This paper discusses the mathematical model and simulation of Solid Oxide Fuel Cell (SOFC), where the conventional and reversible SOFC are studied. Their performance is studied at the steady state to get the V-I polarization curves, as well as in case of changing the electric current drawn from the cell to get the change in voltage over time. Unlike most existing studies, the focus is on the time needed by the cell to reach voltage stability after a change, in preparation for studying the cell integration into an electrical network, and all of these studies are carried out when changing the cell operating conditions such as temperatures and the flow rate of reactive gases. Then optimization techniques are used, such as Walrus Optimization Algorithm (WaOA), Secretary Bird Optimization Algorithm (SBOA), Chaos Game Optimization (CGO) and Teaching-Learning Based Optimization (TLBO) to increase model accuracy and make its results closer to the published laboratory results. The results demonstrate that WaOA achieves the lowest modeling error among the investigated optimization techniques and provides the most accurate model calibration. The proposed WaOA-based tuning reduces the modeling error by more than five orders of magnitude compared to curve-fitting approaches reported in the literature, and is further employed to optimize reactant flow rates, resulting in an output power increase of approximately 6% compared to nominal operating conditions.

The wide use of renewable energy resources (RERs) and energy storage systems (ESSs) in modern distribution networks increases the complexity of studying the performance of these systems. Estimating the maximum hosting capacity (HC) is essential for the utilities to calculate the maximum penetration of RERs and ESSs that the power system can host without violating pre-specified operational constraints. Therefore, enhancing the performance of the distribution systems is an essential goal for power system operators. Several ways can improve HC, such as network reconfiguration, system reinforcement, and adding external compensators, such as capacitor banks (CBs) and automatic voltage regulators (AVRs). This paper introduces a modeling strategy for modeling the photovoltaic and wind-based distributed generators (DGs) used for planning purposes in the distribution network in the presence of ESSs and AVRs to maximize the HC level. Algorithmically, for optimization purposes, this article applies a new optimization technique called the Snake optimization algorithm, in which the optimizer decides the optimal allocation (i.e., location and size) of DGs, CBs, AVRs, and ESSs to increase the HC at each hour at each season of a rural Egyptian radial feeder system called the Egyptian Talla system. An AC power flow is performed using a forward–backward sweep technique to simulate the system’s operating conditions. The proposed modeling strategy is based on Monte Carlo simulation (MCS). The objective function was formulated to improve the voltage stability index and the loading capacity of the system, maximize the benefits obtained from reducing the system’s active power loss and the apparent power purchased from the utility, and improve the network’s HC while meeting the operator requirements. A techno-economic analysis that includes the fixed and operating costs of DGs, CBs, AVRs, and ESSs, as well as the benefits obtained from the reduction in active power loss and the complex power purchased from the grid, is also performed. The results show that the employed algorithm provides good results in which the probabilistic HC reaches 100

This study proposes an improved version of the wild horse optimizer (WHO) for the optimal allocation and sizing of distributed generators (DGs) and capacitor banks (CBs) to promote the system's susceptibility. The proposed method, namely, improved WHO (IWHO), aims to improve the performance of the system not only in terms of power loss, voltage deviation index (VDI), and voltage stability index (VSI) as in most previous studies, but also in terms of generation cost and total emissions. Five operational cases are carried out on four different systems, the IEEE 33-bus, 69-bus, 118-bus standard radial distribution systems and the real 78-bus Egyptian distribution system, to demonstrate the best performance of the proposed technique. In addition, two multiobjective functions are implemented to compare with the original WHO and other existing optimization techniques. Based on the statistical analysis, the simulation results prove that the proposed IWHO provides the best results for flexible operations, especially for large-scale complex systems. After the optimal integration of DGs and CBs, the power loss was reduced up to 94.18%, 98.53%, 92.05%, and 93.87%; the cost was reduced by 43.23%, 43.77%, 14.68%, and 99.99%; and the emissions were reduced by 99.96%, 99.99%, 76.01%, and 61.20% for 33-bus, 69-bus, 118-bus radial systems and the real 78-bus system, respectively. It is observed that the IWHO algorithm also gives recognized enhancements in conflicting objective functions such as technical, economic, and environmental objectives.

This research is dedicated to improving the control system of wind turbines (WT) to ensure optimal efficiency and rapid responsiveness. To achieve this, the fuzzy logic control (FLC) method is implemented to control the converter in the rotor side (RSC) of a doubly fed induction generator (DFIG) and its performance is compared with an optimized proportional integral (PI) controller. The study demonstrated an enhancement in the performance of the DFIG through the utilization of the proposed FLC, effectively overcoming limitations and deficiencies observed in the conventional controllers, this approach significantly improved the performance of the wind turbine. Additionally, the selected membership functions were found to be highly compatible with the unique characteristics of wind energy. The optimization process is implemented for the controllers of both the grid side converter (GSC) and RSC. Through simulated analyses conducted using MATLAB/Simulink software, comprehensive assessments are carried out. The robustness of the FLC is evaluated compared to the optimized controllers across various wind profiles and challenging fault conditions. The results demonstrate satisfactory performance of the FLC in terms of steady-state time, stability, and precision under diverse wind speed profiles. The FLC achieves a significantly better settling time than the enhanced PI, improving by approximately 14–70% under normal conditions and 40–70% under various fault conditions. Additionally, the FLC outperforms the enhanced PI in fault conditions by reducing peak-to-peak oscillations by about 30–65%. It also delivers a smaller steady-state error, with improvements of around 2–4% under both normal conditions and most fault scenarios.

This paper proposes solving single and multi-objective functions using a new metaheuristics called wild horse optimizer (WHO) used for optimal locating and sizing of Distribution Static Compensators (D-STATCOMs) in the radial distribution networks. The main highlights of this study include maximizing total annual cost savings, minimizing power loss, and enhancing total voltage deviation (VD), both with and without considering load growth factors. The feasibility and efficacy of the proposed WHO algorithm are evaluated by testing it on standard IEEE 69 bus radial distribution systems. After the installation of D-STATCOMs, power loss is reduced by up to 64.49%, total annual cost is reduced by up to 71.79%, and minimum bus voltage is enhanced from 0.909 to 0.93492. The outcomes are contrasted with other approaches documented in existing literature to affirm the proficiency of the proposed WHO method.

Injection of Distributed generators (DGS) and Shunt capacitors (SCS) simultaneously with system reconfiguration significantly promotes smart grid performance. In addition, system reconfiguration increases the injected distributed generation capacity in the system. This work proposes a wild horse optimizer (WHO) for the optimal siting and sizing of DGS and SCS in parallel with network reconfiguration. The proposed method aims to attain single and multi-objectives: minimizing active power loss, maximizing voltage stability index (VSI), and minimizing voltage deviation index (VDI). Five operational cases are introduced to elucidate the superior performance of the proposed method. The five cases are executed on IEEE 33-bus standard radial distribution test system. The single-objective function results are compared with other optimization algorithms. The simulation results belay that the proposed WHO optimizer has the best results for unfixed DGS and SCS locations.

In this paper, a novel energy management system is proposed to optimally allocate Electrical Vehicles Charging Stations (EVCS) in conjunction with Distributed Generations (DGs) to enhance the reliability and performance of distribution networks. The innovation of this work lies in the integrated approach of using distributed system reconfiguration alongside EVCS and DG allocation, specifically targeting the reduction of system interruptions as measured by the Average Energy Not Supplied (AENS) reliability index. Unlike traditional methods, our approach considers the increasing complexity of EV integration and its impact on distribution network losses. By applying advanced optimization techniques such as the Grey Wolf Optimizer (GWO) and Artificial Gorilla Troops Optimizer (AGTO), we achieve significant improvements in system reliability and loss reduction. Additionally, a novel Plant Growth Simulation Algorithm (PGSA) is introduced to further enhance distribution system reliability through network reconfiguration. The IEEE 69-bus system is utilized as a standard testbed to validate the effectiveness of the proposed methods, showcasing their potential to address the challenges of modern power distribution networks.

Many utilities adapt transactive energy markets to distribute the benefit between market participants. Utilities can benefit from markets in terms of peak load shaving and network expansion delay. On the other hand, the energy market subtracts a portion of the utility sales. Hence, the utility will have to split its fixed costs over lower energy, increasing the tariff. The tariff increase will lead to more sales reduction and eventually result in a death spiral. This work investigates the financial impact of market implementation on electricity company sales. The paper presents a multiagent-based market model. The model consists of day-ahead and balancing markets. Both markets are cleared using auction theory. Then, the utility's financial status is assessed before and after the market application. Then, the impact of utility sales reduction on the tariff is analyzed to raise the awernace of electricity company operators to avoid a death spiral.

This research is dedicated to improving the control system of wind turbines (WT) to ensure optimal efficiency and rapid responsiveness. To achieve this, the fuzzy logic control (FLC) method is implemented to control the converter in the rotor side (RSC) of a doubly fed induction generator (DFIG) and its performance is compared with an optimized proportional integral (PI) controller. The optimization process is implemented for the controllers of both the grid side converter (GSC) and RSC. Through simulated analyses conducted using MATLAB/Simulink software, comprehensive assessments are carried out. The robustness of the FLC is evaluated in contrast to the optimized controllers across various wind profiles and challenging operational scenarios. The results demonstrate satisfactory performance of the FLC in terms of steady-state time, stability, and precision under diverse wind speed profiles.

This paper demonstrates the performance of a solid oxide fuel cell (SOFC) stack with a three-phase inverter connected to the electrical grid. The system is simulated, explaining the main components of the system model and all its control methods. This study examines how the voltage and the current of the SOFC stack and the electrical grid react in case the load demand of the electrical grid is changed. The operating temperature of the fuel cell, the number of the fuel cells in the stack, and the value of the hydrogen utilization factor are changed during the study to evaluate the system performance and their effect on the voltage and the current changes, so different case studies are presented and compared. The comparison results in that the operating temperature and the fuel utilization factor are directly proportional to the stack current and inversely proportional to the stack voltage, and the number of the fuel cells in the stack is inversely proportional to the stack current and directly proportional to the stack voltage. The simulation results can be used to develop the control strategies of the SOFC system and the power electronics converters it contains.

The usage of renewable energy sources as an alternative energy source is faced with a major challenge introduced by the intermittency nature of the source. We have two options i) adapt the demand to the source, or ii) adapt the source to the demand. If the process demand is flexible and can be adapted to the energy source, it will enjoy cheap energy. Alternatively, non-flexible processes will require extra costs to be paid to adapt the source to the process energy requirements. This paper presents a demand response model to adapt the demand of industrial processes to the available energy source. The model is solved analytically using mixed integer linear programming. Then, three metaheuristic search algorithms were used. The results of the solving algorithms are compared with each other using the Wilcoxons' rank-sum test. Then the model is tested for two examples of flexible and rigid processes showing the differences.

Smart grids (SGs) concept evolution around the world is highly encouraging the integration of distributed generators (DGs) with the distribution network to increase reliance on renewable energy sources instead of fossil fuels in electrical power generation. As result of varying DGs output, the predetermined current directions and short circuit (SC) currents values will change which introduce a formidable obstacle to the conventional protection system to maintain selectivity and sensitivity of the system among different operating modes. To tackle the above mentioned problems, this paper introduces directional overcurrent relays (DOCR) in the distribution system as well as adaptive protection scheme that combines online and offline method to update relay settings according to the prevailing network topology. The proposed scheme employs network monitoring unit and communicated relays to allow relay settings updates. MATLAB/Simulink software is used in modeling and simulation of the proposed scheme on the modified IEEE 4-bus. The proposed scheme can accurately detect direction of faults and adaptively reset relay parameters for different network configurations, fault types and locations.

This paper presents a new virtual inertia control (VIC) technique for an islanded microgrid, considering the high penetration of renewable energy sources. The gains of the proposed VIC system are determined using various metaheuristic optimization algorithms (Wild Horse, Grey Wolf, and Genetic Algorithms) to accomplish the optimal performance of the frequency stability besides getting over the physical constraints effects. The dynamic effects of both virtual inertia and virtual damping have been examined. Furthermore, the power system operational requirements are considered for the frequency deviation and rate of change of frequency (RoCoF) limits. Simulations are carried out using MATLAB/Simulink software. The performance of the power system with regard to time specifications such as settling time, undershoot, overshoot, and RoCoF for the proposed techniques is presented and compared to the conventional system. The proposed technique has shown superior results over the conventional one.

Most nations, advanced and developing, use electric arc furnaces. Smelting and purifying many metals requires the Electric Arc Furnace (EAF). Unfortunately, electric arc furnaces have many problems of power quality that affect nearby loads. Thus, a model that accurately depicts electric arc furnace operation is necessary to describe this process. Two time-domain modeling methods are introduced and compared in this work. The Cassie-Mayr Time Domain correlation for arc furnaces was used to improve existing models and computational efficiency. Models accurately simulate Electric Arc Furnace behavior. The magnitudes and dynamics of arc voltage and current can be used to study the events. This study validates Electric Arc Furnaces’ famed non-linear and stochastic properties. This study introduces a unique black-box model for the EAF based on AutoRegressive Moving Average with eXogenous inputs (ARMAX) and improves the existing Cassie-Mayr model by reducing the use of differential equations to simulate EAF behavior.

Abstract The regulation of the frequency and line power flow in interconnected power networks is considered to be a key aspect of load frequency control (LFC). This article broaches a modern power network composed of three interconnected control areas including traditional generation units taking into account non‐linearities, also renewable energy sources (RESs) and energy storage (ES) units are involved in the power grid paradigm. Two forms of RESs are included in the analysis, which are photovoltaic (PV) and wind power plants. In addition, the study framework involves three types of ES units, which are batteries of plug‐in electric vehicles (PEVs), flywheel energy storage system (FESS) and capacitive energy storage system (CESS). In this analysis, LFC is accomplished by the use of proportional‐integral‐derivative (PID) controllers in the system control loops. A recent optimization algorithm called Manta Ray Foraging optimization (MRFO) is employed to obtain the optimal gain configuration of the controllers. Real site measurements are imported to the RESs involved in the study aiming to examine the proposed control scheme under realistic conditions. Compared with other rival algorithms, the effectiveness of the MRFO‐based PID controller is validated. Simulation results confirm the efficacy of the proposed control scheme. The findings also ensure the role of ES units in optimizing the time‐domain responses. The main contributions of this paper are applying a new metaheuristic optimization algorithm to solve the LFC problem and introducing a new criteria for judging the system performance in compliance with the harmonic spectrum of the responses in the frequency domain. The results of the simulation are retrieved through a MATLAB model.

PCM thermo-fluid properties are significant in the cooling process, so researchers studied many types of PCMs. The integration of PCM in the PV active cooling technique increases the efficiency of the cooling system. The current study focuses on the selection of the PCM, which is integrated as a heat source for the active device. PCM melting temperatures and latent heat control the amount of heat transferred from PV panels, which affect the PV panels' output. So, the selection of a suitable PCM with suitable characteristics will increase the cooling system's efficiency. The analysis of simulation results to optimize the best PCM characteristics found that the RT25 PCM presents maximum PV output power and minimizes PV panel temperature. Otherwise, the best PV panel performance is achieved when the PCM melting temperature is close to the ambient air temperature and the PCM solidification temperature is close to the water flow temperature.

The annual growth of grid connected wind turbines raises various challenges in power grids. Improving the control of wind turbines (WT) has a very important mission in ensuring their excellent performance. Thus, in the present paper, different optimization techniques (local unimodal sampling (LUS), harmony search algorithm (HSA), and equilibrium optimization (EO)) are presented. The optimization is applied to the proportional-integral (PI) controllers of the grid side and the rotor side converters (GSC and RSC) installed in the doubly fed induction generator (DFIG). The major goal of the current paper is to improve the grid-connected wind turbine's performance when it encounters an asymmetrical one-line to ground fault. A robustness test is performed by testing the response of the optimized controllers with different wind profiles. The results are obtained by performing simulation analyses using MATLAB/Simulink software. Results show the superiority of EO in enhancing the system's performance.

Improving the performance of distribution systems is one of the main objectives of power system operators. This can be done in several ways, such as network reconfiguration, system reinforcement, and the addition of different types of equipment, such as distributed generation (DG) units, shunt capacitor banks (CBs), and voltage regulators (VRs). In addition, the optimal use of renewable and sustainable energy sources (RSESs) has become crucial for meeting the increase in demand for electricity and reducing greenhouse gas emissions. This requires the development of techno-economic planning models that can measure to what extent modern power systems can host RSESs. This article applies a new optimization technique called RUN to increase hosting capacity (HC) for a rural Egyptian radial feeder system called the Egyptian Talla system (ETS). RUN relies on mathematical concepts and principles of the widely known Runge–Kutta (RK) method to get optimal locations and sizes of DGs, CBs, and VRs. Furthermore, this paper presents a cost-benefit analysis that includes fixed and operating costs of the compensators (DGs, CBs, and VRs), the benefits obtained by reducing the power purchased from the utility, and the active power loss. The current requirements of Egyptian electricity distribution companies are met in the formulated optimization problem to improve the HC of this rural system. Uncertain loading conditions are taken into account in this study. The main load demand clusters are obtained using the soft fuzzy C-means clustering approach according to load consumption patterns in this rural area. The introduced RUN optimization algorithm is used to solve the optimal coordination problem between DGs, CBs, and VRs. Excellent outcomes are obtained with a noteworthy reduction in the distribution network power losses, improvement in the system’s minimum voltage, and improvement of the loading capacity. Several case studies are investigated, and the results prove the efficiency of the introduced RUN-based methodology, in which the probabilistic HC of the system reaches 100% when allowing reverse power flow to the utility. In comparison, this becomes 49% when allowing reverse power to flow back to the utility.

Current research aims to identify the finest phase change material container construction and tries to close the design gap for optimum photovoltaic panel thermal management. The phase change material is used as heat sink of photovoltaic panel and heat source for thermoelectric generator. The latent heat of phase change material maximizes the power generation from thermoelectric generator. The results show that the efficiency of the photovoltaic panel was enhanced by 3% and steady for ten hours. The photovoltaic panel electrical output power was enhanced by 25% under different weather conditions. The hybrid cooling system with capsules phase change material cavity shows a significant enhancement and stability in thermal system management, photovoltaic efficiency, and system output power.

This paper presents an efficient Grey Wolf optimization technique to solve the Electrical Vehicles Charging Stations (EVCS) allocation problem in radial feeders for reliability enhancement. Energy not supplied is a reliability objective resolved to apply the Grey Wolf optimization method. Modeling and allocating the EVCS is a challenging task in the distribution network as per the growing use of Electric vehicles (EVs) due to the massive demand for electrical energy. Siting of EVCS greatly impacts the system losses of the distribution network. Based on component reliability data and system topology, distribution system reliability assessment can predict the interruption profile of a distribution network. EVCS is being adopted in distribution networks taking into consideration the state of charge of the vehicle battery. Grey Wolf optimization technique is proposed to obtain EVCS optimal location for reliability improvement. The effectiveness of the proposed objective function has been studied on standard test distribution systems (IEEE 69 bus radial distribution systems).