the power changes in the microgrid are reduced. The simulation results show that the proposed controller performs better than the Ziegler–Nichols PI controller (PI‐ZN) method, PI‐based controllers that rely on fuzzy logic (PI‐Fuzzy), the fractional‐order proportional‐integral‐derivative (FOPID) controller that is based on chaos particle swarm optimisation (FOPID‐CPSO) algorithm and the PID controllers based on CPSO algorithm (PID‐CPSO). It has been able to effectively reduce the frequency fluctuations in terms of amplitude and number of oscillations is also more resistant to the uncertainty of microgrid parameters and shows better performance when changing parameters than other methods.

Abstract Antibiotic resistance is a global problem. This is the reason why scientists search for alternative treatments. In this regard, seven novel silver chromite nanocomposites were synthesized and assayed to evaluate their antimicrobial, antiviral, and cytotoxic activity. Five bacterial species were used in this study: three Gram-positive (Bacillus subtilis, Micrococcus luteus, and Staphylococcus aureus) and two Gram-negative (Escherichia coli and Salmonella enterica). Three fungal species were also tested: Candida albicans, Aspergillus niger, and A. flavus. The MIC of the tested compounds was determined using the bifold serial dilution method. The tested compounds showed good antibacterial activity. Maximum antibacterial activity was attained in the case of 15 N [Cobalt Ferrite (0.3 CoFe2O4) + Silver chromite (0.7 Ag0.5Cr2.5O4)] against M. luteus. Concerning antifungal activity, C. albicans was the most susceptible fungal species. The maximum inhibition was recorded also in case of 15 N [Cobalt Ferrite (0.3 CoFe2O4) + Silver chromite (0.7 Ag0.5Cr2.5O4)]. The most promising antimicrobial compound 15 N [Cobalt Ferrite (0.3 CoFe2O4) + Silver chromite (0.7 Ag0.5Cr2.5O4)] was assayed for its antiviral and cytotoxic activity. The tested compound showed weak antiviral activity. The cytotoxic activity against Mammalian cells from African Green Monkey Kidney (Vero) cells was detected. The inhibitory effect against Hepatocellular carcinoma cells was detected using a MTT assay. The antimicrobial effect of the tested compounds depends on the tested microbial species. The tested compounds could be attractive and alternative antibacterial compounds that open a new path in chemotherapy.

This paper introduces fault type identification, fault section selection, and fault phase selectivity using a Karen Bell Transformation matrix in earthed distribution grids coupled with Doubly Fed Induction Generator (DFIG). Rogowski Coil’s (RC) model is estimated using a system identification toolbox to obtain the precise terminal voltage. This model is calculated and verified using experimental results for the RC’s primary input current and output voltage. Then, the estimated voltage relies on the current signals in the measuring point. The transient slope of the ground and aerial modes voltages is extracted during the fault to classify the fault type, discriminate the fault section and identify the faulted phase in every cycle. Further, earth faults are detected using ground and phase faults using aerial modes. The relative polarity of the front slopes at the terminals of each section created by the fault voltage is extracted to perform faulty section discrimination. The value of aerial modes is used to identify the defective phase selectivity. The overall protection selectivity function’s integration procedure with modified autonomous control is proposed for earthed distribution systems. The Transient Selectivity Function (TSF) was presented in unearthed and compensated grids in the literature. After estimating the RC voltage output at each agent, the discrete Fourier transform (DFT) phasor extraction is done. Different standard fault cases and system conditions are investigated to evaluate the proposed scheme’s behavior. Simulation results are designed using the ATP-EMTP program. The simulation results obtained validate the effectiveness of the scheme proposed in this work.

The broad spread of renewable energy sources (RESs) and storage systems increases modern power systems’ challenges and may conflict with system operation requirements. Determination of the maximum hosting capacity (HC) is crucial for utilities to estimate the maximum capacity of RESs and storage units that a network can accommodate efficiently. Several models were developed in the literature that lacks studying the impact of reliability requirements and the size of other network components on the HC levels. This paper proposes a framework to effectively maximize the hosting of RESs and storage systems in power systems. An AC optimal power flow is used to formulate the problem and consider the networks’ real operation. The objective function is formulated to expand transmission lines, allocate fault current limiters (FCLs), and ensure HC enhancement. The AC models are always complex and non-convex, and finding optimal solutions is critical. A hybrid artificial rabbits sine–cosine algorithm is developed to solve the problem, and its performance is compared to some well-known metaheuristics. The numerical studies on the Garver network and the IEE 24-bus system demonstrated that the size of different power technologies directly impacted the amount of RESs deployed. The penetration of RESs and storage systems was maximized by increasing the number of candidate circuits. The results revealed a 22 % increase in the size of RESs installed in the Garver network. Also, a 25.7 % increase in the size of RESs for the 24-bus system was obtained due to the increased candidate transmission lines. Further, an increase of 49 % was noticed in the size of storage systems built for the 24-bus system. The use of FCLs was necessary to maintain the short-circuit current below the required values. For both systems, the size of FCLs required was increased by 24.3 % and 28 %, respectively. The results also showed that the hybrid artificial rabbits sine–cosine optimizer was more efficient than other algorithms in solving the investigated problem.

In this study, a nonlinear Archimedes wave swing (AWS) energy conversion system was employed to enable the use of irregular sea waves to provide useful electricity. Instead of the conventional PI controllers used in prior research, this study employed fractional-order PID (FOPID) controllers to control the back-to-back configuration of AWS. The aim was to maximize the energy yield from waves and maintain the grid voltage and the capacitor DC link voltage at predetermined values. In this study, six FOPID controllers were used to accomplish the control goals, leading to an array of thirty parameters required to be fine-tuned. In this regard, a hybrid jellyfish search optimizer and particle swarm optimization (HJSPSO) algorithm was adopted to select the optimal control gains. Verification of the performance of the proposed FOPID control system was achieved by comparing the system results to two conventional PID controllers and one FOPID controller. The conventional PID controllers were tuned using a recently presented metaheuristic algorithm called the Coot optimization algorithm (COOT) and the classical particle swarm optimization algorithm (PSO). Moreover, the FOPID was also tuned using the well-known genetic algorithm (GA). The system investigated in this study was subjected to various unsymmetrical and symmetrical fault disturbances. When compared with the standard COOT-PID, PSO-PID, and GA-FOPID controllers, the HJSPSO-FOPID results show a significant improvement in terms of performance and preserving control goals during system instability

In the literature, different approaches that are employed in designing automatic voltage regulators (AVRs) usually model the AVR as a single-input-single-output system, where the input is the generator reference voltage, and the output is the generator voltage. Alternately, it could be thought of as a double-input, single-output system, with the excitation voltage change serving as the additional input. In this paper, unlike in the existing literature, we designed the AVR system as a sextuple-input single-output (6ISO) system. The inputs in the model include the generator reference voltage, regulator signal change, exciter signal change, amplifier signal change, generator output signal change, and the sensor signal change. We also compared the generator voltage responses for various structural configurations and regulator parameter choices reported in the literature. The effectiveness of numerous controllers is investigated; the proportional, integral and differential (PID) controller, the PID with second-order derivative (PIDD2) controller, and the fractional order PID (FOPID) controller are the most prevalent types of controllers. The findings reveal that the regulator signal change and the generator output signal change significantly impact the generator voltage. Based on these findings, we propose a new approach to design the regulator parameter to enhance the response to generator reference voltage changes. This approach takes into consideration changes in the generator reference voltage as well as the regulator signal. We calculate the regulator settings using a cutting-edge hybrid technique called the Particle Swarm Optimization African Vultures Optimization algorithm (PSO–AVOA). The effectiveness of the regulator design technique and the proposed optimization algorithm are demonstrated.

Today’s power systems are characterized by a considerable penetration of electricity production from renewable energy sources. However, renewable energy sources are characterized by intermittent non-stationary output, which leads to more complicated regulation of voltage, power flows, and frequency in power systems. Energy storage systems (ESSs) have been identified as one of the more efficient solutions to overcome some of these problems. In the literature, ESSs have been predominantly viewed as producers (in discharging) or consumers (in charging) of active power. Using bi-directional converters (BDCs), ESSs can regulate the connection point’s reactive power. Therefore, this paper aims to consider the effects of the coordination of BDCs and ESSs on modern power systems. The CONOPT solver embedded in the general algebraic modeling system (GAMS) was used to analyze this problem and find the optimal location of ESSs in the system. All calculations were performed on both IEEE 39- and IEEE 30-test networks, while the objective function was minimizing the power loss. The presented results show that losses in the power system can be reduced by correctly allocating ESSs. Furthermore, the simultaneous allocation of ESSs and BDCs efficiently reduce power system losses using the CONOPT solver.

This paper presents an economical-technical-environmental dispatch (ETED) model for an adapted IEEE 30-bus system incorporated with thermal and a mix of renewable energy sources (RESs). Total fuel costs, active power losses, and emissions level minimization is the main aim. Different equality and inequality limits involving prohibited operating zones (POZs) are considered as system restrictions. Metaheuristic optimization techniques – moth-flame optimization, salp swarm algorithm, improved grey wolf optimizer, and multi-verse optimizer – are employed to find the best solution for the generation cost, losses, and emissions. Various scenarios are examined to approve the ability of the formulated optimization model in solving the problem. A weighted sum strategy using the analytic hierarchy process (AHP) is used to convert the multi-objective problem into a normalized single-objective one. The AHP-ETED model presented in this work can significantly minimize fuel costs to 902.4951 $/h, lower emission levels as 0.09785 t/h, and achieve a lower power loss of 2.4110 MW. The results attained validate that the IGWO outperforms the other considered algorithms in finding the best solution to the ETED problem.

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.

Hosting capacity (HC) is the mathematical interpretation for the maximum aggregated distributed generations (DGs) penetration among the distribution networks. DGs integration has been adopted as a cleaner energy resource, but it has major operational issues like overvoltage, harmonics, and others. In this work, a novel mathematical optimization algorithm is adopted for HC maximization via sequential network reconfiguration followed by soft open points (SOPs) placement. Besides, a novel SOPs allocation index is proposed to reduce the computational burden taken by the optimization techniques while controlling the discrete allocation variables. Thus, to ensure maximum penetration of DG units and optimum operation scheme, HC maximization is formulated as multi-objective optimization approach, while considering system performance indices as a portion of the objective function. The proposed optimization algorithm succeeded in maximizing the HC of the IEEE 33- node distribution network, the IEEE 69- node network, the 59-node Egyptian distribution network and the 135-node Brazilian distribution network efficiently while improving system performance indices. Further analysis is conducted on the studied distribution systems to assess the effect of load growth on system’s HC after allocating both SOPs and DGs.

The old economic and social growth model, characterized by centralized fossil energy consumption, is progressively shifting, and the third industrial revolution, represented by new energy and Internet technology, is gaining traction. Energy Internet, as a core technology of the third industrial revolution, aims to combine renewable energy and Internet technology to promote the large-scale use and sharing of distributed renewable energy as well as the integration of multiple complex network systems, such as electricity, transportation, and natural gas. This novel technology enables power networks to save energy. However, multienergy synchronization optimization poses a significant problem. As a solution, this study proposed an optimized approach based on the concept of layered control–collaborate optimization. The proposed method allows the distributed device to plan the heat, cold, gas, and electricity in the regional system in the most efficient way possible. Moreover, the proposed optimization model is simulated using a real-number genetic algorithm. It improved the optimal scheduling between different regions and the independence of distributed equipment with minimal cost. Furthermore, the inverse system and energy and cost saving rate of the proposed method are better than those of existing methods, which prove its effectiveness.

The microgrid (MG) ensures a reliable power supply as it can work in a grid-independent mode. This mode requires a coordinated control strategy among distributed generators (DGs). One major challenge in a grid-independent MG is the reactive power-sharing issue. The reactive power sharing is affected by the mismatch of feeder impedance and private loads. This study thus proposed a proportionate reactive power-sharing scheme in a grid-independent mode by mathematically computing the equivalent impedance without the need for communication lines. A mathematical formula is derived to compute the equivalent impedance as a function of the total power output of DG and the power fed to the feeder. Based on the computed equivalent impedance, the virtual impedance is added to each feeder. The inclusion of virtual impedance in each line compensates the mismatch in feeder impedance and private loads providing accurate reactive power sharing among DGs. MATLAB Simulink was used to verify the effectiveness of the proposed control strategy. Furthermore, the real-time OPAL-RT simulator was used to verify the results.

This paper presents a hybrid metaheuristic method for optimal tuning of four different types of proportional-integral-derivative (PID) controller for an automatic voltage regulator (AVR) system. The method is based on the manta ray foraging optimization algorithm which is merged with the simulated annealing algorithm. Additionally, novel objective functions for the optimization of the controller’s parameters are proposed. The performance of the obtained ideal PID, real PID, fractional-order PID, and PID with second-order derivative controllers is verified by carrying out a comparison with the controllers tuned by different algorithms presented in the literature. Results of the simulations validate that each type of controller tuned with the proposed SA–MRFO algorithm outperforms the controllers tuned by other algorithms. Further, a comparative analysis is carried out to determine the most suitable controller for application in AVR systems. The main advantage of the proposed algorithm is the significant increase in the convergence speed.

Power loss is a bottleneck in every power system and it has been in focus of majority of the researchers and industry. This paper proposes a new method for determining the power loss in wind-solar power system based on deep learning. The main idea of the proposed scheme is to freeze the feature extraction layer of the deep Boltzmann network and deploy deep learning training model as the source model. The sample data with closer distribution with the data under consideration is selected by defining the maximum mean discrepancy contribution coefficient. The power loss calculation model is developed by configuring the deep neural network through the sample data. The deep learning model is deployed to simulate the non-linear mapping relationship between the load data, power supply data, bus voltage data and the grid loss rate during power grid operation. The proposed algorithm is applied to an actual power grid to evaluate its effectiveness. Simulation results show that the proposed algorithm effectively improved the system performance in terms of accuracy, fault tolerance, nonlinear fitting and timeliness as compared with existing schemes.

In this article the research results of application of back-to-back HVDC system in the Tomsk electric power system are presented. Historically, power transmission between the South to the North parts of the Tomsk electric power system is realized via the long double-circuit 220 kV overhead transmission line. However, due to electrical load increasing and growing complexity of the grid, as well as the length (about 800 km) and low transfer capacity of this transit, it cannot be used as a reliable link for parallel operation of these parts, as a consequence, there is an operational section at this transit. To solve this task, the back-to-back HVDC system is proposed to provide an asynchronous link for parallel operation of these parts. Therefore, appropriate studies and calculations were performed, in particular the optimal location and sizing of back-to-back HVDC system were defined. Extra researches of back-to-back HVDC’s impact on emergency scenarios of Tomsk electric power system power are conducted. It is shown, that its installation decreases the level of short-circuit current and oscillations in electric power system, and as a result increases the stability of load operation, especially motor load.

The problems of increasing electricity consumption and atmospheric pollutants emissions are targeted as critical challenges for power transfer in modern distribution networks. In order to tackle this issue, many researchers have proposed the incorporation of Distributed Generator (DG) units, as well as different types of reactive power compensation devices, which will contribute to the increase of networks efficiency through loss minimization, voltage profile improvement, or even increasing transmission line’s capacity. This paper addresses the optimal allocation/coordination of DG, Distributed Static VAR Compensator (D-SVC), and Distributed Thyristor-Controlled Series Compensator (D-TCSC) to reduce total power loss in the distribution network. The allocation problem is formulated as a mixed-integer nonlinear programming (MINLP) and is solved using a basic open-source MINLP (BONMIN) solver embedded in GAMS. The proposed method was tested in IEEE 16-bus and 33-bus distribution test systems, where two cases were considered. Firstly, BONMIN solver is used for allocation of D-SVC, and D-TCSC, respectively, where the results showed that incorporation of these devices leads to reduction of the active power losses, while the minimal losses and highest efficiency increase for both test networks occurred in the case where 3 D-TCSCs allocated. Secondly, a coordinated allocation of DGs and D-SVCs, as well as DGs and D-TCSCs, is presented, where it was shown that coordination of these devices leads to even greater loss reduction, i.e. increased efficiency. Also, it was concluded that DG and D-TCSC coordination is better suited for loss reduction in the 16-bus system, while DG and D-SVC coordination leads to greater loss reduction for the 33-bus system. Furthermore, the comparison between BONMIN and metaheuristic methods was performed for DG allocation and DG and D-TCSC coordination in IEEE 33-bus system, where it was concluded that BONMIN performs better in terms of minimal power loss, compared to the results which were presented in the available literature. Finally, the BONMIN solver’s performance is evaluated using three parameters: number of nodes, number of iterations, and total execution time, where the numerical analysis showed that BONMIN is efficient in solving the addressed problems.

Due to the low voltage of the PV module and to enable AC module technology, where every PV module has its own DC-AC converter, this paper introduces a new high step-up seven-level two-stage multilevel inverter for a grid-connected Photovoltaic (PV) system. The introduced schematic consists of two stages. The first stage is a switched capacitor boost converter and the second stage is a seven-level seven-switches multilevel inverter. The first stage is designed to extract the maximum power of PV module and step-up its input voltage. The proposed seven-level seven-switches topology generates seven levels with only seven switches. Thus, the number of switches is reduced. The proposed inverter has eight valid switching states. In each switching state only three switches are ON at the same time. The proposed topology has some distinct features such as reduced number of switches and reduced conduction; total harmonic distortion (THD) of the current is 23% in case of pure resistive and with no filtration at the output and less than 3% using a small filter with integration into the grid. Due to the high boosting ability of the switched capacitor boost converter, a wide voltage gain is easily achievable. In this paper the system is tested at different voltage gains and the highest simulated efficiency was around 96%. The developed system is simulated in MATLAB/SIMULINK and in real time using DSPACE1202. Both simulation and real-time results are consistent with the theoretical analysis.

Wind speed modeling is essential for predicting wind energy potential and properly utilizing wind power. The efficiency of the chosen probability distribution function (PDF) to describe the measured wind speed frequency distribution determines the quality of the wind speed assessment. The original probability distribution functions are insufficient in wind speed modeling as they have a significant error between real wind speed frequency distribution and the estimated distribution curve, while the mixture probability distributions can enhance the fitting of wind speed frequency distribution and reduce this error. The objective of this work is to model wind speed characteristics using two and three components mixture probability distribution functions generated from the combination of original Weibull, Gamma, and Inverse Gaussian distribution functions. To examine the fitness of the probability distribution functions, statistical errors such as the root mean square error (RMSE), Chi-square error (X2), and coefficient of Correlation (R2) are used as judging criteria. Results indicate that the two-component mixture Weibull distribution gives the best fitting among the original and the other two-component mixture PDFs. Moreover, the results show that the three-component mixture Weibull distribution gives the best fitting among all distributions proposed in this work. The mixture distribution functions used in this study have a higher level of complexity than the original distribution in estimating parameters as they have a higher number of optimization variables that need an artificial intelligence optimization method. The novel metaheuristic Aquila Optimizer (AO) method is used to estimate the parameters of proposed original and mixture PDFs in this work.

Mathematical design of a new fourth-order harmonic passive filter is presented and its optimal strategy is proposed with regards to various design scenarios. The considered objective function is total demand distortion minimization of the supply current. The crow spiral-based search algorithm (CSSA) is applied to solve the design problem formulated. This CSSA relies on a spiral search mechanism that improves the convergence and balances the exploration and exploitation abilities of the conventional crow search algorithm. Various power quality indices are taken into consideration like the individual and total harmonic voltage distortion values, individual and total current harmonic distortion values, active power loss, actual and displacement power factor percentages, and resonance damping capabilities. These indices were handled as non-linear constraints in the design. Additionally, a comparative investigation of the different damped filters is presented to assess the planned filter performance. The results obtained confirm the success of the filter design proposed in this paper.

Usually, electric vehicle systems are based on various modules that should ensure the high power and stability of the vehicle on the track. The majority of these components are linked to the charging mechanism. In this regard, dynamic wireless power transfer is a practical method to solve electric vehicle range anxiety and reduce the cost of onboard batteries. Wireless recharging has long been common with pure electric vehicles and is designed to allow charging even when the vehicle is in motion. However, it is difficult to analyze this method since its operating philosophy is complex, particularly with the existence of several variables and parameters. Also, the state of the vehicle, whether it is in motion or not, defines several parameters such as the vehicle speed as well as the sizes and dimensions of the coil receivers. This paper presents a novel method to improve the performance of the dynamic wireless recharge system. In the proposed system, receiver coils have been added to maximize charging power by offering a dynamic mathematical model that can describe and measure source-to-vehicle power transmission even though it is in motion. In the proposed mathematical model, all physical parameters describing the model were presented and discussed. The results showed the effectiveness of the proposed model. Also, the experimental tests confirmed the validity of the simulation results obtained by providing two coil receivers under the vehicle.

This paper introduces a comprehensive examination of vector-controlled- (VC-) based techniques intended for induction motor (IM) drives. In addition, the evaluation and critique of modern control techniques that improve the performance of IM drives are discussed by considering a systematic literature survey. Detailed research on variable-speed drive control, for instance, VC and scalar control (SCC), was conducted. The SCC-based systems’ speed and V/f control purposes are clarified in closed and open loops of IM drives. The operations, benefits, and drawbacks of the direct and indirect field-oriented control systems are illustrated. Furthermore, the direct torque control (DTC) method for IMs is reviewed. Numerous VC methods established along with microprocessor/digital control, model reference adaptive control (MRAC), sliding mode control (SMC), and intelligent control (in terms of fuzzy logic (FL) and artificial neural networks (ANNs)) are described and examined. Uncertainties in the IM parameter are a considerable problem in VC drives. Therefore, this problem is addressed, and some studies that attempted to provide solutions are listed. Magnetic saturation and core loss impact are mentioned, as they are important issues in IM drives. Toward demonstrating the strengths and limitations of various VC configurations, a few experiments were simulated via MATLAB® and Simulink® that show the influence of machine parameter variation. Efforts are made to supply powerful guidelines for practicing engineers and researchers in AC drives.

The resistance–capacitance (RC) model is one of the most applicable circuits for modeling the charging and discharging processes of supercapacitors (SCs). Although this circuit is usually used in the electric and thermal investigation of the performance of SCs, it does not include leakage currents. This paper presents exact analytical formulas of leakage-current-based supercapacitor models that can be used in industrial applications, i.e., constant-power-based applications. In the proposed model, current and voltage are represented as a solution of nonlinear equations that are solved using the standard Newton method. The proposed expressions’ accuracy is compared with the results obtained using traditional numerical integration methods with leakage current formulation and other methods, found in the literature, with no leakage current formulation. The results confirm that including leakage current represents a more accurate and realistic manner of modeling SCs. The results show that the derived expressions are precise, allowing the generation of results that closely match those obtained using traditional numerical-based methods. The derived expressions can be used to investigate SCs further and achieve more accurate and efficient regulation and control of SCs in different applications.

The most commonly used model of solar cells is the single-diode model, with five unknown parameters. First, this paper proposes three variants of the single-diode model, which imply the voltage dependence of the series resistance, parallel resistance, and both resistors. Second, analytical relationships between the current and the voltage expressed were derived using the Lambert W function for each proposed model. Third, the paper presents a hybrid algorithm, Chaotic Snake Optimization (Chaotic SO), combining chaotic sequences with the snake optimization algorithm. The application of the proposed models and algorithm was justified on two well-known solar photovoltaic (PV) cells—RTC France solar cell and Photowatt-PWP201 module. The results showed that the root-mean-square-error (RMSE) values calculated by applying the proposed equivalent circuit with voltage dependence of both resistors are reduced by 20% for the RTC France solar cell and 40% for the Photowatt-PWP201 module compared to the standard single-diode equivalent circuit. Finally, an experimental investigation was conducted into the applicability of the proposed models to a solar laboratory module, and the results obtained proved the relevance and effectiveness of the proposed models.

An experimental and numerical study on the solar water heater is carried out in Blida area, Algeria during winter in this work. Numerical results are confirmed by experimental results, where the numerical results are presented in two forms: Numerical solution based on estimated equations of climatic data and numerical solution based on actual values of weather data. Finite difference method was used to analyze and simplify energy balance equations. Results obtained showed a significant convergence between experimental and numerical results, where optical efficiency has passed 42.65 %, while the highest value for thermal efficiency was 29.212 % on February 22, 2015, while its average exergy efficiency for February 19, 2015 is 1.81 %. Experimentally, the maximum value of hot water temperature reached 352 K on February 19, 2015. As for the volume of water heated by the studied collector, it is 0.4 m3/day. The system performance is 19.75%, the equivalent of an electrical capacity of 545.68 kWh. Economically, the LFR reflector cost is 378.87 $, where this cost can be recovered after sixteen years of the first use, as the cost of heat generation is 0.043 $/kWh. Environmentally, the CO2 mitigation is 247.14 kg. Additionally, this reflector will provide the equivalent of 127.47 m3 of Liquefied natural gas.

Among all renewable energy sources, solar cells are considered the most popular solution for a clean source of energy and have a wide range of applications from few watts to Megawatt industrial and domestic loads. Building a precise mathematical model based on nonlinear equations for solar cells as well as photovoltaic (PV) modules is an essential issue for reasonable performance assessment, control and optimal operation of PV energy systems. In the current study, a novel optimization algorithm, Tree Growth Algorithm (TGA), is applied for accurate and efficient extraction of the unknown solar cell and PV module parameters. TGA is applied for estimating the unidentified parameters of PV models. Single diode model (SDM), double diode model (DDM) and three diode model (TDM) are investigated in the mathematical models of both solar cells and PV modules. The obtained results from the application of TGA to achieve this objective are compared with different algorithms reported in the literature. Moreover, the results demonstrated that the proposed algorithm of TGA superior to other reported methods. The good matching of the I-V characteristic curve of the computed parameters with those of the measured data from the manufacturer's PV modules/cells datasheet proved that the proposed TGA may function as a competitor to the methods provided in literature for parameters' identification of PV of solar cells.

Recent hybrid simulators (or co-simulators) of the electric power system are focused on scientific and research features to propose and develop novel and more accurate simulators. The present paper demonstrates one more hybrid modelling approach based on application and combination of three modeling approaches all together: physical, analog and digital. The primary focus of the proposed approach is to develop the simulation tool ensuring such vital characteristics as three-phase simulation and modeling of a single spectrum of processes in electric power system, without separation of the electromagnetic and electromechanical transient stages. Moreover, unlimited scalability of the electric power system model and real-time simulation to ensure the opportunity of data exchange with external devices have been considered. The description of the development of the hybrid model of back-to-back HVDC link based on the proposed approach is discussed and analyzed. To confirm properties of the mentioned hybrid simulation approach and hybrid model of back-to-back HVDC link, the simulation results of the interconnection of non-synchronously operating parts of the electric power system; power flow regulation; dynamic response to external fault and damping of power oscillation in electric power system are presented and examined. Moreover, to confirm the adequacy of the obtained results, the comparison with a detailed voltage source converter HVDC model (Simulink Matlab) and Eurostag software are introduced.

This article presents a novel method for identification of synchronous generator parameters that is based on sudden short-circuit test data and a novel metaheuristic algorithm, called the adaptive black widow optimization algorithm. Unlike traditional methods defined by IEEE and International Electrotechnical Commission (IEC) standards, which rely on the armature current oscillogram, the method proposed in this article uses the field current waveform during the short-circuit test. Moreover, the standard graphical method for extraction of the generator parameters is replaced by an effective metaheuristic algorithm. The proposed algorithm tends to minimize the normalized sum of squared errors (NSSE) between simulation and experimental results. The applicability and accuracy of the proposed optimization technique are verified using experimentally obtained results from a 100-MVA synchronous generator at the Bajina Basta hydropower plant.

This paper presents the economic analysis and optimal energy management of a grid-connected MG that comprises renewable energy resources and different battery storage technologies with different characteristics such as initial charge, depth of discharge, and the number of charging/discharging cycles to minimize the total operating cost of the system by maximizing the benefits of BSS, minimizing the investment and replacement cost of BSS, and minimizing the operation and maintenance cost of DGs. Several constraints are considered, such as the output power limits of the distributed generators, the limits of power imported from or exported to the grid, load balance, and other sets of battery storage constraints. The general algebraic modeling system (GAMS) is used to solve the deterministic optimization problem. Second, stochastic optimization is used to solve the deterministic problem with market price uncertainty. Third, robust optimization using the information gap decision theory is presented to model the electric load uncertainty. The validity and effectiveness of the proposed solution are explained by comparing the results obtained by GAMS to the results obtained by other optimization techniques presented in the literature.

This article assesses the energy management of reconfigurable residential smart hybrid AC/DC microgrids considering the combined heat and power (CHP) loads as well as the electric vehicles charging/discharging behaviors. A holistic model is developed for the proton exchange membrane fuel cell to retrieve the unwanted thermal energy generated at the operation time. The proposed model makes use of the unoccupied capacity of the fuel cell for producing/storing hydrogen for the later usage and increasing its efficiency. A stochastic framework is designed using point estimate method (PEM) to capture the uncertainties of the photovoltaic and wind turbine forecast error, power company price, the operating temperature of the proton exchange membrane fuel cell, the price for natural gas, price for selling hydrogen, and the pressure of the H2 and O2 in the fuel cell stack. The PEM approach has shown superior advantages in terms of accuracy and running time. Considering the complex and nonlinear structure of the proposed framework, a proficient optimization technique based on the teacher learning algorithm (TLA) is devised. A two-phase modification method is proposed to increase the algorithm variety and help its convergence characteristics. The performance of the proposed algorithm is compared with the TLA, particle swarm optimization (PSO) algorithm and genetic algorithm (GA). For enhancing the security of the energy and data transaction within the system, a directed acyclic graph (DAG)-based security framework is introduced to guarantee the performance of the system against the subversive accesses. By using this scheme, the essential data of the units are recorded and secured in the form of public, private and transaction blockchains. The economic characteristics of the proposed method are assessed on a residential hybrid AC-DC microgrid test system.

Controlled islanding is known as a last resort to prevent power system blackouts. The most important challenge in this context is the appropriate selection of separation points in a very short time considering the practical constraints. In this regard, several islanding methods have been proposed until now. Among them, the methods that provide the appropriate solutions are usually very complicated and difficult to implement in the real-time application of power system separation. Also, all methods result in one islanding solution, which may not be optimal due to the limitation of the commonly used objective function. To overcome these limitations and reach a comprehensive solution, this paper proposes a straightforward multi-solution approach through a suggested hierarchical spectral clustering algorithm. In this concept, the most desirable islanding scenario could be selected based on secondary criteria to reach more sustainable islands. In the proposed method, the hierarchical clustering algorithm, which has good records in other applications, is improved such that the generator coherency constraint can be considered in the clustering process. Meanwhile, the transmission lines without remote-controllable circuit breakers could be easily excluded from the islanding solutions. The proposed method is tested using the model of the IEEE 39-bus test system. Furthermore, to evaluate the computational effectiveness and accuracy of the method in a large-scale grid, the model of Khorasan Regional Electric Company (KREC) power system (which is the biggest part of Iran power system) is used. The comparative analysis with the state-of-the-art methods verifies the superiority of the proposed approach.

Battery lifetime represents a significant concern for the techno-economical operation of several applications based on energy storage. Moreover, the charging method is considered as one of the main critical elements in defining and influencing the operating lifetime of batteries. Several charging techniques have been addressed in the literature, however almost all of them are suffering from lack of temperature feedback in order to maintain battery lifetime. This paper presents a new high-reliable charging method for battery energy storage systems (ESSs). The proposed temperature compensated multi-step constant current (TC-MSCC) method is developed based upon the modified (MSCC) charging method. It enhances the operating lifetime of batteries by employing a feedback from the battery temperature to control the duration and starting time of each charging current step. Compared with the traditional charging methods addressed in the literature, the proposed TC-MSCC method achieves faster charging than the conventional constant current (CC) and the constant-current constant-voltage (CC-CV) methods. Moreover, the proposed TC-MSCC method possesses longer operating battery lifetime with reduced thermal stresses compared to the traditional MSCC methods. The proposed charging method is verified by simulation and experimental results using 9 Ah lead-acid battery. However, the new proposed TC-MSCC method is generalized and can be applied to various types of batteries. The detailed performance comparisons and results show the superiority of the proposed methods over the most widespread charging methods in the literature.

In the literature, all investigations dealing with regulator design in the AVR loop observe the AVR system as a single input single output (SISO) system, where the input is the generator reference voltage, while the output is the generator voltage. Besides, the regulator parameters are determined by analyzing the terminal generator voltage response for a step change from zero to the rated value of the generator voltage reference. Unlike literature approaches, in this study, tuning of the AVR controllers is conducted while modeling the AVR system as a double input single output (DISO) system, where the inputs are the setpoint of the generator voltage and the step disturbance on the excitation voltage, while the output is the generator voltage. The transfer functions of the generator voltage dependence on the generator voltage reference value and the excitation voltage change were derived in the developed DISO-AVR model. A novel objective function for estimating DISO-AVR regulator parameters is proposed. Also, a novel metaheuristic algorithm named hybrid simulated annealing and gorilla troops optimization is employed to solve the optimization problem. Many literature approaches are compared using different regulator structures and practical limitations. Furthermore, the experimental results of 120 MVA synchronous generators in HPP Piva (Montenegro) are presented to show the drawbacks of the literature approaches that observe generator setpoint voltage change from zero to the rated value. Based on the presented results, the proposed procedure is efficient and strongly applicable in practice.