Search

The propagation characteristics of magneto-quasistatic waves, more commonly, known as magnetostatic waves in a long, metallic rectangular waveguide filled with a metamaterial slab are comprehensively investigated. The metamaterial slab consists of split-ring resonators as an anisotropic uniaxial medium with transversal negative effective permeability. Some analytical relations and numerical validations on the characteristics of these waves are presented. The results include the dispersion relations, mode patterns (field distributions) that can be supported by such media, and their corresponding cutoff frequencies, group velocities, power flows, and storage energies of magnetostatic waves. The findings from the present research study can be advantageous to advance the synthesis and development of negative permeability materials with peculiar features in guiding structures. [ABSTRACT FROM AUTHOR]

Epoxy insulators in gas-insulated power apparatus are subjected to the combined effects of electrical and mechanical loads. In this work, a simulation model is built based on the energy theory to explore the electrical tree growth of epoxy resin under tensile and compressive stresses. With increasing AC voltage, the electrical tree growth is promoted, exhibiting a morphology with more branches. Tensile stress accelerates the electrical tree growth, while proper compressive stress has the opposite effect. However, when the compressive stress exceeds a certain value, electrical tree growth is promoted again. When the mechanical stress is vertical to the needle electrode, these effects primarily impact the length of the trees. Conversely, in parallel cases, mechanical stress mainly affects the width of the electrical trees. Filler doping play the role of obstacles as well as enhancing the electric field concentration, the electrical tree growth is firstly inhibited and then promoted as the doping content increases. The electrical tree morphologies of simulation and experiment are in good consistency, proving the reasonability of the simulation model. [ABSTRACT FROM AUTHOR]

The flexible traction power supply system (FTPSS), integrating power flow controller (PFC), energy storage system, and photovoltaic (PV) system, is able to cancel the neutral section, realize the collaborative operation of multiple traction substations (MTSs), and enhance the efficient utilization of regenerative braking energy (RBE) and renewable energy in electrified railways. However, co‐dispatching energy among MTSs influences the power flow distribution within the traction network, impacting the traction network voltage (TNV). Maintaining optimal energy dispatch while ensuring compliance with TNV standards is critical. Therefore, a two‐stage energy optimal dispatch and control strategy is proposed for FTPSS here. The first stage develops a synergistic energy optimal dispatch model for MTSs considering TNV constraint and power flow constraints, aimed at maximizing the utilization of RBE and PV while minimizing the electricity cost of FTPSS. In the second stage, a control strategy for multiple converters is designed to ensure the dynamic response of FTPSS. Finally, the effectiveness of the proposed strategy and model is substantiated through field load data and experimental validation, and the electricity cost of the FTPSS is reduced by about 21.4%, meanwhile the TNV is maintained in the range of 22.5–29 kV. [ABSTRACT FROM AUTHOR]

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]

ABSTRACT This paper proposes a novel approach for the optimal allocation of a depot for electric buses (EBs) charging in a specific transit service, considering the impact on the power system. The main objective is to minimize the total cost, achieved by minimizing the cost of the new cables connecting the depot station to the distribution system and the upgrade cost of existing lines to meet the additional loads. The outcomes are the optimal location of the depot, the optimal electric node bus in the distribution system to supply it, and the required system upgrades. The optimization problem is formulated as a Mixed Integer Nonlinear Programming (MINLP) model and solved using the General Algebraic Modeling System (GAMS). The methodology is tested on the H16 EB service line in Barcelona, Spain, and a typical electrical distribution system. Three case studies are presented in this paper. In the first case, the impact of a single service on the distribution network is analyzed, and in the second case, three H16 EB services are assumed to serve the network. The third case handles a multi‐route, multi‐terminal bus service to allocate and supply a depot capable of accommodating all the routes. This case will also include sensitivity analysis to test the robustness and reliability of the model. Results show that for a single H16 EB service, no line upgrades were needed, and the total cost per phase was $120,000. For three H16 EB services, three lines required upgrades, and the total cost per phase increased to $718,000. In the third case, the sensitivity analysis revealed that higher demand factors lead to increased costs due to more update requirements and voltage deviations. The results demonstrate that the minimum distance between the depot and node is not always the optimal or feasible solution that would prevent the depot load installation at a weak spot and meet the power flow constraints. [ABSTRACT FROM AUTHOR]

Accurate line parameters are critical for and dispatch in distribution systems. External operating condition variations affect line parameters, reducing the accuracy of state estimation and power flow calculations. While many methods have been proposed and obtained results rather acceptable, there is room for improvement as they don't fully consider line connections in known topologies. Furthermore, inaccuracies in measurement devices and data acquisition systems can introduce noise and outliers, impacting the reliability of parameter identification. To address these challenges, we propose a line parameter identification method based on Graph Attention Networks and Multi-gate Mixture-of-Experts. The topological structure of the power grid and the capabilities of modern data acquisition equipment are utilized to capture. We also introduce a multi-task learning framework to enable joint training of parameter identification across different branches, thereby enhancing computational efficiency and accuracy. Experiments show that the GAT-MMoE model outperforms traditional methods, with notable improvements in both accuracy and robustness. [ABSTRACT FROM AUTHOR]

ABSTRACT Bi‐directional power exchange between the lithium‐ion battery and the grid can be achieved using vehicle‐to‐grid (V2G) technology. Bi‐directional battery chargers for V2G applications present opportunities and challenges, as the rapid growth of active components can compromise system reliability. This paper proposes an adaptive control mechanism for a three‐phase bi‐directional battery charging system to address this. The system integrates a bi‐directional active front‐end (AFE) converter (BAC) with an interleaved buck‐boost converter to enable adaptive constant current/constant voltage (CC/CV) mode operation and bi‐directional power flow. A feedforward decoupled current controller and pulse width modulation scheme optimize battery charging, while an optimized voltage‐oriented control (OVOC) model‐based controller ensures stable bi‐directional power exchange between the electric vehicle (EV) and the utility. Experimental results validate the effectiveness of the proposed approach, with findings demonstrating low total harmonic distortion (THD), unity power factor (UPF), and minimal DC voltage ripple. A 12.5 kW prototype further confirms the practical viability of the design, offering a promising solution for enhanced reliability and performance in V2G battery chargers. [ABSTRACT FROM AUTHOR]

The aim of this paper is to present mathematical models of all the propulsion components of hybrid drivetrains. These models are based on very limited amounts of data, nevertheless they match real characteristics with high accuracy based on numerous measurement data points, thus they allow the modelling process of fuel and energy consumption for a particular vehicle in any homologation driving cycle to be performed in a very short time at the early stage of vehicle design. The characteristics provided in the article concern: the full load engine torque curve, full load electric motor power curve, combustion engine efficiency characteristic, including dynamic states and warm-up period, electric motor efficiency characteristic, inverter efficiency characteristic, battery characteristics in a wide temperature range and efficiency of the drivetrain. Moreover, the main principles concerning gear ratio selection are provided to conduct subsequent steps of transmission modelling from scratch. Simulations of drivetrain performance and vehicle fuel consumption in WLTP and US06 tests prove the suitability of the presented models. [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]

This paper presents our initial research findings on distribution system operator (DSO) functions in South Korea. The project team includes the Korean Electric Power Corporation, which is the only public utility company in South Korea. The planned development is based on a real distribution network and real‐world environments. The project encompasses DSO–transmission system operator coordination and management of real‐time distribution network congestion; this paper focuses on the latter aspect. For real‐time congestion management, algorithms that handle all steps from database analysis to data export, including data preprocessing, topological configuration, power flow calculation, sensitivity analysis, and derivation of solutions to congestion, are developed. The distribution system equipment database of Korean Electric Power Corporation is used to develop a network congestion management system. When examining the database, many gaps between the theoretical and practical environments were found which are introduced in this paper. A practical distribution network database for South Korea is developed to narrow these gaps and to implement practical congestion management techniques. The remaining problems are introduced as future works. [ABSTRACT FROM AUTHOR]

ABSTRACT Reverse power flow from a photovoltaic (PV) power generation system to distribution system is limited below a certain limit value. The behavior of a power conditioning system (PCS) for large capacity PV power generation system when the output power approached the output power limit value was experimentally estimated. Under the condition, the PCS became uncontrollable. It was pointed out that this phenomenon was caused by the maximum power point tracking (MPPT) control of the PCS. Furthermore, some methods to avoid the uncontrollable condition of PCS were shown. [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]

ABSTRACT A research study on the three‐port DC–DC converters is done in this article. The proposed converter is a power electronic unit with a low‐voltage, high‐voltage, and battery port. The power of the input ports can be transferred to the output port simultaneously and individually. In this converter, the power can flow bidirectional. In other words, the battery can be charged not only by the low‐voltage port but also by the returned energy from the high‐voltage port. Hence, the proposed converter can operate in step‐up and step‐down modes. Continuous input current, the low voltage stress on semiconductors, and the low count of the devices are other advantages of the converter. These features and the benefits mentioned above make the proposed converter proper for hybrid energy systems. This converter is analyzed in continuous conduction mode, resulting in mathematical equations. To validate these theoretical analyses, a laboratory scale of the converter performs at 50 kHz, 200 W, and 115 V. [ABSTRACT FROM AUTHOR]

With the rapid development of distributed generation (DG) within the framework of modern power systems, accurately assessing the maximum DG hosting capacity in distribution networks is crucial for ensuring the safe and stable operation of the power grid. This paper first introduces an assessment model of maximum DG hosting capacity in distribution network based on optimal power flow (OPF). Then, a two-step method that combines the linearization method and the recursive method is proposed, which consists of two parts: firstly, using linearization method to quickly calculate the preliminary assessment value of maximum DG hosting capacity, and then using a recursive method to accurately correct the preliminary assessment value. Additionally, the proposed improved comprehensive sensitivity index and safety constraint verification method can enhance the computational efficiency and accuracy of the recursive algorithm. Finally, the proposed methods were simulated and validated on the IEEE 33-bus system. [ABSTRACT FROM AUTHOR]

Strategy for energy development of Russia for the period up to 2035 provides for the participation of nuclear power plants with generation 3+ power units in regulating the daily unevenness of the electrical load, which forces the nuclear power plant to operate in an ineffective unloading mode. In order to solve this problem, a combination of nuclear power plants with a hydrogen complex is being considered, when during the hours of supposed unloading of the nuclear power plant due to the electrolysis of water, unclaimed power is converted into hydrogen and oxygen, and during peak load hours, hydrogen is burned in an oxygen environment in order to heat/overheat the working fluid in the steam turbine cycle Nuclear power plants and peak power generation. At the same time, the safety issues of using hydrogen when burned in oxygen are of great importance, which is, first of all, due to the inevitable presence of a certain amount of chemical underburning, which creates the risk of the formation of an explosive hydrogen-oxygen mixture along the working fluid path in the steam turbine cycle of a nuclear power plant. Based on the existing experience of the authors, a method for assessing the underburning of hydrogen and an indicator of the efficiency of recirculation in the proposed scheme using the recycling of unreacted hydrogen have been developed; a theoretical assessment of the underburning of hydrogen has been previously performed. In addition, a small-scale experimental installation has been developed that allows simulating the conditions of combustion of hydrogen in oxygen with recycling of unreacted hydrogen under the conditions of the steam turbine cycle of a nuclear power plant. This work represents a new approach to solving the problem of the safe use of hydrogen. The developed method makes it possible to determine specific concentrations of unreacted hydrogen depending on the flow rate and pressure in the flame tube. Based on the proposed indicator of recycling efficiency, it is shown that the proportion of hydrogen entrainment due to its low solubility in water is very small, which, at the accepted pressure and temperature of recycled unreacted hydrogen, determines a sufficiently high indicator of recycling efficiency. As a preliminary theoretical assessment has shown, the magnitude of the recirculation efficiency depends on the pressure and temperature at which unreacted hydrogen is recycled, which will obviously require further assessments over a wider range of pressures and temperatures. The proposed experimental methodology will make it possible to perform a reasonable assessment of the efficiency of recirculation of unreacted hydrogen for conditions of additional heating of feedwater in the steam turbine cycle of a nuclear power plant. [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]

This paper presents an approximate power flow model‐based forecasting‐aided state estimation estimator for power distribution networks subject to naive forecasting methods and nonlinear filtering processes. To this end, this estimator designs a voltage perturbation vector around the priori‐determined nominal value as the dynamic state variable, which enables more detailed depictions of voltage changes. Then, a state transition model incorporating nodal power variation is derived from the approximate power injection model. The constant state transition matrix working on power variations only consists of nodal impedance, which reduces the extensive parameter tuning effort when facing different estimation tasks. Furthermore, an approximate branch power flow observation equation is proposed to improve the filtering efficiency. The observation matrix with branch admittance information presents the linear filtering relationship between power flow measurements and forecasted states, omitting the complex iterative updates of the Jacobian matrix for nonlinear measurements. Finally, the overall estimated voltage state at each time sample is entirely obtained by combining the filtered voltage perturbation vector with the priori‐determined nominal value. Numerical simulation comparisons on a symmetric balanced 56‐node distribution system verify the performance of the proposed estimator in terms of accuracy and robustness under normal and abnormal conditions. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of the operational characteristics of three-phase AC–DC matrix converters, which operate as rectifiers but possess a range of additional capabilities. These include the ability to reverse the power flow, control the input power factor, and generate a multilevel output voltage. The analysis yields a modulation strategy that minimizes switching losses. The proposed modulation strategy is compared with seven well-known space vector modulation strategies in terms of efficiency and distortion of input and output current. The performance is experimentally validated. [ABSTRACT FROM AUTHOR]

Accurate identification of network topology and line parameters is essential for effective management of distribution systems. An innovative joint estimation method for distribution network topology and line parameters is presented, utilizing a power flow graph convolutional network (PFGCN). This approach addresses the limitations of traditional methods that rely on costly voltage phase angle measurements. The node correlation principle is applied to construct a node correlation matrix, and a minimum distance iteration algorithm is proposed to generate candidate topologies, which serve as graph inputs for the parameter estimation model. Based on the topological dependencies and convolutional properties of AC power flow equations, a PFGCN model is designed for line parameter estimation. Parameter refinement is achieved through an alternating iterative process of pseudo-trend calculation and neural network training. Training convergence and loss function values are used as feedback to filter and validate candidate topologies, enabling precise joint estimation of both topologies and parameters. The proposed method's accuracy, transferability, and robustness are demonstrated through experiments on the IEEE-33 and modified IEEE-69 distribution systems. Multiple metrics, including MAPE, IAE, MAE, and R2, highlight the proposed method's advantages over Adaptive Ridge Regression (ARR). In the C33 scenario, the proposed method achieves MAPEs of 4.6% for g and 5.7% for b, outperforming the ARR method with MAPEs of 7.1% and 7.9%, respectively. Similarly, in the IC69 scenario, the proposed method records MAPEs of 3.0% for g and 5.9% for b, surpassing the ARR method's 5.1% and 8.3%. [ABSTRACT FROM AUTHOR]

Summary: This paper outlines the development of an interleaved bidirectional DC–DC converter featuring single‐phase high‐frequency isolation and employing two distinct pulse width modulations (PWMs), one for each power flow direction. The analysis of converter stages, expressions for DC voltage gains, and voltage/current stresses in continuous conduction mode are presented. The proposed converter offers several advantages, including a new modulation approach, a current ripple frequency, and voltage in two ports that are four times higher than the switching frequency. Additionally, it entails a reduction in the volume and weight of passive elements and ensures balanced current distribution between the arms. Moreover, the single‐phase transformer offers galvanic isolation between the DC bus and the battery or supercapacitor. An experimental prototype has been implemented in the laboratory to validate the mathematical and theoretical analysis for both energy flow directions under the test conditions, with a converter‐rated power of 2.5 kW, a low voltage side of 180 V, and a high voltage side of 380 V. [ABSTRACT FROM AUTHOR]

Copyright of Scientia Iranica. Transaction D, Computer Science & Engineering & Electrical Engineering is the property of Scientia Iranica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study investigates the use of automated machine learning to forecast the demand of electrical loads. A stochastic optimization algorithm minimizes the cost and risk of the traded asset across different markets using a generic framework for trading activities of load portfolios. Assuming an always overbought condition in the Day-Ahead as well as in the Futures Market, the excess energy returns without revenue to the market, and the results are compared with a standard contract in Greece, which stands as the lowest as far as the billing price is concerned. The analysis achieved a mean absolute percentage error (MAPE) of 12.89% as the best fitted model and without using any kind of pre-processing methods. [ABSTRACT FROM AUTHOR]

In a power grid with multiple two-state loads, the total power can vary over a significant range. This results in the inability to supply this system from a low-power source. The solution is an algorithm that shapes energy demand depending on its availability. For this purpose, a new load distribution method is proposed based on introducing a buffer between the temperature controller output and the heater and filtering the load using a master Proportional–Integral (PI) controller. The aim of the work was to evaluate the quality of the developed algorithm for limiting power peaks in the power grid. The research was conducted on a model of the Creep Test Laboratory with 389 heaters simulated in MATLAB Simulink R2023b. The algorithm was tested with various settings of the master controller parameters. By experimentally adjusting these parameters, a ten-fold reduction in peak power was achieved. The standard deviation for the L1 phase was reduced from 7.6 kW to 0.6 kW. Similar results were obtained for phases L2 and L3. The tested control system tracked changes in the average power value by changing the number of loads switched on and by frequency-modulating the signal when the change was less than the power of a single load. It was demonstrated that the controlled delayed switching of electrical loads can modify the shape of the total electric power without affecting their operation. The proposed solution features a low computational complexity, which allows its implementation in various systems. [ABSTRACT FROM AUTHOR]

In the context of the increasingly present policies of circular economy and the emergence of "Circular Bioeconomy" (CB), this article presents the results of a literature review on the sociometabolic research of biomasses. Six schools of thought are identified and distinguished according to their authors, their conceptions of metabolism, methodologies and social and spatial dimensions. Based on this state of the art, we propose an analytical framework that combines quantitative and qualitative approaches at the territorial scale, drawing upon the currents of Territorial Ecology and Political Ecology. This framework enhances our understanding and provides a critical perspective on the geography of CB. We compare discourses to actual practices by analysing biomass flows and power relations while adopting a critical perspective toward circular economy policies. [ABSTRACT FROM AUTHOR]

The design of renewable energy systems traditionally emphasizes life cycle costs, often focusing primarily on emissions rather than a comprehensive life cycle impact assessment. This research proposes a four‐tier methodology to balance cost‐effectiveness and sustainability in the electrification of remote areas. Tier 1 focuses on understanding the community context by analyzing electrical load profiles, meteorological data, and component specifications for microgrid design. Tier 2 evaluates the feasibility of various systems, optimizing them through cost analysis and Multi‐Criteria Decision‐Making (MCDM) to rank alternatives. Tier 3 assesses environmental impacts using life cycle assessment, ranking alternatives based on environmental criteria. Tier 4 integrates cost and environmental rankings to determine the most suitable energy configurations, followed by sensitivity analysis to ensure robust decision‐making. The methodology is validated through a case study of an unelectrified remote community, demonstrating that the PV‐Wind Turbine‐Biomass Generator‐Converter configuration is the most robust alternative, proving to be the optimal choice in 50% of the analyzed scenarios, achieving a Cost of Energy of 0.213 USD/kWh while minimizing environmental impact across all 18 criteria considered over a 25‐year life cycle. This novel framework offers a scalable approach to designing renewable energy systems, enhancing sustainable electrification efforts in developing regions. [ABSTRACT FROM AUTHOR]

The aim of this research is to evaluate the performance of the distribution network by connecting wind distributed generation (DG) and determining the optimal location and size using the particle swarm optimization (PSO) technique, once the wind DG is connected at the optimal location, the output of wind turbines is not constant but varies with changes in wind speed. Wind turbines are designed to generate the energy from the wind. As the output of the wind turbines changes, it influences the power flow and voltage levels in the distribution network. The injection of power from the wind turbines can cause variations in voltages within the distribution network. Additionally, the changing power flow may contribute to power losses in the distribution system. In this paper, the voltages and active power losses are evaluated with the change in wind speed for the IEEE 15 Bus system by conducting load flow analysis in MATLAB. The results reveal optimized solutions that contribute to reduced power losses, increased renewable energy generation, and improved voltage profiles. This research underscores the potential of PSO-based optimization in conforming more efficient and sustainable distribution networks. [ABSTRACT FROM AUTHOR]

In a hydrokinetic turbine, the flow completely surrounds the turbine allowing it to convert the kinetic energy of the water to mechanical power. The general objective of this work is the numerical study of a low power axial flow hydroelectric turbine using CFD (Computational Fluid Dynamics). The scope of this work includes a CFD analysis of a hydroelectric turbine, including the rotor, hub and casing, a comparison between results of steady and transient solvers, and the turbine performance in design and off-design conditions. Three-dimensional simulations were carried out using ANSYS Fluent software, with the aim of estimating the design loads and determining the nominal operating conditions, including results of steady and transient simulations. Results were obtained with the k-ω SST turbulence model and the SIMPLEC calculation algorithm, in structured and unstructured meshes, which involve stationary and rotating domains. [ABSTRACT FROM AUTHOR]

Recently, the dependency on renewable energy sources (RES) has increased. This is due to several reasons, including reducing environmental harm and lowering production costs. The RES in nature is variable and intermittent, such as the wind turbine (WT), which depends on the wind speed, so several challenges are faced in integrating (RES) with power systems. This article used the optimal power flow (OPF) to solve these challenges with a hybrid optimization algorithm, combining the conventional differential evolution (DE) and particle swarm optimization (PSO) this hybrid method called differential evaluation-particle swarm optimization (DEPSO), with thyristor-controlled series compensators (TCSC). This approach gives better results than conventional algorithms such as particle swarm optimization (PSO), differential evaluation (DE), and artificial bee colony (ABC). The constraints of the power system such as the reactive power of the generator or voltage of the load bus are retained in their limits. The objective functions (OF) discussed here are active power losses (MW), voltage deviation (VD) (p.u.), and thermal generation fuel cost ($/h). The DEPSO reduced the active power losses by 49.98%, voltage deviation by 91.02%, and generation cost by 11.36%. This article uses the reactance of the TCSC and the wind turbine bus magnitude voltage as control variables in OPF. This paper has tested the approach with an IEEE 30 bus with two WT farms. [ABSTRACT FROM AUTHOR]

The increasing penetration of distributed photovoltaic (PV) brings challenges to the safe and reliable operation of distribution networks, distributed PV access to the grid changes the characteristics of the traditional distribution grid. Therefore, the assessment of distributed PV carrying capacity is of great significance for distribution network planning. To this end, a differentiated scenario-based distributed PV carrying capacity assessment method based on a combination of Convolutional Neural Networks (CNN) and Gated Recurrent Unit (GRU) is proposed. Firstly, the meteorological characteristics affecting PV power are quantitatively analyzed using Pearson's correlation coefficient, and the influence of external factors on PV power characteristics is assessed by integrating the measured data. Then, for the problem of high blindness of clustering parameters and initial clustering centers in the K-means clustering algorithm, the optimal number of clusters is determined by combining the cluster Density Based Index (DBI) and hierarchical clustering. The improved K-means clustering method reduces the complexity of massive scenarios to obtain distributed PV power under differentiated scenarios. On this basis, a distributed PV power prediction method based on the CNN-GRU model is proposed, which employs the CNN model for feature extraction of high-dimensional data, and then the temporal feature data are optimally trained by the GRU model. Based on the clustering results, the solution efficiency is effectively improved and the accurate prediction of distributed PV power is realized. Finally, taking into account the PV access demand of the distribution network, combined with the power flow calculation of distribution network, the bearing capacity of distribution network considering node voltage in differentiated scenarios is evaluated. In addition, verified by source-grid-load measured data in IEEE 33-bus distribution system. The simulation results show that the proposed CNN-GRU fusion deep learning model can accurately and efficiently assess the distributed PV carrying capacity of the distribution network and provide theoretical guidance for realizing distributed PV access on a large scale. [ABSTRACT FROM AUTHOR]

In distribution networks with distributed generators (DGs), power generation and load demand exhibit increased randomness and volatility, and the line parameters also suffer more frequent fluctuations, which may result in significant state shifts. Existing model-driven methods face challenges in efficiently solving uncertain power flow, especially as the size of the system increases, making it difficult to meet the demand for rapid power flow analysis. To address these issues, this paper proposes an SVR-based interval power flow (IPF) prediction method for distribution networks with DGs integration. The method utilizes intervals to describe system uncertainty and employs Support Vector Regression (SVR) for model training. The input feature vector consists of the intervals of active power generation, load demand, and line parameters, while the output feature vector represents the intervals of voltage or line transmission power. Ultimately, the SVR-based IPF prediction model is established, capturing the linear mapping relationship between input data and output IPF variables. Simulation results demonstrate that the proposed method exhibits high prediction accuracy, strong adaptability, and optimal computation efficiency, meeting the requirements for rapid and real-time power flow analysis while considering the uncertainty in distribution networks with DGs integration. [ABSTRACT FROM AUTHOR]

With the increasing penetration of electric vehicles (EVs) in road traffic, the spatial and temporal stochasticity of the travel pattern and charging demand of EVs as a mode of transportation and an electrical load have generated different degrees of congestion impacts on both the power grid and the transportation network. Based on this, this paper proposes a power–transportation-coupled distribution network optimization strategy based on stochastic user traffic equilibrium theory. First, a stochastic user equilibrium-mixed traffic flow allocation model that expresses user non-completely rational path selection behavior is established, and the traffic flow equilibrium solution is obtained using an improved method of successive algorithm and mapped to charging loads. Second, a distribution network power flow optimization model under the coupled power–transportation architecture is established to optimize the operation state of the distribution network by combining distributed resources such as energy storage, demand response loads, wind power, photovoltaic, and gas turbines. [ABSTRACT FROM AUTHOR]

In the background of the global commitment to low-carbon renewable energy transformation, new energy sources such as distributed wind–solar storage and charging and single-phase loads, such as electric vehicles, are connected to the distribution network. Due to the asymmetric access of load and the randomness of load power consumption, the problem of three-phase imbalance is becoming more and more prominent, and the uncertainty is increasing. In this paper, by analyzing the three-phase load imbalance problem of the distribution network, starting from the cause of three-phase load imbalance, the three-phase imbalance calculation method of the distribution network is deduced. The access mode of a distributed power flow controller in the distribution network and the three-phase imbalance compensation method are studied. The control strategy of the distributed power flow controller for the three-phase imbalance control of the distribution network is proposed. The simulation results show that the proposed method can effectively control the three-phase asymmetry problem of the distribution network, the configuration is more flexible, and the capacity utilization rate is higher. [ABSTRACT FROM AUTHOR]

To efficiently utilize the power generated by a photovoltaic (PV) system, integrating it with an energy storage system (ESS) is essential. Furthermore, maximizing the economic benefits of such PV‐ESS integrated systems requires selecting the optimal capacity and performing optimal energy operation scheduling. Although many studies rely on rule‐based energy operation scheduling, these methods prove inadequate for complex real‐world scenarios. Moreover, they often focus solely on determining the ESS capacity to integrate into existing PV systems, thereby limiting the possibility of achieving optimal economic benefits. To address this issue, we propose an optimal energy operation scheduling and system sizing scheme for a PV‐ESS integrated system based on metaheuristic algorithms. The proposed scheme employs a zero‐shot PV power forecasting model to estimate the potential power generation from a planned PV system. A systematic analysis of the installation, operation, and maintenance costs is then incorporated into the economic analysis. We conducted extensive experiments for comparing economic benefits of various scheduling methods and capacities using real electrical load data collected from a private university in South Korea and estimated PV power data. According to the results, the most effective metaheuristic algorithm for scheduling is simulated annealing (SA). Additionally, the optimal PV system, battery, and power conversion system capacities for the university are 13,000 kW each, 10% of the PV system capacity, and 60% of the battery capacity, respectively. The estimated annual electricity tariff calculated from the data used in the experiment is $3,315,484. In contrast, SA‐based scheduling in the optimal PV‐ESS integrated system achieved annual economic benefits of $875,000, an improvement of approximately 7% over rule‐based scheduling of $817,730. [ABSTRACT FROM AUTHOR]

Active distribution networks (ADNs) are consistently being developed as a result of increasing penetration of distributed energy resources (DERs) and energy transition from fossil‐fuel‐based to zero carbon era. This penetration poses technical challenges for the operation of both transmission and distribution networks. The determination of the active/reactive power capability of ADNs will provide useful information at the transmission and distribution systems interface. For instance, the transmission system operator (TSO) can benefit from reactive power and reserve services which are readily available by the DERs embedded within the downstream ADNs, which are managed by the distribution system operator (DSO). This article investigates the important factors affecting the active/reactive power flexibility area of ADNs such as the joint active and reactive power dispatch of DERs, dependency of the ADN's load to voltage, parallel distribution networks, and upstream network parameters. A two‐step optimization model is developed which can capture the P/Q flexibility area, by considering the above factors and grid technical constraints such as its detailed power flow model. The numerical results from the IEEE 69‐bus standard distribution feeder underscore the critical importance of considering various factors to characterize the ADN's P/Q flexibility area. Ignoring these factors can significantly impact the shape and size of Active Distribution Networks (ADN) P/Q flexibility maps. Specifically, the Constant Power load model exhibits the smallest flexibility area; connecting to a weak upstream network diminishes P/Q flexibility, and reactive power redispatch improves active power flexibility margins. Furthermore, the collaborative support of reactive power from a neighboring distribution feeder, connected in parallel with the studied ADN, expands the achievable P/Q flexibility. These observations highlight the significance of accurately characterizing transmission and distribution network parameters. Such precision is fundamental for ensuring a smooth energy transition and successful integration of hybrid renewable energy technologies into ADNs. [ABSTRACT FROM AUTHOR]

A soft switching non‐isolated bidirectional DC–DC converter with an improved voltage conversion ratio without any additional auxiliary switch is presented in this paper. In the proposed converter, improved step‐up/step‐down gain conversion is achieved by employing the coupled inductors method. Also, the auxiliary circuit provides soft switching conditions for all the semiconductor elements, regardless of the power flow direction and without any extra voltage stress. The other switch helps provide soft switching conditions for the main switch. Moreover, the switch used for providing soft switching conditions operates as a synchronous rectifier as well. The additional circuit added to attain soft switching is composed of an inductor, coupled with the converter's main inductor, and two auxiliary diodes. The auxiliary diodes benefit from zero‐current‐switching conditions. Fully soft switching conditions for all semiconductor devices, removing the reverse recovery problem, and a low number of components have led to mitigating switching losses and improving efficiency. Detailed operating principles and a theoretical analysis of the proposed converter are presented. Also, the experimental results of a 220 W prototype circuit are provided to confirm the validity of the proposed topology. [ABSTRACT FROM AUTHOR]

The present study introduces a nature inspired improved liver cancer algorithm (ILCA) for solving the non‐convex engineering optimization issues. The traditional LCA (t‐LCA) inspires from the conduct of liver tumours and integrates biological ethics during the optimization procedure. However, t‐LCA facing stagnation issues and may trap into local optima. To avoid such issues and provide the optimal solution, there are some modifications are implemented into the internal structure of t‐LCA based on Weibull flight operator, mutation‐based approach, quasi‐opposite‐based learning and gorilla troops exploitation‐based mechanisms to enhance the overall strength of the algorithm to obtain the global solution. For validation of ILCA, the non‐parametric and the statistical analysis are performed using benchmark standard functions. Moreover, ILCA is applied to resolve the stochastic renewable‐based (wind turbines + PVs) optimal power flow problem using a modified RER‐based IEEE 57‐bus. The objective of this work is to obtain the minimum predicted power losses and enhance the predicted voltage stability. By incorporation of renewable resources into the modified IEEE57‐bus network can help the system to reduce the power losses from 5.6622 to 3.8142 MW, while the voltage stability is enhanced from 0.1700 to 0.1164 p.u. [ABSTRACT FROM AUTHOR]

Accurate and fast transient stability assessment (TSA) of power systems is crucial for safe operation. However, deep learning-based methods require long training and fail to simultaneously extract the spatiotemporal characteristics of the transient process in power systems, limiting their performance in prediction. This paper proposes a novel TSA method based on a spatiotemporal graph convolutional network with graph simplification. First, based on the topology and node information entropy of power grids, as well as the power flow of each node, the input characteristic matrix is compressed to accelerate evaluation. Then, a high-performance TSA model combining a graph convolutional network and a Gated Convolutional Network is constructed to extract the spatial features of the power grid and the temporal features of the transient process. This model establishes a mapping relationship between spatiotemporal features and their transient stability. Finally, the focal loss function has been improved to dynamically adjust the influence of samples with different levels of difficulty on model training, adaptively addressing the challenge of sample imbalance. This improvement reduces misclassification rates and enhances overall accuracy. Case studies on the IEEE 39-bus system demonstrate that the proposed method is rapid, reliable, and generalizable. [ABSTRACT FROM AUTHOR]

Coordinated voltage control enables the active management of voltage levels throughout electricity distribution networks by leveraging the voltage support capabilities of existing grid-connected PV inverters. The efficient management of power flows and precise voltage regulation through coordinated voltage control schemes facilitate the increased adoption of rooftop PV systems and enhance the hosting capacity of electricity distribution networks. The research work presented in this paper proposes a coordinated voltage control scheme and evaluates the enhanced hosting capacity utilizing a deep reinforcement learning-based approach. A comparative analysis of the proposed algorithm is presented, and the performance is benchmarked against existing local voltage control schemes. The proposed coordinated voltage control scheme in this paper is evaluated using simulations on a real-world low-voltage electricity distribution network. The evaluation involves quasi-static time series power flow simulations for assessing performance. Furthermore, a discussion is presented that reflects on the strengths and limitations of the proposed scheme based on the results observed from the case study. [ABSTRACT FROM AUTHOR]

This paper introduces an open-source tool capable of performing the Composite System Reliability evaluation developed in the high-level, dynamic Julia™ programming language. Employing Monte Carlo Simulation and parallel computing, the tool evaluates probabilistic adequacy indices for combined generation and transmission systems, focusing on both individual delivery points and the broader system. Proficiency in Optimal Power Flow problem formulations is demonstrated through two distinct methods: DC and linearized AC, enabling comprehensive resource and transmission adequacy analysis with high-performance solvers. Addressing replicability and the insufficiency of available software, the tool supports diverse analyses on a unified platform. The paper discusses the tool's design and validation, particularly focusing on the two optimal power flow problem formulations. These insights significantly contribute to understanding transmission system performance and have implications for power system planning. [ABSTRACT FROM AUTHOR]

In the context of the green energy transition, the rapid expansion of flexible resources such as distributed renewable energy, electric vehicles (EVs), and energy storage has significantly impacted the operation of distribution networks. This paper proposes a multi-objective optimization approach for active distribution networks (ADNs) based on analytical target cascading (ATC). Firstly, a dynamic optimal power flow (DOPF) calculation method is developed using second-order conic relaxation (SOCR) to address power flow and voltage issues in the distribution network, incorporating active management (AM) elements. Secondly, this study focuses on aggregating the power of flexible resources within station areas connected to distribution network nodes and incorporating these resources into demand response (DR) programs. Finally, a two-layer model for collaborative multi-objective scheduling between station areas and the active distribution network is implemented using the ATC method. Case studies demonstrate the model's effectiveness and validity, showing its potential for enhancing the operation of distribution networks amidst the increasing integration of flexible resources. [ABSTRACT FROM AUTHOR]

Accurate electric load forecasting at various time periods is considered a necessary challenge for electricity consumers and generators to maximize their economic efficiency in energy markets. Hence, the accuracy and effectiveness of existing electric load forecasting approaches depends on the data quality. Nowadays, with the implementation of modern power systems and Internet of Things technology, forecasting models are faced with a large volume of data, which puts the security and health of data at risk due to the use of numerous measuring devices and the threat of cyber‐attackers. In this study, a cyber‐resilient hybrid deep learning‐based model is developed that accurately forecasts electric load in short‐term and long‐term time horizons. The architecture of the proposed model systematically integrates stacked multilayer denoising autoencoder (SMDAE) and generative adversarial network (GAN) and is called SMDAE‐GAN. In the proposed framework, SMDAE layer is used to pre‐process and remove real fs and intentional anomalies in data, and GAN layer is also utilized to forecast electric load values. The effectiveness of the SMDAE‐GAN structure is studied using realistic electrical load data monitored in the distribution network of Tabriz, Iran, and meteorological data measured in weather station there. Compared with other conventional load forecasting approaches, the developed framework has the highest accuracy in both cases of using normal data with real‐world noise and damaged data under false data injection attacks. [ABSTRACT FROM AUTHOR]

With the rapid development and wide application of sensing devices and communication networks, the traditional power system has transformed into a cyber physical power system (CPPS) gradually. The constant interaction of information flow and power flow exposes the grid to potential cyber-attack risks. In this paper, the defense strategy selection approaches are proposed based on incomplete information game against false data injection attack (FDIA) in different scenarios. Firstly, the attack-defense tree model containing economic indicators and a measure of attack is established, and the expected revenues of both attacker and defender are calculated in detail. Secondly, the static Bayesian attack-defense game model in which the defender grasps incomplete information is constructed, considering two cases of low-tech attacker and high-tech attacker. Then, the zero-sum attack-defense game model in which the attacker masters incomplete information is structured, considering the changing attack space. Finally, the simulations are carried out on the IEEE 30-bus system, and the optimal defense strategies in the case of two incomplete information are obtained by solving the equilibrium point. The simulation results demonstrate that the proposed method is effective in cyber security defense system, and can provide guiding ideology and theoretical basis for operators to deploy defense measures. [ABSTRACT FROM AUTHOR]

AbstractForecasts of regional electricity net-demand, consumption minus embedded generation, are an essential input for reliable and economic power system operation, and energy trading. While such forecasts are typically performed region by region, operations such as managing power flows require spatially coherent joint forecasts, which account for cross-regional dependencies. Here, we forecast the joint distribution of net-demand across the 14 regions constituting Great Britain’s electricity network. Joint modeling is complicated by the fact that the net-demand variability within each region, and the dependencies between regions, vary with temporal, socio-economic and weather-related factors. We accommodate for these characteristics by proposing a multivariate Gaussian model based on a modified Cholesky parameterization, which allows us to model each unconstrained parameter via an additive model. Given that the number of model parameters and covariates is large, we adopt a semi-automated approach to model selection, based on gradient boosting. In addition to comparing the forecasting performance of several versions of the proposed model with that of two non-Gaussian copula-based models, we visually explore the model output to interpret how the covariates affect net-demand variability and dependencies. The code for reproducing the results in this article is available at https://doi.org/10.5281/zenodo.7315105. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work. [ABSTRACT FROM AUTHOR]

ABSTRACT This work deals with the design and development of a dual source based propulsion architecture for electric vehicles. The converter utilized for the propulsion system is derived from a conventional CuK converter, which operates in a bidirectional power flow mode. The proposed propulsion system has two energy sources: a battery and a supercapacitor (SC). The proposed system has continuous input and output currents at low and high voltage sides, which improves the cycle life of hybrid energy storage (SC and battery) and the DC‐link capacitor. Further, the proposed converter has magnetic reduction compared to a conventional two‐input bidirectional CuK converter. Moreover, an effective and simpler control strategy (only one switch is pulse width modulated (PWM) operated at a time) has been developed for energy management between sources during charging (regenerative braking) and discharging (propulsion), which is effectively verified by simulation and experimental results. Further, a frequency domain (bode plot)‐based technique is used for controller design and stability analysis of the proposed system. [ABSTRACT FROM AUTHOR]

Based on the energy development strategy of Russia until 2035, the article substantiates the prospect for the development of nuclear energy, according to which nuclear plants are planned to be forced to unload in the range of up to 50% of the rated power while participating in the regulation of daily unevenness of the electrical load. In addition, nuclear power plants will be involved in primary frequency regulation, which is also associated with the unloading mode of operation of nuclear power plants. All these circumstances force us to look for ways to provide nuclear power plants with base load. Along with pumped storage power plants, the article considers an alternative option in the form of using a hydrogen complex based on the electrolysis production of hydrogen, which satisfies the concept of its carbon-free production. The article presents the global dynamics of the introduction of electrolyzers, and in addition to the increase in the share of electrolysis hydrogen produced at nuclear power plants. The hydrogen complex in this case is a means of providing the nuclear power plant with a base load, which involves the consumption of unclaimed electricity at cost. This allows hydrogen to be produced at a competitive price compared to individual producers on the market. The resulting hydrogen is in great demand as a useful product in a number of industries. The article provides examples of hydrogen consumption in various areas of consumption depending on purity, taking into account the requirements of GOST. A number of consumer industries use hydrogen of a special degree of purity, i.e., more than 99.999% vol. In this regard, additional hydrogen purification on palladium membranes was taken into account. A comprehensive assessment of the efficiency of the hydrogen production system was carried out, taking into account its additional purification and delivery to the consumer in various ways, mastered and in demand in world practice. A comparison has been made of the methods of delivering hydrogen to the consumer in comparison with the production of hydrogen by electrolysis at the point of consumption. A comparison was made using the criterion of net present value for the option of third-party consumers who do not have their own hydrogen production. [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]