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In order to solve the optimal power flow (OPF) problem, a unique algorithm based on a search and rescue method is applied in this study. For the OPF problem under three objective functions, the SAR offers a straightforward and reliable solution. The three objective functions are used to minimize the fuel cost, power loss and voltage deviation as a single objective function. The OPF problem for benchmark test system, including the IEEE-14 bus, IEEE-30 bus, and IEEE-57 bus, are solved by the Search and Rescue algorithm (SAR) under specific objective functions that are determined by the operational and economic performance indices of the power system. To demonstrate the efficacy and possibilities of the SAR algorithm, SAR is contrasted with alternative optimization techniques such as harmony search algorithm, gradient method, adaptive genetic algorithm, biogeography-based optimization, Artificial bee colony, gravitational search algorithm, particle swarm optimization, Jaya algorithm, enhanced genetic algorithm, modified shuffle frog leaping algorithm, practical swarm optimizer, Moth flam optimizer, whale and moth flam optimizer, grey wolf optimizer, cheap optimization algorithm and differential evolution algorithm. The value of minimum power losses based on SAR technique is equal to 0.459733441487247 MW for IEEE-14 bus. The value of minimum total fuel cost based on SAR technique is equal to 8051.12225602148 $/h for IEEE-14 bus. The value of minimum voltage deviation based on SAR technique is equal to 0.0357680148269292 for IEEE-14 bus. The value of minimum power losses based on SAR technique is equal to 2.71286428848434 MW for IEEE-30 bus. The value of minimum total fuel cost based on SAR technique is equal to 798.197578585806 $/h for IEEE-30 bus. The value of minimum voltage deviation based on SAR technique is equal to 0.0978069572088536 for IEEE-30 bus. The value of minimum total fuel cost based on SAR technique is equal to 38017.7691758245 $/h for IEEE-57 bus. The acquired results for the OPF compared to all competitor algorithms in every case of fitness function demonstrate the superiority of the SAR method. [ABSTRACT FROM AUTHOR]

The current study concentrates on the planning (sitting and sizing) of a renewable integrated energy system that incorporates power-to-hydrogen (P2H) and hydrogen-to-power (H2P) technologies within an active distribution network. This is expressed in the form of an optimization model, in which the objective function is to reduce the annual costs of construction and maintenance of integrated energy systems. The model takes into account the planning and operation model of wind, solar, and bio-waste resources, as well as hydrogen storage (a combination of P2H, H2P, and hydrogen tank), and the optimal power flow constraints of the distribution network. Electrical and hydrogen energy are administered in an integrated energy system. The modeling of the uncertainties regarding the quantity of load and renewable resources is achieved through stochastic optimization using the Unscented Transformation method. The novelties of the scheme include the sizing and placement of a combined hydrogen and power-based renewable integrated energy system, the consideration of the impacts of bio-waste units, P2H, and H2P systems on the planning of the integrated energy system and the operation of the active distribution network, and the modeling of uncertainties using the Unscented Transformation method to reduce the calculation time. The study's results demonstrate the scheme's ability to improve the technical conditions of the distribution network by considering the optimal planning of integrated energy systems. In comparison to the network power flow, the operation status of the network has been improved by approximately 23-45% through the optimal siting, sizing, and energy management of hydrogen storage equipment, as well as renewable resources in the form of integrated energy systems. In other words, optimal energy management and planning of the integrated energy systems in the distribution network has been able to reduce energy losses and voltage drop by 44.5% and 42.4% compared to the load flow studies. In this situation, peak load carrying capability has increased by about 23.7%. In addition, compared to the case of the network with renewable resources, the overvoltage has decreased by about 43.5%. Also, Unscented Transformation method has a lower calculation time than scenario-based stochastic optimization. [ABSTRACT FROM AUTHOR]

The main challenge in finding the optimal Pareto Front (PF) and Pareto Set (PS) sets for multimodal multi-objective optimization problems (MMOPs) with conflicting objective functions is to exhibit a balanced and sustainable diversity and exploitation capability in both the objective and decision spaces. This paper introduces dynamic reference spaces based clustering (DRSC) as a new archive handling method to overcome this challenge. DRSC incorporates a niche method called dynamically switched reference spaces and adapts the K-means-based method for clustering non-dominant vectors and handling the archive. The performance of the proposed DRSC is tested on twenty-four benchmark problems. According to the results of non-parametric statistical analysis using data from four different performance metrics, DRSC-MOAGDE designed using the proposed archiving mechanism managed to achieve the best Friedman rank among thirty different competitors. According to the stability analysis results, the average success rates and average computation times of the three best performing algorithms DRSC-MOAGDE, MMODE-ICD and SSMOPSO are (88.69%, 3.01 s), (66.87%, 9.47 s) and (64.29%, 1372.49 s), respectively. It is also observed that the proposed DRSC-MOAGDE outperforms the best cost optimization values in the literature with a minimum of 0.1838 $/h and a maximum of 30.9157 $/h for the multi-objective OPF real-world problem. [ABSTRACT FROM AUTHOR]

Electrospray thrusters offer remarkable efficiency at low power levels and compact form factor, positioning them as promising propulsion systems for small satellite platforms. The ability of electrospray thrusters to emit both positive and negative ion beams has significant implications for charge neutralization. In theory, electrosprays could function without the need for an external cathode. If two separate thrusters are operated simultaneously with one emitting a positive ion beam and the other an identical negative ion beam, the spacecraft would theoretically remain charge-neutral, allowing the thrusters to operate continuously without inducing spacecraft charging. This article explores the self-balancing of emitted currents, and consequently the ion beams, by incorporating a capacitor as a control element. This approach achieves current balancing without altering the operation of the power converter. The thruster's behavior as an electrical load is simulated using a novel electrical model derived from experimental thruster data. This model, integrated within the converter, demonstrates that the inclusion of a capacitor effectively balances the emitted currents. Experimental validation aligns with the simulation results, confirming current balance with a relative error between polarities of only 1% in the final test cases. [ABSTRACT FROM AUTHOR]

In the current work, an innovative nondominated sorting colliding bodies optimization (NSCBO) technique is introduced to tackle multiobjective optimal power flow (MOOPF) challenges within electrical power networks. This method offers a means to generate a diverse array of nondominated solutions in a single iteration by including the nondominated (ND) sorting process and the concept of crowding distance. Additionally, it utilizes a spread indicator to archive the latest nondominated solutions. In the NSCBO method, the mass of each colliding body is determined by its nondominated rank rather than relying on objective function information. Moreover, a fuzzy decision-making strategy is employed to identify a suitable solution from the set of ND solutions. To showcase the scalability and viability of the NSCBO method, experiments are conducted on IEEE 30-bus, considering both bi- and tri-objective models. Comparative analysis with existing methods from recent literature demonstrates the efficacy of the NSCBO technique in handling constraints and deriving nondominated solutions for MOOPF problems. [ABSTRACT FROM AUTHOR]

The vibration energy flow characteristics of stiffened laminated composite (SLC) panels with variable angle tow (VAT) fibers subjected to a harmonic excitation force was investigated. A finite element (FE) model was established for the free and forced vibration analysis of SLC panels and validated via comparisons with results from the literature. The power flow analysis of SLC panels with various layups was carried out. A FE-based power flow analysis was conducted to show input power, energy transmission and distribution patterns on the various SLC panels. The vectors of power flow density explicitly exhibit the detailed paths of vibration energy flow within the plate with single and double stiffeners. The influences of the thickness, number, and position of the stiffeners as well as the fiber angles of the stiffener and plates on energy transfer were investigated. It was shown that the stiffeners exert significant impact on energy transmission paths. Effectiveness of using VAT skins for vibration energy flow tailoring was demonstrated. An enhanced understanding of the energy flow behavior of SLC panels was obtained, providing benefits to the vibration suppression of stiffened composite structures by designing stiffeners and VAT skins. [ABSTRACT FROM AUTHOR]

In this paper, we develop a splitting algorithm incorporating Bregman distances to solve a broad class of linearly constrained composite optimization problems, whose objective function is the separable sum of possibly nonconvex nonsmooth functions and a smooth function, coupled by a difference of functions. This structure encapsulates numerous significant nonconvex and nonsmooth optimization problems in the current literature including the linearly constrained difference-of-convex problems. Relying on the successive linearization and alternating direction method of multipliers (ADMM), the proposed algorithm exhibits the global subsequential convergence to a stationary point of the underlying problem. We also establish the convergence of the full sequence generated by our algorithm under the Kurdyka–Łojasiewicz property and some mild assumptions. The efficiency of the proposed algorithm is tested on a robust principal component analysis problem and a nonconvex optimal power flow problem. [ABSTRACT FROM AUTHOR]

We study finite-time performance of a recently proposed distributed dual subgradient (DDSG) method for convex-constrained multi-agent optimization problems. The algorithm enjoys performance guarantees on the last primal iterate, as opposed to those derived for ergodic means for standard DDSG algorithms. Our work improves the recently published convergence rate of O (log T / T) with decaying step-sizes to O (1 / T) with constant step-size on a metric that combines sub-optimality and constraint violation. We then numerically evaluate the algorithm on three grid optimization problems. Namely, these are tie-line scheduling in multi-area power systems, coordination of distributed energy resources in radial distribution networks, and joint dispatch of transmission and distribution assets. The DDSG algorithm applies to each problem with various relaxations and linearizations of the power flow equations. The numerical experiments illustrate various properties of the DDSG algorithm–comparison with standard DDSG, impact of the number of agents, and why Nesterov-style acceleration can fail in DDSG settings. [ABSTRACT FROM AUTHOR]

The fundamental objective of a modern power system lies in ensuring reliable and effective energy access for its customers. The assessment and determination of optimal operating conditions for power systems involve the utilization of the optimal power flow (OPF) tool. By considering critical factors such as generator power, bus voltages, and line power flow limits while satisfying the power balance equations, the OPF tool enables the identification of the most favorable configuration for efficient power system operation. Traditional optimization methods have limitations in addressing complex power system problems due to poor convergence and long computational times. As a result, computational intelligence tools have gained popularity in recent years. These tools are versatile and enable efficient solution of power system problems by effectively handling qualitative constraints. This paper presents a well-organized and comprehensive review of the algorithms used in power system optimization in the existing literature, encompassing the most recent developments in the field. Specifically, it examines the application of various population-based artificial intelligence techniques that have gained widespread adoption over the past decade (2012–2022). The aim of these techniques is to resolve an OPF problem. This paper organizes the reviewed papers into various types of population-based metaheuristic algorithms, each one implemented sequentially to deal with the OPF problem in the same chronological order in which they appeared in the literature. [ABSTRACT FROM AUTHOR]

A power system is a complex, nonlinear, and dynamic system, with operating parameters that change over time. Because of the complexity of big load lines and faults, high-voltage transmission power systems are usually vulnerable to transient instability concerns, resulting in power losses and increased voltage variation. This issue has the potential to cause catastrophic events such as cascade failure or widespread blackouts. This problem affects Ethiopia's high-voltage transmission power grid in the northwest area. Because of the increased demand for electrical energy, transmission lines' maximum carrying capacity should be enhanced to maintain a secure and uninterrupted power supply to consumers. To address the problem, ANFIS-based UPFC is used for high-voltage transmission lines. This device was chosen as the ideal alternative due to its capacity to rapidly correct reactive power on high-voltage transmission networks. The PSO algorithm was used to determine the best location for the UPFC. The ANFIS controller receives voltage error and rate of change of voltage error as inputs. Seventy percentage of the retrieved data are used for ANFIS training and 30% for ANFIS testing. To show the performance of the proposed controller, three-phase ground faults are used from a severity perspective. When a three-phase ground fault occurs at the midpoint of the Beles to Bahir Dar transmission line, for instance, the settling time of rotor angle deviation, rotor speed, rotor speed deviation, and output active power of synchronous generators using ANFIS-based UPFC is reduced by 70.58%, 37.75%, 37.75%, and 43.75%, respectively, compared to a system without UPFC. At peak load, PI-based UPFC and ANFIS-based UPFC reduce active power loss by 51.25% and 71.50%, respectively, compared to a system without UPFC on the Beles to Bahir Dar transmission line. In terms of percentage overshoot and settling time, ANFIS-based UPFC outperforms PI-based UPFC for transient stability enhancement. [ABSTRACT FROM AUTHOR]

Power flow (PF) analysis is a foundational computational method to study the flow of power in an electrical network. This analysis involves solving a set of non-linear and non-convex differential-algebraic equations. State-of-the-art solvers for PF analysis, therefore, face challenges with scalability and convergence, specifically for large-scale and/or ill-conditioned cases characterized by high penetration of renewable energy sources, among others. The adiabatic quantum computing paradigm has been proven to efficiently find solutions for combinatorial problems in the noisy intermediate-scale quantum (NISQ) era, and it can potentially address the limitations posed by state-of-the-art PF solvers. For the first time, we propose a novel adiabatic quantum computing approach for efficient PF analysis. Our key contributions are (i) a combinatorial PF algorithm and a modified version that aligns with the principles of PF analysis, termed the adiabatic quantum PF algorithm (AQPF), both of which use Quadratic Unconstrained Binary Optimization (QUBO) and Ising model formulations; (ii) a scalability study of the AQPF algorithm; and (iii) an extension of the AQPF algorithm to handle larger problem sizes using a partitioned approach. Numerical experiments are conducted using different test system sizes on D-Wave's Advantage™ quantum annealer, Fujitsu's digital annealer V3, D-Wave's quantum-classical hybrid annealer, and two simulated annealers running on classical computer hardware. The reported results demonstrate the effectiveness and high accuracy of the proposed AQPF algorithm and its potential to speed up the PF analysis process while handling ill-conditioned cases using quantum and quantum-inspired algorithms. [ABSTRACT FROM AUTHOR]

In this contribution, the time-domain harmonic state estimation is evaluated using the real-time digital simulation, Kalman filter, an optimal measurement algorithm and a physical scaled-down laboratory implementation. This methodology is implemented using MATLAB/Simulink® and runs on RT-LAB® platform in real time. The optimal placement algorithm of measurement devices is used to obtain full observability of the system. This measurement algorithm finds the optimal placement with a limited number of measurement devices, whose measurements may be contaminated with noise. The measurement model is implemented in the physical test power system. Besides, the state estimator assesses the system inputs (internal generator voltages, nonlinear electrical load variables) whose dynamics are totally unknown. The harmonic content of the waveforms is obtained through the fast Fourier transform. The estimated responses in the case studies using the RT-LAB® platform are validated through direct comparison against those obtained in the physical implementation of the electrical power system, obtaining satisfactory results. [ABSTRACT FROM AUTHOR]

An optimization method is proposed for the grid-tied unbalanced MG to reduce overall operating costs and voltage deviation. The proposed method consists of battery systems, EV, PV units, DG, and WT units. The proposed technique is the Ladder Spherical Evolution (LSE) Search algorithm, while finding the optimum method that minimizes both the net cost of the microgrid and the VDI while taking into account uncertain parameters is the major goal of the proposed work. The proposed model includes a 3-phase power flow to prevent the adoption of unworkable solutions. The restrictions for a 3-phase unbalanced network (such as DG, PV, WT, and BS units) originate from their technological constraints and the current-related depiction of power flow. At this point, the performance of the proposed technique is better than the existing approaches in MATLAB. The proposed method manages uncertainties, reduces functional costs and the voltage variation index, and offers more dependable and realistic solutions. The total cost of SSA is 480$, CHA is 470$, AOS is 450$, and the proposed is 340$. Thus, the proposed method provides an optimal solution with less computation and low cost than the existing methods. [ABSTRACT FROM AUTHOR]

The analysis of harmonic distortions and their propagation in electric power systems is crucial for power quality assessment. Harmonic power flow (HPF) is a widely used technique for this purpose. However, most existing research on HPF focuses on deterministic algorithms, and only a few studies have considered uncertainties associated with harmonic sources connected to microgrids. Due to the lack of recent approaches for evaluating the impact of uncertain data on HPF results, this paper proposes a new methodology for calculating harmonic voltages and distortions in microgrids by assuming uncertain parameters for both loads and harmonic sources. To obtain interval results for harmonic voltages, the load flow equations are expanded up to the second-order terms using Taylor series, considering all partial derivatives of the system variables with respect to a percentage uncertainty value. The proposed method is evaluated using simulations on a 33-bus distribution system, considering the microgrid in both islanded mode and connected to a main power substation. As demonstrated, the interval results are similar to those obtained from Monte Carlo simulations, proving the efficiency of the proposed method with robust solution and reduced computational time. [ABSTRACT FROM AUTHOR]

The escalating integration of renewable energy sources along with the prevalence of nonlinear loads in electric power systems have heightened power quality (PQ) issues. In response, this research proposes a novel distribution static compensator (DSTATCOM) design featuring a three-phase single-source five-level inverter for distributed power systems based on wind energy conversion systems. The multi-level inverter (MLI) adopts a single DC source and minimum switch count, prioritizing simplicity. A radial basis function neural network (RBFNN) assisted p-q theory-based control, coupled with a genetic algorithm tuned proportional integral (PI) controller, ensures effective compensation and stable dc-link voltage. Simulation models and a laboratory prototype confirm the effectiveness and dynamic compensation capabilities of the presented DSTATCOM design across various load conditions. Results reveal stable grid-side parameters, low total harmonic distortion (THD), and the maintenance of a unity power factor. The hardware analysis confirms reduced THD values post-compensation, 2.174% for voltage and 1.898% for current, attesting to the DSTATCOM's efficacy in improving PQ. The proposed design outperforms conventional MLI topologies in terms of component count, reliability, and simplicity. The introduced RBFNN exhibits superior harmonic extraction, achieving a minimal prediction error of 1.3 × 10 - 8 . This work establishes the effectiveness of the proposed DSTATCOM in enhancing PQ in distributed power systems, setting the stage for further optimization and integration of advanced control strategies. [ABSTRACT FROM AUTHOR]

Power flow studies are essential for planning and operating microgrids (MGs). However, power flow is generally calculated separately for MGs and medium voltage (MV) systems, which tends to overlook some characteristics of the joint MG-MV system. In this context, the literature proposes methods to simulate MG and MV systems in a unique power flow structure. However, some Volt/Var controls must be considered when solving the MG power flow, which increases the power flow iterations. This paper proposes a method to include the Volt/Var strategy in the three-phase Newton–Raphson current injection (NRCI) power flow. The Volt/Var strategy is included in the Jacobian matrix and solved with each power flow iteration. The CIGRE benchmark MV and LV networks and the European LV test Feeder are used to validate the proposed methodology. The conventional Newton–Raphson is employed for the MV system, and the NRCI for the MG systems. Three cases are considered: the first evaluates the MG with the Volt/Var strategy; the second compares the implementation; and the last evaluates the global power flow with the Volt/Var. The results show that the proposed implementation can converge smoothly with few iterations. [ABSTRACT FROM AUTHOR]

The importance of supercapacitors has grown significantly in recent times due to several key features. These include their superior power density, faster charging and discharging capabilities, eco-friendly nature, and extended lifespans. Battery Energy Storage Systems (BESS), on the other hand, have become a well-established and essential technology in the field of energy storage. However, BESS still faces challenges such as lower power density, limited cycle stability, and potential chemical hazards. Additionally, batteries perform poorly during transient events (sudden changes in power) due to their larger time constants, which can also shorten their lifespan. By strategically combining supercapacitors with BESS, their combined strengths can optimize battery performance. This study explores the role of supercapacitors in enhancing battery performance through both simulation and experimentation. The results obtained experimentally matched with simulated results. [ABSTRACT FROM AUTHOR]

A mathematical model was developed for the calculation of thermal processes occurring in hexagonally located cells in the form of connected channels with a filler. The proposed model was used to calculate the temperature fields and power of heat flows along a single homogeneous long thin rod of calcium carbonate perovskite embedded in the hexagonally arranged channels of anodic aluminum oxide. In addition, the equations for calculating the distribution of temperature and power of heat flows in the wall of the aluminum oxide channel in the perpendicular direction were derived. The resulting calculations can be used to develop advanced highly efficient solar power electronic devices. [ABSTRACT FROM AUTHOR]

The Distributed power flow controller can be considered as advance power flow controller which is combination of series and shunt compensator without DC link. In this paper Space vector Pulse width Modulation employs in Distributed power flow Controller for Harmonics Reduction and power quality improvement. A new technique SVPWM adopted which reduces the harmonics and the same time improves power quality and increase transient stability. In this paper a new technique of Multi objective Grey wolf algorithm used to optimize the controller parameter of DPFC. For the design of PI controller GWO algorithm can be used for better optimistic performance of DPFC. As a result of that power quality and voltage profile improves drastically and the same time harmonics also reduced remarkable level of 4.62% of Voltage THD and 1.8% of current THD. The simulation result and hardware model Prove the feasibility of proposed Configuration. [ABSTRACT FROM AUTHOR]

The operational behavior of modern active distribution networks differs greatly from their passive predecessors. Active network management schemes, such as Conservation Voltage Reduction (CVR), are being widely explored to enhance energy conservation and network resiliency. CVR can potentially decrease energy consumption in distribution networks by reducing the voltage at load terminals. However, accurately assessing CVR suitability for distribution feeders is paramount, as it exploits the voltage dependency of load. Most CVR assessment schemes require extensive data from smart meters, micro-pmus, and detailed network topology information. This paper presents a novel synthetic data-based approach for probabilistic CVR assessment, which uses customer categorization to generate realistic synthetic data for probabilistic assessment, taking into account the voltage dependency of common household appliances. Additionally, an extended version of the probabilistic synthetic-data-based approach is introduced to enhance the accuracy of CVR assessment using limited distribution network information. MATLAB was used for data handling and customer categorization, while OpenDSS was used for power flow analysis to evaluate CVR effectiveness. The proposed CVR assessment methodologies are tested and validated in detail for the TOPI distribution network in KPK, Pakistan. The study's findings shows that CVR can significantly reduce energy and cost, with a 10.13% reduction in energy use and approximately 4 million PKR in cost [ABSTRACT FROM AUTHOR]

As one of the largest and most complex artificial systems in the world, power systems present challenges for statistical analysis under multi-fault contingencies that alter the network topology. This paper proposes a meta-transfer learning-based (MTL) method, where base-learners are designed to learn the power flow mapping relationships for different network topologies, and a meta-learner is developed to guide the updating of structural parameters in base-learners in response to topological changes. The proposed MTL model quickly generates base-learners to adapt to new topologies and enables large-scale statistical analysis of multi-fault contingencies in power systems. The efficacy of the proposed method has been validated using the benchmark Institute of Electrical and Electronics Engineers (IEEE) 39-bus and 300-bus systems, specifically focusing on multi-fault contingencies. The results indicate that the proposed MTL model not only achieves high accuracy in dynamic topologies but also provides adaptive initial parameters for few/zero-shot learning within each topology. [ABSTRACT FROM AUTHOR]

The reliable operation of power systems while integrating renewable energy systems depends on Optimal Power Flow (OPF). Power systems meet the operational demands by efficiently managing the OPF. Identifying the optimal solution for the OPF problem is essential to ensure voltage stability, and minimize power loss and fuel cost when the power system is integrated with renewable energy resources. The traditional procedure to find the optimal solution utilizes nature-inspired metaheuristic optimization algorithms which exhibit performance drop in terms of high convergence rate and local optimal solution while handling uncertainties and nonlinearities in Hybrid Renewable Energy Systems (HRES). Thus, a novel hybrid model is presented in this research work using Deep Reinforcement Learning (DRL) with Quantum Inspired Genetic Algorithm (DRL-QIGA). The DRL in the proposed model effectively combines the proximal policy network to optimize power generation in real-time. The ability to learn and adapt to the changes in a real-time environment makes DRL to be suitable for the proposed model. Meanwhile, the QIGA enhances the global search process through the quantum computing principle, and this improves the exploitation and exploration features while searching for optimal solutions for the OPF problem. The proposed model experimental evaluation utilizes a modified IEEE 30-bus system to validate the performance. Comparative analysis demonstrates the proposed model's better performance in terms of reduced fuel cost of $620.45, minimized power loss of 1.85 MW, and voltage deviation of 0.065 compared with traditional optimization algorithms. [ABSTRACT FROM AUTHOR]

Taper beams are widely used due to its good damping properties in vibration and noise control field. For dynamic response analysis of taper beams in the high frequency, the energy finite element analysis (EFEA) based on wave theory is the most promising method. However, there is no energy finite element model for taper beams. In this paper, energy finite element model for taper beam is built though two steps. Firstly, the approximate displacement solution of the vibration equation of the taper beam is expressed by the geometry-acoustics approximation method. The expression of the relationship between energy density and power flow of the taper beam is obtained by utilizing the displacement solution. Based on the principle of conservation of energy, the governing equation taken energy density as a variable was derived. Secondly, the stiffness matrix of taper beam element and the solution format of this governing equation are obtained using the Galerkin weighting method, which led to the energy finite element model of the taper beam. Taking a taper beam as an example, the energy finite element model presented in this paper is used to calculate the energy density distribution on the taper beam, and the calculation results are consistent with the finite element calculation results, which proving the correctness of the energy finite element model of the taper beam established in this paper. In order to show the advantages of the EFEA model presented in this paper, a comparison with approximate EFEA model based on constant cross-section elements is performed. The results shown that the EFEA model presented in this paper have higher accuracy and less time consumption, which can reflect the dynamic response characteristics of the taper beam better. [ABSTRACT FROM AUTHOR]

In the studies of unit commitment or optimal power flow, to formulate a mixed-integer linear programming model that can be efficiently solved with commercial solvers, it is necessary to approximate the quadratic cost curves of thermal units as piecewise linear (PWL) functions. The conventional approach involves evenly spaced piecewise linear (ES-PWL) interpolation, which often results in relatively large approximation errors. In order to reduce the error, this paper proposes a more accurate PWL method for approximating the quadratic cost functions of thermal units. The method employs a linear least-squares fit instead of linear interpolation within each subinterval and introduces a one-terminal-constraint approach to ensure the continuity of the piecewise function. Subsequently, a straightforward equation is derived, applicable to the widely used ES-PWL interpolation, with the potential to enhance the accuracy of the approximation. Mathematical verification attests that the proposed method substantially diminishes the squared 2-norm error, less than 37.5% of the error associated with ES-PWL interpolation. Subsequent numerical investigations are carried out on a 10-unit system, the IEEE RTS-79, and a real industrial system. The findings validate that all the approximation errors of the proposed method are within 37.5% of the errors associated with the ES-PWL interpolation, meaning that a unit commitment solution that closely approximates the outcome of the quadratic function is obtained. The computational time is also acceptable. [ABSTRACT FROM AUTHOR]

The frequency security issue has become more critical in recent decades for various reasons, especially the reduced and degraded primary frequency control (PFC) response of traditional participants based on the economic incentives of generation units to earn more profit in the energy market. Since frequency security is a dynamic phenomenon, incorporating it into an optimization problem such as scheduling is complicated, especially with realistic modeling assumptions. This difficulty can be solved using a variety of solutions ranging from simple and inaccurate static methods to complex and accurate dynamic approaches. This paper proposes a competitive mechanism among participants in real-time scheduling (hour-ahead) that integrates the dynamic frequency security in the economic optimization procedure, hence enhancing the frequency security. The proposed formulation is a rescheduling scheme based on optimal power flow (OPF) which clears the energy and reserve simultaneously and considers the network constraints. Also, this mechanism uses the accurate model of the governors by including all of the influential parameters on PFC. The cutting-plane model of the nadir frequency function is used to resolve the complexity of the optimization problem. In addition, a framework for multi-objective optimization has been developed that enables the policymakers to reconcile the frequency security with the rescheduling cost. Finally, the correctness and the efficiency of the proposed framework are demonstrated from the economic and technical perspectives based on the implementation of the IEEERTS. [ABSTRACT FROM AUTHOR]

The occurrence of debris flow events in small-scale watersheds with dense vegetation in mountainous areas that result in significant loss of life and missing individuals challenges our understanding and expertise in investigating and preventing these disasters. This has raised concerns about the occurrence of large debris flow disasters from small watersheds. This study focused on a catastrophic debris flow that took place in Longtou Gully (0.45 km2) in Tianquan County, Ya'an City on 25 September 2021, which resulted in 14 deaths and missing individuals. Through comprehensive field investigations, high-precision remote sensing data analyses, and numerical simulations, we analyzed the triggering mechanisms and dynamic processes of this event. Our results indicate that the convergence hollow at the channel head exhibited higher hydraulic conditions during rainfall compared to gentle slopes and convex terrains, leading to the instability of colluvial soil due to the expansion of the saturated zone near the soil–bedrock interface. The entrainment of material eroded from the channel resulted in an approximately 4.7 times increase in volume, and the channel scarp with a height of about 200 m amplified the destructive power of the debris flow. We emphasize the need to take seriously the possibility of catastrophic debris flows in small-scale watersheds, with colluvial deposits in hollows at the channel head under vegetation cover that serve as precursor material sources, and the presence of channel scarps formed by changes in the incision rate of the main river, which is common in the small watershed on both sides. This study provides insights for risk assessment of debris flows in small-scale catchments with dense vegetation cover in mountainous areas, highlighting the importance of vigilance in addressing disasters in small-scale catchments, particularly in regions with increasing human–environment conflicts. [ABSTRACT FROM AUTHOR]

The Sine–Cosine algorithm (SCA) is efficient but faces challenges in exploitative abilities, slow convergence, and exploration–exploitation balance. This study proposes a novel optimization method, the learning-based sine–cosine algorithm (L-SCA), to solve the optimal power flow (OPF) problem. The basic SCA has been modified with a learning phase operator inspired by TLBO. The SCA handles global exploration, while the learner phase of teaching–learning based optimization (TLBO) offers strong local search capabilities, which can be utilized to enhance the solution neighborhood space provided by the SCA technique. The L-SCA and original SCA algorithms address OPF in IEEE 57-bus, Algerian 59-bus, and IEEE 118-bus power systems, considering twelve cases with a focus on cost savings, voltage stability, voltage profile, emissions, and power losses. The comparative study shows that the proposed L-SCA consistently outperforms standard SCA and other reported methods in all cases for varied-scale standard test systems as well as for a practical power system, within reasonable execution times. For instance, L-SCA in the Algerian 59-bus system cut fuel costs by around 13.13% compared to initial case, equating to annual savings of $2.2 million, while in the IEEE-118 bus system, power loss is significantly reduced to 17.881 MW, marking an 86.5% reduction compared to the base case. [ABSTRACT FROM AUTHOR]

In the operation and control of power systems, load frequency control (LFC) plays a critical role in ensuring the stability and reliability of interconnected power systems. Modern power systems with significant penetration of highly variable and intermittent renewable sources present new challenges that make traditional control strategies ineffective. To address these new challenges, this paper proposes a novel LFC strategy that employs a cascaded fractional-order proportional integral-fractional-order proportional integral derivative with a derivative filter (FOPI-FOPIDN) as a controller. The parameters of the FOPI-FOPIDN are optimised using a variant of the particle swarm optimization (PSO) in the literature called ADIWACO. The effectiveness and scalability of the proposed strategy are validated by extensive simulations conducted on two- and three-area test systems and performance comparisons with recent LFC control strategies in the literature. The performance metrics used for the evaluation are ITAE values, deviations in the power flows in the tie-lines, and deviations in the frequencies of the control areas with the power systems subjected to diverse load and RES generation disturbances in several experimental scenarios. Governor dead band, communication time delay, and generation rate constraints are considered in one of the scenarios for more realistic evaluation. Again, the controller's robustness to uncertain model parameters is validated by varying the parameters of the three-area test system by ± 50%. The simulation results obtained confirm the controller's robustness and its superiority over the comparison LFC strategies in terms of the above performance metrics. [ABSTRACT FROM AUTHOR]

This paper studies the possibility of connecting Wind Farms (WF) to the electric grid with the use of finite space model predictive command (FS-MPC) to manage wind farms to improve the quality of the current output from the doubly-fed induction generator (DFIG) with considering fault ride-through technique. This proposed system can generate active power and enhance the power factor. Furthermore, the reduction of harmonics resulting from the connection of non-linear loads to the electrical grid is achieved through the self-active filtering mechanism in DFIGs-WF, facilitated by the now algorithm proposed. FS-MPC technique has the ability to improve system characteristics and greatly reduce active power ripples. Therefore, MATLAB software is used to implement and verify the safety, performance, and effectiveness of this designed technique compared to the conventional strategy. The results obtained demonstrated the effectiveness of the proposed algorithm in handling the four operational modes (Maximum power point tracking, Delta, Fault, and Filtering). Additionally, the suggested technique exhibited flexibility, robustness, high accuracy, and fast dynamic response when compared to conventional strategies and some recently published scientific works. On the other hand, the THD value of the current was significantly reduced, obtaining at one test time the values 56.87% and 0.32% before and after filtering, respectively 27.50% and 0.26% at another time of testing, resulting in an estimated THD reduction percentage of 99.43% and 99.05%, respectively. These high percentages prove that the quality of the stream is excellent after applying the proposed strategy. [ABSTRACT FROM AUTHOR]

The interior-point method (IPM) has become the workhorse method for nonlinear programming. The performance of IPM is directly related to the linear solver employed to factorize the Karush–Kuhn–Tucker (KKT) system at each iteration of the algorithm. When solving large-scale nonlinear problems, state-of-the art IPM solvers rely on efficient sparse linear solvers to solve the KKT system. Instead, we propose a novel reduced-space IPM algorithm that condenses the KKT system into a dense matrix whose size is proportional to the number of degrees of freedom in the problem. Depending on where the reduction occurs, we derive two variants of the reduced-space method: linearize-then-reduce and reduce-then-linearize. We adapt their workflow so that the vast majority of computations are accelerated on GPUs. We provide extensive numerical results on the optimal power flow problem, comparing our GPU-accelerated reduced-space IPM with Knitro and a hybrid full-space IPM algorithm. By evaluating the derivatives on the GPU and solving the KKT system on the CPU, the hybrid solution is already significantly faster than the CPU-only solutions. The two reduced-space algorithms go one step further by solving the KKT system entirely on the GPU. As expected, the performance of the two reduction algorithms depends critically on the number of available degrees of freedom: They underperform the full-space method when the problem has many degrees of freedom, but the two algorithms are up to three times faster than Knitro as soon as the relative number of degrees of freedom becomes smaller. [ABSTRACT FROM AUTHOR]

This study presents two innovative machine learning-based models: one for daily electrical load forecasting in the State of Rio de Janeiro and another for monthly forecasting for each Light concessionaire substation in the Metropolitan Area of Rio de Janeiro (MARJ). The utilized data include (1) daily electrical load data from the National System Operator (ONS) for the State of Rio de Janeiro spanning four years (2017-2020); (2) monthly electrical load data from 84 Light substations over 11 years (2010-2020); and (3) maximum, minimum, and mean air temperatures. Additionally, remotely sensed land-surface temperature (LST) based on Landsat data from 1984 to 2020 is employed to reconfigure the existing meteorological network in MARJ based on the distribution of Light substations. The regressive machine learning algorithms undertook training using a cross-validation procedure in 500 daily and 500 monthly training-testing experiments. Results for daily-ONS and monthly-Light loads show average correlations (hindcast in parentheses) of the fitted models of 0.85±0.09 (0.83±0.07) and 0.89±0.05 (0.91±0.06), respectively. The model's Mean Absolute of Error (MAE) values correspond to a percentage error of about 4.03% (daily) and 4.83% (monthly). According to the monthly electrical load behavior revealed, when the temperature changes from 23 to 26℃ at MARJ, it rises roughly from 1.92 x 106 ± 67227.4 kWh to 2.70 x 106 ± 90198.5 kWh. A cluster analysis was conducted to optimize the meteorological station network, considering the locations of the existing 18 meteorological stations, the 84 Light electrical load substations, and the urban heat island cores. This analysis identified seven specific locations for installing new meteorological stations, aiming to improve the spatial resolution of electrical load modeling in MARJ. This insight opens paths for future research to refine the network and further enhance the models' precision in capturing the intricate relationships between meteorological factors and electrical loads in the region. [ABSTRACT FROM AUTHOR]

This paper describes a comprehensive energy-related dataset, collected from residential electricity households within an energy community in Ireland, as part of StoreNet project. The data includes local weather parameters and per household power (W) and energy (Wh) components for various aspects such as active power consumption, PV generation, grid import and export, charging and discharging, and the state of charge of energy storage. Additionally, it provides weather data for the location at a 1-minute temporal resolution for the year 2020. The dataset has been validated by comparing measurements that should yield identical results to standard load profiles, with no significant inconsistencies discovered. Validation examples have also been given through the published articles where this dataset has been used to analyse peer-to-peer energy trading benefits for the energy community and decision-making support for aggregators. The dataset aids in understanding patterns in electrical load curves and the duty cycle of energy storage within an energy community. Furthermore, it can assist in comprehending the impact on distribution networks caused by distributed energy storage. [ABSTRACT FROM AUTHOR]

This paper provides six metaheuristic algorithms, namely Fast Cuckoo Search (FCS), Salp Swarm Algorithm (SSA), Dynamic control Cuckoo search (DCCS), Gradient-Based Optimizer (GBO), Northern Goshawk Optimization (NGO), Opposition Flow Direction Algorithm (OFDA) to efficiently solve the optimal power flow (OPF) issue. Under standard and conservative operating settings, the OPF problem is modeled utilizing a range of objectives, constraints, and formulations. Five case studies have been conducted using IEEE 30-bus and IEEE 118-bus standard test systems to evaluate the effectiveness and robustness of the proposed algorithms. A performance evaluation procedure is suggested to compare the optimization techniques' strength and resilience. A fresh comparison methodology is created to compare the proposed methodologies with other well-known methodologies. Compared to previously reported optimization algorithms in the literature, the obtained results show the potential of GBO to solve various OPF problems efficiently. [ABSTRACT FROM AUTHOR]

To measure the spatial distribution of the magnetic field, both single Hall sensors and arrays of Hall sensors are used. In most cases, a one-dimensional ruler is sufficient. The finished device is called a measuring carriage. The rest of the article describes the first stage of its creation. This article consists in Hall sensor rejection, during which the sensors operate under extreme operating conditions (with increased electrical and thermal loads). During this so-called accelerated aging, the changes in the residual stress values, temperature coefficients, nonlinearity, and divergence of the sensitivity coefficient were monitored. This study can serve as a methodological guide in determining the selection criteria for Hall sensors for precision measuring systems. The need for aging sensors was shown to stabilize their long-term characteristics. In addition, the process of rejecting sensors according to the parameters of interest is described. [ABSTRACT FROM AUTHOR]

In road transport, varying fuel flow rates make it hard to maintain a consistent water ratio in non-surfactant emulsion fuels using the Real-Time Non-Surfactant Emulsion Fuel Supply System (RTES). Thus, it becomes more reasonable to establish an appropriate range of water content tailored to a road condition. Therefore, this study aims to evaluate fuel consumption and exhaust emissions of non-surfactant emulsion fuel in light-duty trucks equipped with RTES, focusing specifically on urban conditions. On-road testing and 300-s idling tests were used as the urban conditions to compare diesel with non-surfactant Water-in-Diesel Emulsion (WiDE) fuel with water percentages from low to high concentrations of water, namely WiDE low%, WiDE med%, and WiDE high%. During idling tests, all emulsion variants reduce fuel consumption. WiDE high% exhibits the most substantial NOx reduction of 9.2%. On-road testing reveals comparable WiDE and diesel fuel consumption, despite the RTES increased electrical load. WiDE high% shows an increment for NOx and CO emissions by 11.71% and 202.19%. In conclusion, a 7.4% to 21.1% water content range was suggested for non-surfactant emulsion fuel in urban road conditions. [ABSTRACT FROM AUTHOR]

The expansion planning of distribution network is based on the implementation information fusion management of distributed generation in distribution network. In order to improve the management and scheduling capability of distributed generation, an implementation information management platform for distributed generation in distribution network based on information integration is proposed, and a comprehensive optimization model for distributed generation and energy storage of distribution network is constructed. The load change from the planning base year to the level year is simulated by Monte Carlo Method; According to the installed capacity of distributed generation, Monte Carlo Method is used to simulate the output of distributed generation, and the planning model of township distribution network expansion is established to meet all power supply needs. The line loss calculation is based on the simulation results of load and distributed generation output. The corresponding steady state model for all kinds of distributed generations is established, and the type of interface connected to the power grid should be confirmed. The information integration method is used to optimize the design of the information management platform for distributed generation in the distribution network. The test shows that the designed implementation information management platform for distributed generation in distribution network can accurately realize the information fusion and management of distributed generation, solve the simultaneity problem caused by the fluctuation and interval of load data and distributed generation output on power flow calculation, and integrate distribution network planning with distributed generation planning. [ABSTRACT FROM AUTHOR]

Food rheology is key to manage the swallowing safety of people affected by swallowing disorders (dysphagia). Simple approaches to assess the flow properties of texture-modified drinks are widely used, but relatively poorly understood. This study focuses on the International Dysphagia Diet Standardisation Initiative (IDDSI) flow test, adopted by caregivers worldwide. This test considers the gravity-driven flow in a vertical syringe. Newtonian liquids and non-Newtonian fluids obtained using a commercial starch-based thickener were considered in this study. An accurate theoretical description of the flow is proposed for Newtonian and power-law fluids considering the effect of fluid properties and of the syringe geometry. A rheological map is proposed, based on the results of several thousand simulations, to capture quantitatively the effect of rheological properties and density on the IDDSI classification, highlighting the important effect of the fluid density which is usually ignored. The sensitivity of the IDDSI results with respect to the syringe outlet diameter is discussed, as well as the different average shear rates at which different IDDSI levels are tested. The rheological map also shows quantitatively that different combinations of the fluid rheological properties and density can result in the same IDDSI classification, suggesting interesting directions for future clinical research. [ABSTRACT FROM AUTHOR]

In today's context of power generation from wind, the demand for reliable and high-performing inverters is on the rise. The quasi-Z-source inverters (q-ZSI) are gaining attention in grid-tied wind power generation systems (WPGS) when compared to conventional inverters for their inherent capability of single-stage power conversion and maximum power point tracking (MPPT) performance. However, the erratic nature of wind speed creates a constant challenge for engineers seeking to optimize the performance of wind turbines (WT). Therefore, this paper suggests a new grey wolf-optimized MPPT (GWO-MPPT) controller for q-ZSI-based grid-tied WPGS, which holds great potential for maximizing the power output of WTs. The conventional MPPT controller used in q-ZSI-based WPGS cannot guarantee stable operation during large variations in wind speed. The GWO-MPPT controller can immediately respond during wind speed variations and trace the maximum power point (MPP) without fluctuations around it, thus improving the MPPT performance. The GWO algorithm estimates the optimum rectifier DC voltage for each wind speed to locate the MPP and subsequently determines the shoot-through duty ratio for the q-ZSI. In addition, this work introduces a decoupling current control strategy supported by dual frequency modulation (DFM) for managing the grid's active and reactive power flows. The functionality of the proposed controllers is rigorously examined using the MATLAB/Simulink tool through extensive simulations. Finally, their practicality is evaluated through an experimental hardware setup. This integrated approach thoroughly investigates the controllers' effectiveness and suitability for practical implementation in WPGS. [ABSTRACT FROM AUTHOR]

This study presents a multi-objective solving indicator-based evolutionary algorithm (IBEA) to solve the optimal power flow (OPF) problem with multiple and competing objectives. The objective functions for the multi-objective OPF (MOOPF) are active power loss, aggregate voltage deviation, total generation cost, and emission pollution. This algorithm combines the shift-based density estimation method with a weighted sum approach to produce a set of non-dominated solutions on each objective space. Moreover, an S-shaped fuzzy membership approach is used to extract the best compromise solution from the obtained non-dominated solutions. To validate the IBEA's performance, standard IEEE 30-bus and IEEE 57-bus test systems with nine different cases are being used. This paper also presents a stochastic optimal power flow problem for two-objective optimization with load demand and wind power uncertainty. [ABSTRACT FROM AUTHOR]

As the percentage of renewable energy source within the energy production mix has expanded, it is become gradually difficult to determine the appropriate values for controller gains and model parameters. Controlling system frequency of interconnected power system during sudden load disturbance requires careful consideration of the controller gain values and small signal stability model parameters. This study proposes an innovative methodology for ascertaining controller gain values and model parameters for plug-in electric vehicles (PEVs) in load frequency control (LFC) applications. Proposed method uses the root locus (RL) approach to find the suitable controller gain values and model parameters for PEVs. This paper offers a thorough mathematical description of the proposed RL approach. Routh approximation method is used for reduced-order modelling (ROM), which comprises thermal and PEV systems, to reduce complexity of higher-order system while designing controllers. Fractional-order proportional-integral-derivative controller (FO-PID) is proposed, and its parameters are adjusted using particle swarm optimization (PSO) tool. To validate the efficacy of suggested method, a comprehensive comparison of time response parameters and performance indices (PI) is carefully carried out. Also, the various PEVs state of charge (SOC) levels is investigated, and effects of these levels are studied in LFC with robustness analysis of controller. The proposed method for determining gain value is highly reliable and efficient, outperforming existing methodologies in the literature. [ABSTRACT FROM AUTHOR]

Power system simulation is vital for designing and evaluating the performance of electrical system protection and control devices. Although several commercial simulators exist, most are expensive and not open-source code. It is essential to develop educational and research simulators that can prepare students, allow researchers to develop new methodologies, and assist system operators in making decisions. This paper aims to present a computer simulation platform called AnaDyn (dynamic analysis), a non-commercial and open-source platform. AnaDyn is an educational/research platform that allows dynamic (transient stability), quasi-steady-state simulation, modal, and power flow analysis. Short- and long-term studies of voltage, frequency, and rotor angle instability problems can be performed. The main characteristics of AnaDyn are quasi-steady-state simulation, in which the equations are not simplified; easy integration with other tools and toolboxes, since AnaDyn is developing in MATLAB; flexible modeling that allows the inclusion of new devices and controller tests. The detailed modeling of various power system devices, the description of the numerical solution method, and the interface are also presented in this work. The platform is validated by comparing a test system's results with those obtained with Brazil's most popular commercial software. The main potentials of quasi-steady-state simulation are also presented. The results indicate that AnaDyn is efficient for educational and research studies on the stability of short- and long-term electrical systems. [ABSTRACT FROM AUTHOR]

Active distribution networks (ADN) may operate in different modes according to the generation demand balance and the capacity of the primary grid for imposing a constant frequency. Conventionally, a customized optimization model is used for each operating mode. Unlike that conventional approach, this article proposes a general optimization model capable of operating the system in three different modes: grid-connected, islanded with a surplus of generation, and islanded with a deficit of generation. Real-time operation is required in this framework with guarantees such as global optimum, uniqueness of the solution, and fast algorithm convergence; for this reason, a convex approach is employed for grid modeling. Numerical experiments demonstrate that the proposed optimization-based operation model can handle the three types of operation while ensuring the safety operation with frequency and voltage within expected limits. [ABSTRACT FROM AUTHOR]

This article proposed an off-board bidirectional battery charger for electric vehicles (EVs) that have been designed to perform various modes of operation of EVs like grid-to-vehicle (G2V) and vehicle-to-grid (V2G) while improving the grid power quality (PQ). During the charging process, the charger operates in the G2V mode. In this mode, power flows from the utility grid to the EV, ensuring a smooth and efficient transfer, which has the ability to maintain a sinusoidal grid current waveform and a unity power factor throughout the operation. This not only ensures efficient power transfer but also minimizes disruptions and distortions in the grid. On the other side of the operational spectrum lies the discharging phase, governed by the V2G mode. This mode allows the charger to deliver power from the EV's batteries back to the utility grid. This capability is a significant step toward establishing a two-way relationship between EVs and the grid, enabling EVs to act as energy reservoirs and support grid stability during peak demand or emergencies. This paper presents the proposed adaptive direct power control (ADPC) theory for the proposed electric vehicle charger (EVC), which consists of a grid-side converter and an EV side converter. The proposed control algorithm on both sides of the converters plays a significant role in various operating modes. The ADPC employs a control strategy referred to as the second-order generalized integrator to estimate the actual synchronizing voltage templates specifically designed for single-phase grid-side converters. Consequently, the appropriate current reference used by the charger is to ensure that the power factor remains unity throughout various operating modes. Similarly, for the EV side converter, the integration of the power delivery error further enhances steady-state responses with the proposed ADPC by generating actual phase shifted pulses for the DAB. The overall performance under similar conditions is studied with virtual direct power control to further check the robustness of the proposed ADPC. The adopted topology and control technique further implemented in the prototype of an off-board EVC to verify the various operating conditions to demonstrate its effectiveness and justified as per the IEEE 519-2022 standard. [ABSTRACT FROM AUTHOR]

The power quality of the grid-coupled photovoltaic system (GCPVS) is enhanced using a control scheme developed by employing a robust generalized soft-root-sign (GSRS) adaptive filter. Power quality (PQ) deterioration issues are common in the distribution grid due to the mix of irregular loads. The GSRS control scheme mitigates harmonics, compensates load unbalances, achieves unity power factor at the grid, and compensates load reactive power, thus leading to balanced sinusoidal currents at the grid side. The GSRS algorithm utilizes a soft-root-sign cost function to achieve less steady-state misalignment while estimating magnitudes of fundamental frequency load current components for PQ improvement. The designed single-stage GCPVS performs smooth power flow between local loads and the grid without necessitating an energy storage system. The control scheme initially meets load demand and then sends any surplus power to the grid. However, if the generated PV power falls below the load demand due to environmental conditions, the additional power for the load is drawn from the grid. Moreover, voltage source converter used in the GCPVS also provides the functionality of DSTATCOM, thus avoiding the usage of additional equipment. The proposed GSRS control scheme and the GCPVS are modeled in MATLAB/Simulink. A laboratory-designed experimental setup is used to carry out the experimental validation of the GSRS control scheme and found to be performing satisfactorily. The behavior of GCPVS is verified during steady-state and dynamic conditions. The GCPVS performance is verified with the standard IEEE-519-2022, and a comparison of the GSRS algorithm with other filtering algorithms is provided. [ABSTRACT FROM AUTHOR]

Distributed energy generation refers to producing energy at the macro-scale by employing renewable and nonrenewable distributed energy generation (DEG) resources. The hybridization of energy sources is required to ensure cost effective and regulated supply to loads. However, to have voltage regulation in the power system, the hybrid renewable sources are integrated with the hybrid storage system. The power flow in a hybrid storage system concerning battery and superconducting magnetic energy storage (SMES) is controlled by Aquila Optimizer (AqO). Moreover, the optimal power flow to the load is maintained using a Pyramid squeeze attention-based ResNet (PSA-ResNet) controller. The fire hawk optimization (FHO) algorithm tunes the hyperparameters of the proposed PSA-ResNet controller. The simulation is performed through MATLAB/Simulink software to determine the efficiency of a proposed controller. The characteristics response for the parameters such as voltage, current and power for ON and OFF grid modes are analysed. The result clearly shows that the proposed method provides high stability with a reduced THD of 1.2%. [ABSTRACT FROM AUTHOR]

The increasing integration of distributed energy resources such as photovoltaic (PV) systems into distribution networks introduces intermittent and variable power, leading to high voltage fluctuations. High PV integration can also result in increased terminal voltage of the network during periods of high PV generation and low load consumption. These problems can be solved by optimal utilization of the reactive power capability of a smart inverter. However, solving the optimization problem using a detailed mathematical model of the distribution network may be time-consuming. Due to this, the optimization process may not be fast enough to incorporate this rapid fluctuation when implemented in real-time optimization. To address these issues, this paper proposes a co-simulation-based optimization approach for optimal reactive power control in smart inverters. By utilizing co-simulation, the need for detailed mathematical modeling of the power flow equation of the distribution network in the optimization model is eliminated, thereby enabling faster optimization. This paper compares three optimization algorithms (improved harmony search, simplicial homology global optimization, and differential evolution) using models developed in OpenDSS and DigSilent PowerFactory. The results demonstrate the suitability of the proposed co-simulation-based optimization for obtaining optimal setpoints for reactive power control, minimizing total power loss in distribution networks with high PV integration. This research paper contributes to efficient and practical solutions for modeling optimal control problems in future distribution networks. [ABSTRACT FROM AUTHOR]

Natural disasters, which are the major causes of large-scale power outages, have become far more common in recent decades. Therefore, this study evaluates the importance and relevance of optimal Tie-line construction in improving the service restoration performance of power distribution systems following high-impact, low-probability occurrences. In doing so, an improved genetic algorithm (GA) is developed to find the optimal locations for the construction of new Tie-lines in distribution systems. In particular, the restoration process of distribution systems, including fault isolation and system reconfiguration, is planned to deploy Tie-lines in the most appropriate locations to improve distribution system resilience and reliability according to considerations related to the costs of constructing new Tie-lines. To validate the effectiveness of the proposed model, it is tested and verified on the RBTS standard network and the results of the proposed approach have been compared with other optimization algorithms such as particle swarm optimization, gravitational search (GS) and shuffled frog leaping. Examining the simulation results shows the efficiency of the proposed performance compared to other evolutionary methods. For instance, the value of the total cost function resulting from the proposed GA in the first and third scenarios has decreased by about $1589 and $946, compared to the GS method, respectively. [ABSTRACT FROM AUTHOR]

Current plans to decarbonize the electric supply system imply that the generation from wind and solar sources will grow substantially. This growth will increase the uncertainty of system operations due to the inherent variability of these renewable sources, and as a result, more reserve capacity will be required to provide the ramping (flexibility) needed for reliable operations. This paper assumes that all of the increased uncertainty comes from wind farms on the grid, and it shows how distributed storage managed locally by aggregators can provide the ramping needed without introducing a separate market for flexibility. This can be accomplished when the aggregators minimize the expected daily cost of the energy purchased from the grid for their customers by submitting optimal bids into the wholesale market with high and low price thresholds for discharging and charging the storage. This model is illustrated using a stochastic multi-period security constrained optimal power flow together with realistic data for a reduction of the network in the Northeast Power Coordinating Council region of the United States. The results show that the bidding strategy for distributed storage provides ramping to the grid just as effectively as storage managed by a system operator. [ABSTRACT FROM AUTHOR]

The flexible distribution network presents a promising architecture to accommodate highly integrated distributed generators and increasing loads in an efficient and cost-effective way. The distribution network is characterised by flexible interconnections and expansions based on soft open points, which enables it to dispatch power flow over the entire system with enhanced controllability and compatibility. Herein, we propose a multi-resource dynamic coordinated planning method of flexible distribution network that allows allocation strategies to be determined over a long-term planning period. Additionally, we establish a probabilistic framework to address source-load uncertainties, which mitigates the security risks of voltage violations and line overloads. A practical distribution network is adopted for flexible upgrading based on soft open points, and its cost benefits are evaluated and compared with that of traditional planning approaches. By adjusting the acceptable violation probability in chance constraints, a trade-off between investment efficiency and operational security can be realised. Flexible distribution networks with soft open points present a promising way to accommodate distributed generators and increasing loads. Here, authors present a multi-resource dynamic coordinated planning method, allowing allocation strategies to be determined over long-term planning periods. [ABSTRACT FROM AUTHOR]

Bidirectional power flow based smart grid system is implemented in the Distributed Generation (DG) sources using Renewable Energy Generators (REG) like solar, wind, etc. Moreover, the unsuitable connection of a load to a grid and DGs can reduce Power Quality (PQ) and bidirectional power flow. Consequently, the existing power generation system has a limited number of power-generation sources, which are linked as millions of end consumers as well as transmission grid. Also, power generation sources has small controller performance over power loss from producing plants to end customers. Moreover, the power-generation sources are having injection points to transfer the power. In the current and future conditions, power systems must accommodate more power from renewable energy sources, be capable of handling bidirectional power flow with distributed generation, and use automatic metering infrastructure, phasor measurement units, power quality conditioners, electric vehicle charging infrastructure, cyber security, and so on.In recent times, the renewable energy-based distribution system is a challenging task in the Battery Energy Storage System (BESS). However, in many cases, power loss, and harmonic moderation are significant issues. Hence in this research, a novel Intelligent Neural Herd Based Water Drop Optimization (INH-WDO) is proposed to control the bidirectional power flow of the converter. The simulation of these proposed methods is actualized in MATLAB platform; subsequently, the projected performance results such as power loss (0.2MV), THD (2.5%) are compared with existing control techniques for proving the significance of the developed controller design. Moreover, the system efficiency in terms of power quality is based on power loss minimization while reducing the Total Harmonic Distortion (THD). [ABSTRACT FROM AUTHOR]