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In this study, an improved bald eagle search optimization algorithm (IBES) is utilized to develop home energy management systems for smart homes. This research is crucial for energy field researchers who are interested in optimizing energy consumption. The primary objective is to optimally manage load demand, reduce the average peak ratio, lower electricity bills, and enhance user comfort. To accomplish this goal, the load conversion strategy is used to coordinate household appliances and manage the home power system effectively. This approach aims to minimize peak–average ratios and electricity costs while ensuring consumer convenience. To minimize electricity bills, the study schedules the consumer's daily activities based on actual time and next day's energy demand. Furthermore, a fitness criterion is used to balance the load between off-peak and on-peak hours. The scheduler is designed to achieve an optimal device on/off state that minimizes device waiting time by coordinating household appliances in real time. To address the background problem of real-time rescheduling, dynamic programming is employed. The study evaluates the modified algorithm's performance using three pricing strategies: critical peak pricing, real-time pricing, and time of use. The modified IBES technique is utilized to achieve the specified objectives of minimizing the electricity bill, reducing the peak–average ratio, and enhancing user convenience. [ABSTRACT FROM AUTHOR]

In this paper, different methods are utilized to solve the coordination issue involving directional overcurrent relays (DOCRs) and distance relays. The proper coordination of DOCRs and distance relays is a critical issue for system security in electrical networks. Finding the DOCRs setting, pickup current(Ip) and timed dial setting (TDS), and operating time for zone-2 of distance relays is the primary objective of solving the coordination problem. The constant parameters A & B of the directional overcurrent relay that are responsible to control the form of the relay´s characteristics as well as the Ip and TDS have been regarded as variables in this problem. The optimal value for these four DOCR settings has been determined using different optimization techniques. The primary and backup relays must operate sequentially and without any violations, and this must be guaranteed by optimization techniques. In order to determine the operation time for zone-2 and DOCRs setting, optimization methods are examined utilizing the 8-bus and IEEE 30-bus networks. Different optimization algorithms, including recent and traditional techniques, are compared. The obtained results show the superiority of the genetic algorithm (GA) in solving the coordination problem of distance relays and DOCRs. Also, the obtained results prove the ability of the GA method compared to the particle swarm algorithm (PSO), grey wolf optimization (GWO), water cycle technique (WCA), equilibrium optimizer (EO), African vultures optimization algorithm (AVOA), flow direction algorithm (FDA), and gorilla troops optimizer (GTO) techniques. [ABSTRACT FROM AUTHOR]

The focus of this study is on solving the coordination problem between distance relays (DISRs) and directional overcurrent relays (DOCRs), which is a complex nonlinear problem with various constraints. Ensuring proper coordination between DOCRs and distance relays is critical for maintaining the security of electrical networks. The primary objective of employing optimization algorithms is to determine the optimal operating time for zone-2 of distance relays, as well as the DOCRs setting, time dial setting (TDS), and pickup current (Ipickup). The optimization techniques must also guarantee that the primary and backup relays operate in sequence without any violation. To this end, both recent and well-established optimization algorithms are evaluated using an 8-bus network, with the aim of determining the DOCRs setting and the optimal operating time for zone-2 of distance relays. Furthermore, a comparative analysis of different optimization algorithms, including both traditional and recent techniques, is conducted. [ABSTRACT FROM AUTHOR]

The concept of interconnected multi-microgrids (MMGs) is presented as a promising solution for the improvement in the operation, control, and economic performance of the distribution networks. The energy management of the MMGs is a strenuous and challenging task, especially with the integration of renewable energy resources (RERs) and variation in the loading due to the intermittency of these resources and the stochastic nature of the load demand. In this regard, the energy management of the MMGs is optimized with optimal inclusion of a hybrid system consisting of a photovoltaic (PV) and a wind turbine (WT)-based distributed generation (DGs) under uncertainties of the generated powers and the load variation. A modified Capuchin Search Algorithm (MCapSA) is presented and applied for the energy management of the MMGs. The MCapSA is based on enhancing the searching abilities of the standard Capuchin Search Algorithm (CapSA) using three improvement strategies including the quasi-oppositional-based learning (QOBL), the random movement-based Levy flight distribution, and the exploitation mechanism of the prairie dogs in the prairie dog optimization (PDO). The optimized function is a multi-objective function that comprises of the cost and the voltage deviation reduction along with stability enhancement. The effectiveness of the proposed technique is verified on standard benchmark functions and the obtained results. Then, the proposed method is used for energy management of IEEE 33-bus and 69-bus MMGs at uncertainties conation. The results depict that the energy management with inclusion of WTs and PVs using the proposed technique can reduce the cost and summation of the VD by 46.41% and 62.54%, and the VSI is enhanced by 15.1406% for the first MMG. Likewise, for the second MMG, the cost and summation of the VD are reduced by 44.19% and 39.70%, and the VSI is enhanced by 4.49%. [ABSTRACT FROM AUTHOR]

In order to assure accurate modelling, this study presents a new technique for appropriately modelling and simulating a proton exchange membrane fuel cell (PEMFC) system. The PEMFC is a cleaner and more sustainable energy source as compared to fossil fuels. The fundamental idea is to minimize the sum of squared error (SSE) between the estimated and measured output voltage for the Ballard Mark V model in order to identify the model parameters of PEMFC stacks as efficiently as possible using a newly developed meta-heuristic called enhanced efficient optimization algorithm (EINFO). The proposed optimizer is considered an enhanced version of the original INFO algorithm. By balancing the exploration and exploitation phases better, the EINFO algorithm is intended to improve the performance of the original INFO approach and prevent local optima. The new method was tested on 23 benchmark functions and compared to the original INFO algorithm as well as other recently evolved optimizers. The algorithm is examined and compared with some literature meta-heuristics, including the particle swarm optimization, sine cosine algorithm, dragonfly algorithm, atom search optimization, Harris hawks optimization, and efficient optimization algorithm, using 50 independent runs, in terms of convergence speed and least SSE. When compared to other methods, the final findings show that, the suggested technique achieves the fastest convergence speed. [ABSTRACT FROM AUTHOR]

Integrating distributed generations (DGs) into the radial distribution system (RDS) are becoming more crucial to capture the benefits of these DGs. However, the non-optimal integration of renewable DGs and shunt capacitors may lead to several operational challenges in distribution systems, including high energy losses, poor voltage quality, reverse power flow, and lower voltage stability. Therefore, in this paper, the multi-objective optimization problem is expressed with precisely selected three conflicting goals, incorporating the reduction in both power loss and voltage deviation and improvement of voltage stability. A new index for voltage deviation called root mean square voltage is suggested. The proposed multi-objective problems are addressed using two freshly metaheuristic techniques for optimal sitting and sizing multiple SCs and renewable DGs with unity and optimally power factors into RDS, presuming several voltage-dependent load models. These optimization techniques are the multi-objective thermal exchange optimization (MOTEO) and the multi-objective Lichtenberg algorithm (MOLA), which are regarded as being physics-inspired techniques. The MOLA is inspired by the physical phenomena of lightning storms and Lichtenberg figures (LF), while the MOTEO is developed based on the concept of Newtonian cooling law. The MOLA as a hybrid algorithm differs from many in the literature since it combines the population and trajectory-based search approaches. Further, the developed methodology is implemented on the IEEE 69-bus distribution network during several optimization scenarios, such as bi- and tri-objective problems. The fetched simulation outcomes confirmed the superiority of the MOTEO algorithm in achieving accurate non-dominated solutions with fewer outliers and standard deviation among all studied metrics. [ABSTRACT FROM AUTHOR]

In this paper, the main objective is the simulation of the electric supply for homes in remote areas located in Morocco (Oujda and Ouarzazate), Spain (Granada), and Algeria (Bechar). This simulation study is divided into two ideas, the first one is to optimize the hybrid system under a varied number of houses and the second part is to fix it in chosen values. This work relied on the particle swarm optimization (PSO) method to optimize and analyze the proposed stand-alone photovoltaic/wind/diesel/battery hybrid system. MATLAB software is used to run the simulations and to carry out the optimal solutions of the optimized system using a developed PSO algorithm. The PSO approach contains five principal operator axes, which are problem definition, PSO initial parameters, initialization, PSO main loop, and then run of the algorithm. Optimizing the system component sizes and reaching the minimum cost of energy (COE) were the function objectives of this optimization study. Moreover, calculation of the hydrogen production from the generated energy by PV and wind systems is performed. [ABSTRACT FROM AUTHOR]

Zafarana Wind Park is considered the largest wind farm in Middle East. By the year 2022, Zafarana wind farm will inject 545 MW to Egypt national electric network (ENEN). This wind farm has been connecting to ENEN in stages (eight stages) from ten years ago. Each stage represents a project of wind farm. Hence, the impact of the emergency condition of ENEN on the performance of the installed stage of Zafarana Wind Park should be studied. Moreover, determining the possible method to reduce the side effects of the emergency condition of ENEN on the installed stage of Zafarana Wind Park becomes an important issue. In this paper, a combination between Static Synchronous Series Compensator (SSSC) and crowbar (Cro-SSSC) is used to enhance the performance of the one installed stage of Zafarana Wind Park. The studied stage of Zafarana Wind Park is Zafarana Z1 wind farm (Z1). Zafarana Z1 wind farm represents that first stage of Zafarana Wind Park that was connected to ENEN. Hence, the paper takes Zafarana Z1 wind farm as the studied case to examine the impact of Cro-SSSC. The crowbar is used to protect the power components of the SSSC from the high current during grid fault condition. Also, in this paper multi-objective lightning attachment procedure optimization algorithm (LAPO) and the sine cosine algorithm (SCA) are used to evaluate and tune the control gains of SSSC and the value of crowbar's resistance. This step is important in order to determine the values of the control's gains of SSSC and the value of crowbar resistance which suit the studied system (one installed stage of Zafarana Wind Park). The sliding mode control (SMC) is used as an example of nonlinear control of SSSC. Also, a hybrid of LAPO and SMC is used to improve the performance of Zafarana Z1 during faults. The results of LAPO, SCA and SMC are compared during three-phase fault and single line to ground fault applied to the studied system. [ABSTRACT FROM AUTHOR]

In this paper, a stochastic transmission expansion planning (TEP) in the presence of wind farms under uncertain load and wind source conditions when reliability and N-1 contingency is taken into consideration is proposed. The main aim of the suggested TEP is to minimize the total planning cost while satisfying techno-economic constraints. In this paper, reliability cost is also incorporated as the loss of load cost (LOLC) into the objective function in addition to the investment cost. In order to provide more accurate and real results, the N-1 contingency is also considered as a constraint in the TEP problem. Uncertainties for the wind source and loads in the TEP problem are modeled using a scenario-based approach and Monte Carlo simulation. Grey wolf optimization (GWO) is applied to solve the TEP problem, and it is compared to other optimization techniques. The simulation results are provided in various scenarios to confirm the efficiency of the suggested stochastic TEP. [ABSTRACT FROM AUTHOR]

This paper proposes a modified version of a well-known optimization technique called Farmland Fertility Optimization algorithm (FFA). The modified FFA (MFFA) is developed in order to improve the performance of conventional FFA. It is mainly based on two stages. Firstly, the Levy flights are used to enhance the local searching capability in the exploitation phase and the global searching capability in the exploration phase. Secondly sine–cosine functions are used to create different solutions which fluctuate outwards or towards the best possible solution. The developed algorithm has been validated using ten benchmark functions and three mechanical engineering benchmark optimization problems. After that, the newly developed algorithm MFFA is used for extracting the effective unknown parameters of Proton Exchange Membrane Fuel Cells (PEMFCs) models. The optimal extraction of these parameters is essential to determine an accurate semi-empirical mathematical model for PEMFC. The sum of squared errors between the experimental data and the corresponding calculated ones is adopted as the objective function. Four different commercial PEMFC stacks are used to validate the effectiveness of the developed algorithm. The results obtained by MFFA are compared with those obtained by the conventional FFA and other well-known optimization techniques. Moreover, a comprehensive statistical analysis is performed to determine the accuracy and efficiency of the developed algorithm. The results prove the reliability and superiority of the developed algorithm compared with the conventional FFA and other state-of-the-art optimizers. [ABSTRACT FROM AUTHOR]

With arrival of advanced technologies, automated appliances in residential sector are still in unlimited growth. Therefore, the design of new management schemes becomes necessary to be achieved for the electricity demand in an effort to ensure safety of domestic installations. To this end, the Demand Side Management (DSM) is one of suggested solution which played a significant role in micro-grid and Smart Grid systems. DSM program allows end-users to communicate with the grid operator so they can contribute in making decisions and assist the utilities to reduce the peak power demand through peak periods. This can be done by managing loads in a smart way, while keeping up customer loyalty. Nowadays, several DSM programs are proposed in the literature, almost all of them are focused on the domestic sector energy management system. In this original work, four heuristics optimization algorithms are proposed for energy scheduling in smart home, which are: bat algorithm, grey wolf optimizer, moth flam optimization, algorithm, and Harris hawks optimization (HHO) algorithm. The proposed model used in this experiment is based on two different electricity pricing schemes: Critical-Peak-Price and Real-Time-Price. In addition, two operational time intervals (60 min and 12 min) were considered to evaluate the consumer's demand and behavior of the suggested scheme. Simulation results show that the suggested model schedules the appliances in an optimal way, resulting in electricity-cost and peaks reductions without compromising users' comfort. Hence, results confirm the superiority of HHO algorithm in comparison with other optimization techniques. [ABSTRACT FROM AUTHOR]

Optimization of reactive power dispatch (ORPD) problem is a key factor for stable and secure operation of the electric power systems. In this paper, a newly explored nature-inspired optimization through artificial ecosystem optimization (AEO) algorithm is proposed to cope with ORPD problem in large-scale and practical power systems. ORPD is a well-known highly complex combinatorial optimization task with nonlinear characteristics, and its complexity increases as a number of decision variables increase, which makes it hard to be solved using conventional optimization techniques. However, it can be efficiently resolved by using nature-inspired optimization algorithms. AEO algorithm is a recently invented optimizer inspired by the energy flocking behavior in a natural ecosystem including non-living elements such as sunlight, water, and air. The main merit of this optimizer is its high flexibility that leads to achieve accurate balance between exploration and exploitation abilities. Another attractive property of AEO is that it does not have specific control parameters to be adjusted. In this work, three-objective version of ORPD problem is considered involving active power losses minimization and voltage deviation and voltage stability index. The proposed optimizer was examined on medium- and large-scale IEEE test systems, including 30 bus, 118 bus, 300 bus and Algerian electricity grid DZA 114 bus (220/60 kV). The results of AEO algorithm are compared with well-known existing optimization techniques. Also, the results of comparison show that the proposed algorithm performs better than other algorithms for all examined power systems. Consequently, we confirm the effectiveness of the introducing AEO algorithm to relieve the over losses problem, enhance power system performance, and meet solutions feasibility. One-way analysis of variance (ANOVA) has been employed to evaluate the performance and consistency of the proposed AEO algorithm in solving ORPD problem. [ABSTRACT FROM AUTHOR]

This paper proposes an improved probabilistic load and distributed energy resources (DERs) modeling as pseudo-measurements by considering the correlation to be used for distribution network state estimation. The two-point method (TPM) is applied for the modeling of pseudo-measurements. The proposed method has the ability to estimate the states of a distribution network with high accuracy and short computational time. To implement the proposed scheme, the probability density functions (PDFs) of uncertain loads and DERs at different buses are extracted using historical data. Then, the TPM achieves two concentration points at each bus from obtained PDFs. Finally, the weighted least squares state estimation method is utilized at these two concentration points to obtain the probabilistic distribution of output variables. To examine the effectiveness of the suggested model, simulations are carried out on IEEE 69-bus standard test system. The proposed TPM-based state estimation approach is then compared with other conventional methods such as the Gaussian-based model, Gaussian mixture model (GMM) and Monte Carlo simulation. The superiority of the proposed TPM-based state estimation model over the GMM and Gaussian model is confirmed by a significant decrease in the running time and a noteworthy increase in the accuracy of all estimated variables. [ABSTRACT FROM AUTHOR]

Salp swarm algorithm (SSA) is a recent optimization technique inspired by behavior of the salp chains in deep oceans. However, the SSA is efficient, simple and easy to implement, it is susceptible to stagnation at local optima for some cases. The main contribution of this paper is proposing an improved salp swarm algorithm algorithm (ISSA) for enhancing the search capabilities of the original SSA to solve the optimal power flow (OPF) problem. In the proposed ISSA, both of exploration and the exploitation processes are enhanced. The exploration process is achieved by applying a random mutation to find new searching areas while an adaptive process is developed to enhance the exploitation process by focusing on the most promising search area. This strategy will balance the transformation between exploration and exploitation. The ISSA is employed to achieve OPF with non-smooth and non-convex generator fuel cost functions such as; minimizing quadratic fuel cost, piecewise quadratic cost, quadratic fuel cost considering the valve-point effect and prohibited zones. The main advantages of the ISSA are avoiding stagnation at local optima and can solve nonlinear and non-smooth optimization problems where its adaptive operators balance between the exploration and exploitation phases of this algorithm. However, the parameters of ISSA need to be carefully defined before application of algorithm. The proposed algorithm is validated using the standard IEEE 30-bus, IEEE 57-bus and IEEE 118-bus test systems. The performance of proposed algorithm is comprehensively compared with moth-flame optimization algorithm, improved harmony search algorithm, genetic algorithm and other reported optimization techniques. The results prove the effectiveness and superiority of the proposed algorithm compared with other optimization techniques. [ABSTRACT FROM AUTHOR]

This paper presents the performance of a renewable autonomous hybrid grid composed of photovoltaic system, wind turbines, hydrokinetic turbines, diesel generator and energy storage systems. Three energy dispatch control and several storage systems have been studied. Technical, environmental and economic indicators have been used to determine the impact on the hybrid autonomous grid and its sizing optimization. However, this study goes further by conducting a sensitivity analysis such as capital cost, state of charge and time step, to choose the best system configuration. The results show that, when using the energy storage system composed of pumped hydro, under the load following energy dispatch control, the net present cost and cost of energy are lower with respect to others storage technologies proposed. However, the storage system with the lowest CO2 emissions is lead acid battery using the combined cycle energy dispatch control. In addition, the wind turbines have presented the greatest sensibility in the net present cost with respect to the capital cost variation and pumped hydro-storage present sensitivity response with respect to state of charge. All configurations have different several behaviors, therefore, the advantages and disadvantages of each one are analyzed. [ABSTRACT FROM AUTHOR]

Developing a precise semiempirical mathematical model based on multi-nonlinear equations for the proton-exchange membrane fuel cell (PEMFC), which guarantees suitable and accurate simulation of the electrical characteristics of typical PEMFC stacks under various operating scenarios, is the main target of this study. The unknown parameters of the PEMFC model are extracted using a novel efficient optimization technique called coyote optimization algorithm (COA). To validate the effectiveness of the proposed COA-based PEMFC model, two different cases of seven and ten unknown parameters are performed on a commercial PEMFC taken from literature. The sum of squared errors (SSE) between the experimentally measured data and the corresponding computed ones is considered as the objective function. Besides, the effectiveness of the developed algorithm is validated under different operating conditions. Moreover, the results obtained by the application of the proposed COA have been compared with other recent optimization methods reported in the literature, and very competitive results have been provided. Furthermore, parametric and nonparametric statistical analyses are presented to evaluate the accuracy and viability of the developed COA-based PEMFC model. [ABSTRACT FROM AUTHOR]

The power systems become operate closer to loadability limits; hence, the power systems static voltage stability assessment becomes an essential task in planning and operating for electric power systems to prevent voltage instability. In this paper, the improved moth flame optimization (IMFO) technique is applied for optimal location and size of (TCSC) with the aim of reducing load shedding, preventing voltage collapse, and enhancing the power system loadability. IMFO is developed to avoid the stagnating in local optima and improve the convergence characteristics of the conventional moth flame optimization. The loadability of the system is obtained using continuation power flow (CPF). The proposed approach is formulated by merging CPF with IMFO incorporated with TCSC. Multi-objective function is solved for minimization of loadability, load shedding, voltage stability index, and severity index. A contingency analysis is implemented on power system as two scenarios: The first scenario is outage of generator and the second scenario is outage line. Placement of TCSC has been determined by power flow analysis. The developed approach is tested on standard IEEE-30 bus system in normal operation, and contingency cases of generator and bus outage. The IMFO is compared to recent and well-known optimization techniques. The results reveal the efficiency of the proposed algorithm to reduce load shedding, continuous energy service to the customers and prevent occurrence of voltage collapse. [ABSTRACT FROM AUTHOR]

Increasing the generation of renewable energies to reduce the consumption of fossil fuels that produce high concentration of greenhouse gases is the priority that several governments have set for themselves in the medium term. In this paper, the modeling of a solar thermal energy generation plant is carried out. The climatic data correspond to two coastal cities and an island in Ecuador. The main contribution is the simulation of a complete model of solar collector fields and power conversion systems, in which the variables of output temperature and oil flow intervene at the same time. Previous research uses only the outlet temperature to evaluate the power plants. The model of the solar thermal plant is composed of a solar collector field, a storage tank, and an energy conversion system. As a result, a model of a solar thermal plant is obtained that allows to make decisions when considering the incorporation of microgrids in electrical systems isolated. The use of solar thermal technology allows to reduce the risk of spills from the transport of fossil fuels on ships. In addition, the CO2 emissions involved in the construction, operation, and maintenance of a solar thermal power plant are detailed. [ABSTRACT FROM AUTHOR]

This paper proposes a new methodology for solving the coordination problem of DOCRs based on multi-objective grey wolf optimizer and fuzzy logic decision-making. In addition to the conventional objective function, a new objective function which aims to minimize the discrimination time between primary and backup relays is proposed. Moreover, the conventional objective function related to minimizing the total operating time of primary and backup relays is considered. The feasibility and performance of the proposed methodology for solving the coordination problem of DOCRs are investigated using two different systems (8-bus system and IEEE-30 bus system). The proposed methodology is compared with other reported methods. The results prove the viability and effectiveness of the proposed methodology to solve the DOCR coordination problem without any miscoordination between primary and backup relays. [ABSTRACT FROM AUTHOR]

This paper proposes an enhanced version of grey wolf optimizer (EGWO) to solve the coordination problem of directional overcurrent relays (DOCRs). The EGWO is proposed to improve the convergence characteristics and computation time of the conventional grey wolf optimizer (GWO) by selecting a suitable balance between exploration and exploitation phases. This balance is achieved by exponential decreasing of the control parameter during the iterative process. The EGWO is explored in all search space during predetermined iterations, and then it fast converges to the best optimal solution by local exploitation around the optimal solutions. The proposed optimization technique is applied to solve the coordination problem of DOCRs. The main objective of optimal coordination of DOCRs is to minimize total operating time of all primary relays with sustaining the selectivity between relay pairs. The feasibility and performance of the proposed technique for solving the coordination problem of DOCRs are investigated using four different systems, compared with several well-known techniques. The obtained results prove the effectiveness and superiority of the proposed technique compared with these techniques. The proposed technique is able to find the optimal relay settings and minimize the total operating time of relays (with a reduction ratio about 19.3995% relative to the conventional GWO) without any miscoordination. In addition, DIgSILENT PowerFactory is used to validate the proposed technique. [ABSTRACT FROM AUTHOR]

In this study, probabilistic power flow (PPF) for radial distribution systems (RDSs) integrated with photovoltaic (PV) distributed generators (DGs) is presented. The PPF is carried out using a combined approach of cumulants generating function and Gram–Charlier expansion. To express the intermittent nature of the PV power generation and demand powers, the random probabilities for solar irradiance and load demand are considered and modeled in the PPF. The benefits of PVDGs integration into RDS can be accomplished by their optimal placement and sizing. Hence, two optimization approaches are implemented to allocate the PVDG in the RDS. The first optimization approach utilizes a single-objective function based on particle swarm optimization (PSO) to minimize the total power losses in RDSs, while the second approach uses the multi-objective PSO (MOPSO) to minimize the total power losses and voltage deviation. However, in case of MOPSO, a fuzzy logic decision making is developed to adopt a suitable solution from the optimal Pareto set according to the decision-maker preference. The developed algorithm is verified using two standard IEEE radial distribution systems: IEEE 33-bus and 69-bus. The obtained results prove the ability of the developed algorithm in solving the PPF considering the optimal PVDG allocation with low computational time. [ABSTRACT FROM AUTHOR]

Solution of optimal power flow (OPF) problem is very important for power system operation, planning and energy management. OPF analysis aims to find the optimal solution of system nonlinear algebraic equations with satisfying operational constraints. In this paper, a new and efficient technique called Jaya optimizer is comprehensively applied to solve the OPF problem in power systems. Jaya optimization algorithm is characterized with the movement toward the best solution and avoiding the trapping into local optima. Different frameworks are developed for solving the single- and multi-objective (two- to five-objective functions) OPF problems. These frameworks are developed to achieve the following objective functions: fuel cost minimization, voltage deviation minimization, voltage stability enhancement, power loss minimization and emission minimization. In the developed multi-objective OPF framework, Pareto concept is combined with Jaya optimization algorithm to obtain a set of non-dominated solutions, and then the best compromise solution is extracted using fuzzy set theory. The developed OPF frameworks are validate using two standard IEEE test systems with 23 studied cases. The results prove the effectiveness and superiority of the developed OPF frameworks compared with other well-known optimization algorithms. [ABSTRACT FROM AUTHOR]

This paper focuses on modeling the performance of a small-scale combined heat and power (CHP) plant fueled with corn stover pieces (CSP) as very potential biomass resource in Egypt. The developed power plant can be used to satisfy the heat and electricity needs. The proposed power plant performance is simulated using the professional Cycle-Tempo® software. The power plant is divided into three stages; the gasification unit (downdraft fixed bed gasifier and air supply system), gas cleaning and cooling units and the micro-turbine (MT) as an electrical generation unit. In the first stage, the CSP feedstock is converted to producer gas at atmospheric pressure with a reaction temperature about 1028.6 °C, using air as a gasifying agent. The efficiency of gasification process at a regulated air–biomass ratio of 1.5 kg/kg is 77.92% with calorific value (LHV) of 3.87 MJ/kg. Cleaning and cooling stage used to dispose the undesirable gas constitutes such as; tar, flying ash, and etc. The clean gas is passing through the MT as power generator for CHP applications with turbine inlet temperature of 850 °C. Ultimately, the simulation revealed that the CHP overall efficiency of this developed plant is 63.21% providing an electrical power of 94.81 kWel and thermal power of 187.55 kWth. [ABSTRACT FROM AUTHOR]

Communication latency which inherently occurs in wide area measurement system greatly degrades small-signal stability (SSS) and is stochastic in nature, and thus, the current power oscillation damping controllers (PODCs) designed to improve SSS must consider this crucial factor. This paper proposes a probabilistic method to tune the parameters of PODCs incorporated in renewable farms to improve SSS under stochastic time delay and under other power system uncertainties arising due to renewable energy resources and loads. The proposed method is composed of two stages: The first stage quantifies the effect of time delay and other power system uncertainties on SSS, and the second stage uses this information to formulate an optimization problem. This optimization problem is solved with the help of four swarm intelligence-based optimization algorithms which are: bat algorithm, cuckoo search algorithm, firefly algorithm, and particle swarm optimization algorithm. The solutions from all these four optimization algorithms are compared, and the best result is used to optimize the parameters of the PODCs. All the analyses were conducted on a modified IEEE 68 bus system. The results show that the PODCs tuned using the proposed method greatly enhances the SSS margin under different scenarios and are probabilistically robust to the varying time delay and other power system uncertainties. [ABSTRACT FROM AUTHOR]

Wavelets are designed to have compact support in both time and frequency, giving them the ability to represent a signal in the two-dimensional time–frequency plane. The Gaussian, the Mexican hat, and the Morlet wavelets are crude wavelets that can be used only in continuous decomposition. The Morlet wavelet is complex-valued and suitable for feature extraction using continuous wavelet transform. Continuous wavelets are favoured when a high temporal resolution is required at all scales. In this paper, considering the properties from the Morlet wavelet and based on the structure of a recurrent high-order neural network model, a novel wavelet neural network structure, here called recurrent wavelet first-order neural network, is proposed in order to achieve a better identification of the behavior of dynamic systems. The effectiveness of our proposal is explored through the design of a centralized neural integrator backstepping control scheme for a two degree-of-freedom robot manipulator evolving in the vertical plane. The performance of the overall neural identification and control scheme is verified through numerical simulation using the mathematical model for a benchmark prototype. Moreover, real-time results validate the effectiveness of our proposal when using a robotic arm, of our own design, powered by industrial servomotors. [ABSTRACT FROM AUTHOR]

This paper presents an online neural identification and control scheme in continuous-time for trajectory tracking of a robotic arm evolving in the vertical plane. A recurrent high-order neural network (RHONN) structure in a block strict-feedback form is proposed to identify online in a series-parallel configuration, using the filtered error learning law, the dynamics of the plant. Based on the RHONN identifier structure, a stabilizing controller is derived via integrator backstepping procedure. The performance of the neural control scheme proposed is tested on a two degrees of freedom robotic arm, of our own design and unknown parameters, powered by industrial servomotors. [ABSTRACT FROM AUTHOR]

This paper proposes a modified grasshopper optimization algorithm (MGOA) to solve the optimal power flow (OPF) problem. The conventional GOA is a recent optimization technique that is conceptualized from the natural lifestyle of grasshopper including their movement and migration. The MGOA is based on modifying the mutation process in the conventional GOA in order to avoid trapping into local optima. Different single- and multi-objective functions are solved using the proposed optimization technique. These objective functions consist of quadratic fuel cost minimization, emission cost minimization, active power loss minimization, quadratic fuel cost and active power loss minimization, quadratic fuel cost minimization and voltage profile improvement, quadratic fuel cost minimization and voltage stability improvement, quadratic fuel cost minimization and emission minimization, quadratic fuel cost and power loss minimization, voltage profile and voltage stability improvement. The proposed technique is validated using standard IEEE 30-bus, IEEE 57-bus, and IEEE 118-bus test systems with thirteen case studies. Simulation results reveal the better performance and superiority of the proposed technique to solve various OPF problems compared with well-recognized evolutionary optimization techniques stated in the literature review. [ABSTRACT FROM AUTHOR]

This letter proposes a simple modeling for multi-control modes of HVDC into Newton–Raphson load flow algorithm. This modeling is based on power injection approach. HVDC is represented with injected loads as a function of the desired power flow through transmission line. During the iterative process, these loads are updated and injected at the terminal buses of HVDC. Four control modes of HVDC are presented to control the sending bus voltage and the active and reactive powers flow individually or simultaneously in a transmission line which only includes the HVDC. The main advantages of the proposed model are reducing the complexities of incorporating HVDC in load flow code and avoiding the Jacobian matrix modifications. In addition, this paper proposes a simple operating constraints handling strategy of HVDC. The proposed model is validated using IEEE 30-bus, IEEE 57-bus and IEEE 118-bus test systems to prove its feasibility and efficiency. [ABSTRACT FROM AUTHOR]

In this paper, we propose the use of center-node unified power flow controller (C-UPFC) for improving the performance of different types of wind farms and mitigating their negative impacts on the grid. C-UPFC is considered one of the modernist members on Flexible AC Transmission System (FACTS). C-stocktickerUPFC has the ability to control several system parameters; the active and reactive power at both ends of the interconnected transmission line and the voltage at the midpoint. Three different induction-based wind farms are considered; 1) Squirrel Cage Induction Generator (SCIG), 2) Doubly Fed Induction Generator (DFIG), and 3) a combination of SCIG and DFIG turbines, i.e., Combined Wind Farm (CWF). C-stocktickerUPFC is comprehensively modelled for the first time in MATLAB Simulink, then the performance of the three wind farms is assessed with and without this device during three phase faults. Probabilistic voltage stability index (Probabilistic VSI) is used to measure the stability of the studied systems. In addition, the performance of three wind farms integrated with C-UPFC is compared with their performance when they integrated with Static synchronous compensators (STATCOM). The results show that C-UPFC has the ability to enhance the performance of wind farms during the three phase fault. C-UPFC is capable to remain the connection between SCIG wind farm and the interconnected grid during the fault. The voltage of CWF is greatly enhanced in the case of using C-UPFC. C-UPFC also improves the output powers of DFIG and CWF, especially after fault clearance. [ABSTRACT FROM AUTHOR]

This paper presents a simple implementation of Static Shunt Compensator (STATCOM) into Newton-Raphson current injection load flow method. The controlled STATCOM bus in the network is represented by voltage-controlled bus with zero active power generation at the required voltage magnitudes. The power mismatch equation of the connected STATCOM bus is included in Newton-Raphson current injection load flow algorithm, while the other PQ buses are represented by current mismatch equations. Moreover, the parameters of STATCOM can be calculated during iterative process and the final value will be updated after the convergence is achieved. This representation of generator buses reduces the number of required equations with respect to the classical and improved versions of the current injection methods. In addition of that the developed model reduces the complexities of the computer program codes and enhances the reusability by avoiding modifications in the Jacobian matrix. The performance of the developed STATCOM model has been tested using standard IEEE systems. [ABSTRACT FROM AUTHOR]

The interest in incorporating environmentally friendly and renewable sources of energy, like photovoltaic (PV) technology, into electricity grids has grown significantly. These sources offer benefits, such as reduced power losses and improved voltage stability. To optimize these advantages, it is essential to determine optimal placement and management of these energy resources. This paper proposes an Improved RUNge–Kutta optimizer (IRUN) for allocating PV-based distributed generations (DGs) and Battery Energy Storage (BES) in distribution networks. IRUN utilizes three strategies to avoid local optima and enhance exploration and exploitation phases: a non-linear operator for smoother transitions, a Chaotic Local Search for thorough exploration, and diverse solution updates for refinement. The efficacy of IRUN is evaluated using 10 benchmark functions from the CEC’20 test suite, followed by statistical analysis. Next, IRUN is used to optimize the allocation of PVDG and BES to minimize energy losses in two standard IEEE distribution networks. The optimization problem is divided into two stages. In the first stage, the optimal size and the location of PV systems are calculated to meet peak load demand. In the second stage, considering time-varying load demand and intermittent PV generation, effective energy management of BES is employed. The effectiveness of IRUN is compared against the original RUN and other well-known optimization algorithms through simulation results. The comprehensive analysis demonstrates that IRUN outperforms the compared algorithms, making it a leading solution for optimizing PV distributed generation and BES allocation in distribution networks and the results show that the energy loss reduction reaches 63.54% and 68.19% when using PVand BES in IEEE 33-bus and IEEE 69 bus respectively. [ABSTRACT FROM AUTHOR]