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Based on the Gurtin-Murdoch surface elasticity theory and the magnetoelectroelasticity (MEE) theory, the fracture behaviors of MEE materials containing nanoscale lip-shaped orifice with 2 asymmetric cracks under anti-plane mechanical loads and in-plane electromagnetic loads were investigated with the analytic function conformal mapping technique. Analytical solutions for the generalized MEE stress fields around defects (the lip-shaped orifice and cracks), as well as the crack tip MEE intensity factors and energy release rates, were given. Under special conditions, the obtained results would degenerate into existing results or offer new insights. Numerical examples reveal that, the defect surface effects on the MEE intensity factors are dependent on the radii of nano-sized circular holes, the size of the lip-shaped orifice, the size of secondary cracks originating from the lip-shaped orifice, and the applied MEE loads. Under the surface effect, the dimensionless energy release rate varies with the lip width, the infinity mechanical load, the infinity electrical load and the infinity magnetic load. [ABSTRACT FROM AUTHOR]

In recent years, there has been a noticeable surge in electric power load due to economic development and improved living standards. The growing need for smart power solutions, such as leveraging user electricity data to forecast power peaks and utilizing power data statistics to enhance end-user services, has been on the rise. However, the misuse and unauthorized access of data have prompted stringent regulations to safeguard data integrity. This paper presents a novel decentralized collaborative machine learning framework aimed at predicting peak power loads while protecting the privacy of users' power data. In this scheme, multiple users engage in collaborative machine learning training within a peer-to-peer network free from a centralized server, with the objective of predicting peak power loads without compromising users' local data privacy. The proposed approach leverages blockchain technology and advanced cryptographic techniques, including multi-key homomorphic encryption and consistent hashing. Key contributions of this framework include the development of a secure dual-aggregate node aggregation algorithm and the establishment of a verifiable process within a decentralized architecture. Experimental validation has been conducted to assess the feasibility and effectiveness of the proposed scheme, demonstrating its potential to address the challenges associated with predicting peak power loads securely and preserving user data privacy. [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]

In order to avoid the occurrence of unstable phenomena in the Power Grid (PG), it is necessary to have a thorough understanding of the current operating status of the PG. Research on voltage stability evaluation indicators and algorithms in the power system can assist power system operators in making effective decisions. In this paper, an improved continuous power flow scheme based on Lagrange quadratic interpolation was adopted. Secant method and Lagrange quadratic interpolation method were used for prediction comparison, and Newton method was used to correct the predicted results. This article utilized an online voltage safety assessment and warning model to ensure the safe operation of the PG and reduce the occurrence of faults. To ensure the integrity and accuracy of network dynamic information, a least squares fitting method based on orthogonal functions was adopted to improve the fitting accuracy without increasing communication volume. As the step size increases from 2 to 3, the number of power flow solutions decreased and the solution time increased. The effectiveness of the voltage stability evaluation algorithm in this paper was verified through simulation of two node systems: New England 39 and IEEE118. [ABSTRACT FROM AUTHOR]

This paper presents a new optimization technique based on the hybridization of two meta-heuristic methods, Jellyfish Search (JS) and Moth Flame Optimizer (MFO), to solve the Optimal Power Flow (OPF) problem. The JS algorithm offers good exploration capacity but lacks performance in its exploitation mechanism. To improve its efficiency, we combined it with the Moth Flame Optimizer, which has proven its ability to exploit good solutions in the search area. This hybrid algorithm combines the advantages of both algorithms. The performance and precision of the hybrid optimization approach (JS-MFO) were investigated by minimizing well-known mathematical benchmark functions and by solving the complex OPF problem. The OPF problem was solved by optimizing non-convex objective functions such as total fuel cost, total active transmission losses, total gas emission, total voltage deviation, and the voltage stability index. Two test systems, the IEEE 30-bus network and the Mauritanian RIM 27-bus transmission network, were considered for implementing the JS-MFO approach. Experimental tests of the JS, MFO, and JS-MFO algorithms on eight well-known benchmark functions, the IEEE 30-bus, and the Mauritanian RIM 27-bus system were conducted. For the IEEE 30-bus test system, the proposed hybrid approach provides a percent cost saving of 11.4028%, a percent gas emission reduction of 14.38%, and a percent loss saving of 50.60% with respect to the base case. For the RIM 27-bus system, JS-MFO achieved a loss percent saving of 50.67% and percent voltage reduction of 62.44% with reference to the base case. The simulation results using JS-MFO and obtained with the MATLAB 2009b software were compared with those of JS, MFO, and other well-known meta-heuristics cited in the literature. The comparison report proves the superiority of the JS-MFO method over JS, MFO, and other competing meta-heuristics in solving difficult OPF problems. [ABSTRACT FROM AUTHOR]

The vibration and transmission characteristics of track components during vehicle operation is a complex issue. To address this issue, this paper proposes a flexible wheelset-damped track-tunnel-soil model that considers the interaction between the flexible wheelset and the rail. Using field measurements and the developed model, this study investigates the vibration characteristics of track components and explores the influence of fastener stiffness and damping pad stiffness on track vibration and vibration transfer characteristics between components from an energy perspective, using track spectrum power flow as the evaluation index. The results demonstrate that the model considering wheel-rail coupling can more comprehensively reflect the vibration characteristics of the track during train operation. Compared to fasteners with lower stiffness, damping pads with lower stiffness can reduce track vibration in a wider frequency range. Lower fastener stiffness reduces the transmission of vibration in 50∼2000 Hz, while lower damping pad stiffness reduces the transmission of vibrations in 1∼2000 Hz. [ABSTRACT FROM AUTHOR]

This paper addresses the critical challenge of optimizing power flow in multi-area power systems while maintaining information privacy and decentralized control. The main objective is to develop a novel decentralized stochastic recursive gradient (DSRG) method for solving the optimal power flow (OPF) problem in a fully decentralized manner. Unlike traditional centralized approaches, which require extensive data sharing and centralized control, the DSRG method ensures that each area within the power system can make independent decisions based on local information while still achieving global optimization. Numerical simulations are conducted using MATLAB (Version 24.1.0.2603908) to evaluate the performance of the DSRG method on a 3-area, 9-bus test system. The results demonstrate that the DSRG method converges significantly faster than other decentralized OPF methods, reducing the overall computation time while maintaining cost efficiency and system stability. These findings highlight the DSRG method's potential to significantly enhance the efficiency and scalability of decentralized OPF in modern power systems. [ABSTRACT FROM AUTHOR]

To alleviate the power flow congestion in active distribution networks (ADNs), this paper proposes a two-stage load transfer optimization model based on Neo4j-Dueling DQN. First, the Neo4j graph model was established as the training environment for Dueling DQN. Meanwhile, the power supply paths from the congestion point to the power source point were obtained using the Cypher language built into Neo4j, forming a load transfer space that served as the action space. Secondly, based on various constraints in the load transfer process, a reward and penalty function was formulated to establish the Dueling DQN training model. Finally, according to the ε − g r e e d y action selection strategy, actions were selected from the action space and interacted with the Neo4j environment, resulting in the optimal load transfer operation sequence. In this paper, Python was used as the programming language, TensorFlow open-source software library was used to form a deep reinforcement network, and Py2neo toolkit was used to complete the linkage between the python platform and Neo4j. We conducted experiments on a real 79-node system, using three power flow congestion scenarios for validation. Under the three power flow congestion scenarios, the time required to obtain the results was 2.87 s, 4.37 s and 3.45 s, respectively. For scenario 1 before and after load transfer, the line loss, voltage deviation and line load rate were reduced by about 56.0%, 76.0% and 55.7%, respectively. For scenario 2 before and after load transfer, the line loss, voltage deviation and line load rate were reduced by 41.7%, 72.9% and 56.7%, respectively. For scenario 3 before and after load transfer, the line loss, voltage deviation and line load rate were reduced by 13.6%, 47.1% and 37.7%, respectively. The experimental results show that the trained model can quickly and accurately derive the optimal load transfer operation sequence under different power flow congestion conditions, thereby validating the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Hydrodynamic tools reveal information as to the behaviour of a device in the presence of waves but provide little information on how to improve or optimise the device. With no recent work on the transfer of power (energy flux) from a wave field through the body surface of a wave energy converter (WEC), we introduce the energy flux method to map the flow of power. The method is used to develop an open-source tool to visualise the energy flux density on a WEC body surface. This energy flux surface can also be used to compute the total power capture by integrating over the surface. We apply the tool to three WEC classes: a heaving cylinder, a twin-hulled hinged barge, and pitching surge devices. Using the flux surfaces, we investigate power efficiency in terms of power absorbed to power radiated. We visualise the hydrodynamic consequence of sub-optimal damping. Then, for two pitching surge devices with similar resonant peaks, we reveal why one device has a reduced power performance in a wave spectrum compared to the other. The results show the effectiveness of the energy flux method to predict power capture compared to motion-based methods and highlight the importance of assessing the flux of energy in WECs subjected to different damping strategies. Importantly, the tool can be adopted for a wide range of applications, from geometry optimisation and hydrodynamic efficiency assessment to structural design. [ABSTRACT FROM AUTHOR]

In a deregulated power system, managing congestion is crucial for effective operation and control. The goal of congestion management is to alleviate transmission line congestion while adhering to system constraints at minimal cost. This research proposes a hybrid Harris Hawk Optimization–Sine Cosine Algorithm (hHHO-SCA) for an efficient generation rescheduling approach to achieve the lowest possible congestion cost. The hybridization has been performed by introducing the features of SCA in the HHO to boost the exploration and exploitation steps of HHO, providing an efficient global solution and effectively optimizing rescheduled power output. The effectiveness of this methodology is evaluated using IEEE 30 and IEEE 118-bus test systems, taking into account system parameters. The potency of the proposed method is analyzed by comparing the results of the hHHO-SCA with those from other recent optimization techniques. The findings show that the hHHO-SCA outperforms other methods by avoiding local optima and demonstrating promising convergence characteristics. [ABSTRACT FROM AUTHOR]

To ensure the constant availability of electrical energy, power companies must consistently maintain a balance between supply and demand. However, electrical load is influenced by a variety of factors, necessitating the development of robust forecasting models. This study seeks to enhance electricity load forecasting by proposing a hybrid model that combines Sorted Coefficient Wavelet Decomposition with Long Short-Term Memory (LSTM) networks. This approach offers significant advantages in reducing algorithmic complexity and effectively processing patterns within the same class of data. Various models, including Stacked LSTM, Bidirectional Long Short-Term Memory (BiLSTM), Convolutional Neural Network—Long Short-Term Memory (CNN-LSTM), and Convolutional Long Short-Term Memory (ConvLSTM), were compared and optimized using grid search with cross-validation on consumption data from Lome, a city in Togo. The results indicate that the ConvLSTM model outperforms its counterparts based on Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and correlation coefficient (R2) metrics. The ConvLSTM model was further refined using wavelet decomposition with coefficient sorting, resulting in the WT+ConvLSTM model. This proposed approach significantly narrows the gap between actual and predicted loads, reducing discrepancies from 10–50 MW to 0.5–3 MW. In comparison, the WT+ConvLSTM model surpasses Autoregressive Integrated Moving Average (ARIMA) models and Multilayer Perceptron (MLP) type artificial neural networks, achieving a MAPE of 0.485%, an RMSE of 0.61 MW, and an R2 of 0.99. This approach demonstrates substantial robustness in electricity load forecasting, aiding stakeholders in the energy sector to make more informed decisions. [ABSTRACT FROM AUTHOR]

In the context of constructing new power systems, the intermittency and volatility of high-penetration renewable generation pose new challenges to the stability and secure operation of power systems. Enhancing the ramping capability of power systems has become a crucial measure for addressing these challenges. Therefore, this paper proposes a bi-level peak regulation optimization model for power systems considering ramping capability and demand response, aiming to mitigate the challenges that the uncertainty and volatility of renewable energy generation impose on power system operations. Firstly, the upper-level model focuses on minimizing the ramping demand caused by the uncertainty, taking into account concerned constraints such as the constraint of price-guided demand response, the constraint of satisfaction with electricity usage patterns, and the constraint of cost satisfaction. By solving the upper-level model, the ramping demand of the power system can be reduced. Secondly, the lower-level model aims to minimize the overall cost of the power system, considering constraints such as power balance constraints, power flow constraints, ramping capability constraints of thermal power units, stepwise ramp rate calculation constraints, and constraints of carbon capture units. Based on the ramping demand obtained by solving the upper-level model, the outputs of the generation units are optimized to reduce operation cost of power systems. Finally, the proposed peak regulation optimization model is verified through simulation based on the IEEE 39-bus system. The results indicate that the proposed model, which incorporates ramping capability and demand response, effectively reduces the comprehensive operational cost of the power system. [ABSTRACT FROM AUTHOR]

Flexible user-side resources are of great potential in providing power regulation so as to effectively address the challenges of reverse power flow and overvoltage issues in distribution networks characterized by high photovoltaic (PV) penetration. However, existing distributed algorithms typically implement control signals after the convergence of the algorithms, making it difficult to track frequent and rapid fluctuations in PV power outputs in real time. Given this background, an online-distributed control algorithm for the real-time power regulation of flexible user-side resources is proposed in this paper. The objective of the established control model is to minimize network losses by dynamically adjusting active power outputs of flexible user-side resources and reactive power outputs of PV inverters while respecting branch power flow and voltage magnitude constraints. Furthermore, by deconstructing the centralized problem into a primal–dual one, a distributed control strategy based on the dual ascent method is implemented. With the proposed method, agents can achieve global optimality by exchanging limited information with their neighbors. The simulation results verify the good balance between economic efficiency and voltage control performance of the proposed method. [ABSTRACT FROM AUTHOR]

This article explores Facebook's censorship of Kurdish political activism at the request of the Turkish government. I argue that Facebook's censorship of political voices belonging to the marginalized Kurdish community is an articulation of platform coloniality, an outcome constituted by the intersecting of the social media giant's global political economy imperatives with racialized and hierarchized conceptions of human worth. The effects of platform coloniality are exacerbated due to it being mediated by the Turkish nation-state's internal colonial politics and militarist regional policies, thus intensifying the marginalization of Kurds inside and outside Turkey. Covered in a typical neoliberal discourse of freedom and human rights, platform coloniality represents a continuation of the age-old patterns of Western power and its flow toward the Global South. [ABSTRACT FROM AUTHOR]

This paper presents an extra-high voltage synthetic test system that consists of 500 kV and 765 kV voltage levels, specifically designed for transmission expansion planning (TEP) studies. The test network includes long transmission lines whose series impedance and shunt admittance are calculated using the equivalent π circuit model, accurately reflecting the distributed nature of the line parameters. The proposed test system offers technically feasible steady-state operation under normal and all single contingency conditions. By incorporating accurate modeling for long transmission lines and EHV voltage levels, the test system provides a realistic platform for validating models and theories prior to their application in actual power systems. It supports testing new algorithms, control strategies, and grid management techniques, aids in transmission expansion planning and investment decisions, and facilitates comprehensive grid evaluations. Moreover, a TEP study is conducted on this test system and various scenarios are evaluated and compared economically. [ABSTRACT FROM AUTHOR]

Modern electrical power networks make extensive use of high voltage direct current transmission systems based on voltage source converters due to their advantages in terms of both cost and flexibility. Moreover, incorporating a direct current link adds more complexity to the optimal power flow computation. This paper presents a new meta-heuristic technique, named self-adaptive bonobo optimizer, which is an improved version of bonobo optimizer. It aims to solve the optimal power flow for alternating current power systems and hybrid systems AC/DC, to find the optimal location of the high voltage direct current line in the network, with a view to minimize the total generation costs and the total active power transmission losses. The self-adaptive bonobo optimizer was tested on the IEEE 30-bus system, and the large-scale Algerian 114-bus electric network. The obtained results were assessed and contrasted with those previously published in the literature in order to demonstrate the effectiveness and potential of the suggested strategy. [ABSTRACT FROM AUTHOR]

In recent times, the impact of typhoon disasters on integrated energy active distribution networks (IEADNs) has received increasing attention, particularly, in terms of effective cascading fault path prediction and enhanced fault recovery performance. In this study, we propose a modified ORNL-PSerc-Alaska (OPA) model based on optimal power flow (OPF) calculation to forecast IEADN cascading fault paths. We first established the topology and operational model of the IEADNs, and the typical fault scenario was chosen according to the component fault probability and information entropy. The modified OPA model consisted of two layers: An upper-layer model to determine the cascading fault location and a lower-layer model to calculate the OPF by using Yalmip and CPLEX and provide the data to update the upper-layer model. The approach was validated via the modified IEEE 33-node distribution system and two real IEADNs. Simulation results showed that the fault trend forecasted by the novel OPA model corresponded well with the development and movement of the typhoon above the IEADN. The proposed model also increased the load recovery rate by >24% compared to the traditional OPA model. [ABSTRACT FROM AUTHOR]

In power system, stability analysis becomes important to identify the level of stability and security of electrical power systems. This article proposes a flexible alternating current systems-unified power flow compensator (FACTS-UPQC) compensator installed in the high-voltage network to ensure stability of voltage and frequency in the power grid facing voltage dips, over-voltage and short-circuit faults. Thus, an artificial intelligence algorithm based on fuzzy logic method is implemented to have the appropriate values of FACTS-UPQC conditioner. The voltage stability improvement is demonstrated by the variation margin of amplitude and phase angle. Frequency stability aims to obtain a frequency within a minimal variation. A 14-bus test electrical system is modeled to implement the advanced control strategy. MATLAB/Simulink software is used to prove the functionality of the method in improving the stability of power system. The simulation results showed a reduction of harmonic distortion rate (HDR) and a minimization of the voltage variation range for the implemented fuzzy logic system compared with the literature. [ABSTRACT FROM AUTHOR]

Electronic devices and loads are very sensitive to the voltage disturbances like voltage sag and voltage swell. Significant financial losses and safety issues may emerge from voltage sags and interruptions, which can be caused by variables such as system breakdowns and load changes. In order to protect against voltage fluctuations and keep vital loads running, dynamic voltage restorers (DVRs) have become more popular. To mitigate the voltage disturbances, an interlined DVR (IDVR) using a Z-source inverter (ZSI) is developed to protect the sensitive devices and loads. Back-to-back DVR connects the distributed feeders with a common direct current (DC) link. The IDVR compensates for the sag voltage and supplies the energy to control the power flow. In addition, proposed a modified synchronous reference frame (MSRF)/direct quadrature theory, hysteresis controller, and proportional integral (PI) controller, which provides the required amount of control signals for a ZSI and voltage source inverter (VSI). MATLAB/Simulink validated the simulation results. The experimental findings show that the suggested system can be implemented successfully and is effective at reducing voltage dips and interruptions, allowing crucial loads to keep operating consistently and without interruption in residential as well as commercial environments. [ABSTRACT FROM AUTHOR]

The surge in adoption of electric vehicles (EVs) within the transportation sector can be attributed to the growing interest in sustainable transportation initiatives. It is imperative to position electric vehicle charging stations (EVCS) strategically and distribute generations (DGs) to mitigate the effects of electric vehicle loads. This research employs the whale optimization algorithm (WOA) to optimize the placement of EVCS and DGs alongside network reconfiguration. The backward-forward sweep (BFS) power flow technique is utilized to compute load flow under varying load conditions. The primary objective of this investigation is to minimize power losses and enhance the voltage profile within the system. The proposed approach was tested on IEEE-33 and 69 bus systems and compared with particle swarm optimization (PSO) and genetic algorithm (GA) techniques. The simulation outcomes affirm the effectiveness of whale optimization algorithm in determining that integrating 3 EVCS with 3 DGs yields optimal outcomes following network reconfiguration, resulting in a 56.22% decrease in power losses for the IEEE-33 bus system and a 76.13% reduction for the IEEE-69 bus system. The simulation results indicate that the proposed approach enhances system performance across all metrics, showcasing the superior performance of WOA compared to PSO and GA in accomplishing set objectives. [ABSTRACT FROM AUTHOR]

The load losses of the transformers increase considerably with the presence of harmonic distortion in the currents. Its increase can be determined using an analytical method based on ANSI/IEEE Standard C57.110. On the other hand, when it comes to three-phase banks with transformers of different capacities, delta connections, or incomplete connections, the analytical method does not allow the losses to be accurately estimated, which is why digital simulation is necessary. This work presents an adjusted model to determine the load losses in a three-phase bank of three single-phase transformers with different connection schemes. The model allows for determining load and electrical losses and calculating total additional losses. It is also possible to decide on the load capacity of the bank's transformers, the power factor, and the efficiency with which they operate under these conditions. [ABSTRACT FROM AUTHOR]

This article evaluates energy quality in individual circuits within an industrial electrical system and its impact on common connection point parameters. The research is crucial due to rising challenges in power quality arising from increased nonlinear electrical loads in industrial processes. The study involves sequential steps, covering the industrial electrical system's description, power quality parameters analysis, and issue identification. A comprehensive assessment was conducted on a 3,000 kVA, 13.8 kV/460 V point of common coupling (PCC) transformer, and 10 transformers (10 to 250 kVA) supplying individual circuits. Findings indicated load factors below 70% in all transformers and a power factor below 0.9 in eight. Issues like voltage variation, current imbalance, and harmonic distortion were identified in nine transformers supplying individual circuits, while the PCC exhibited no power quality problems. The research emphasizes the importance of including individual circuits in power quality assessments, as compliance with regulatory limits at the PCC may not guarantee the absence of power quality issues in individual circuits, affecting equipment lifespan and increasing energy losses. [ABSTRACT FROM AUTHOR]

Designing actuators that can modulate power, achieve high energy efficiency, and ensure safe collision remains a challenge, especially for dynamic energy robot systems (DERS) with high‐performance requirements. Herein, a novel multi‐configuration elastic actuator (MCEA) is proposed based on a controllable planetary differential mechanism (PDM) with one power port from three springs. These springs, positioned between the inner gear ring and the fixed housing shell, are regulated by a single servo motor through a ratchet–pawl mechanism. This setup enables the springs to absorb energy during collisions, reducing impact and subsequently releasing this energy to boost power output. The inner gear ring functions as a controllable one‐way rotating element, acting either as an input or output for power. The MCEA's ability to manage power modulation and energy flow is demonstrated through experiments that highlight its potential for safe collision management, energy recycling, and power modulation. Experiment results indicate that the maximum output power of the MCEA in the proposed hybrid elastic actuation (HEA) mode is 8.05 times higher than that in the traditional actuation (TA) mode. A single‐legged robot with a four‐link mechanism is also built to validate the considerable performance in the application of legged robots, showing considerable adaptability and prospects for DERS. [ABSTRACT FROM AUTHOR]

As the penetration of renewable energy sources (RESs), particularly wind power, continues to rise, the uncertainty in power systems increases. This challenges traditional optimal power flow (OPF) methods. This paper proposes a Calibrated Safety Constraints Optimal Power Flow (CSCOPF) model that uses the Improved Acceleration Coefficient-Based Bee Swarm algorithm (IACBS) in combination with the equivalent current injection (ECI) model. The proposed method addresses key challenges in wind-integrated power systems by ensuring preventive safety scheduling and enabling effective power incident safety analysis (PISA). This improves system reliability and stability. This method incorporates mixed-integer programming, with continuous and discrete variables representing power outputs and control mechanisms. Detailed numerical simulations were conducted on the IEEE 30-bus test system, and the feasibility of the proposed method was further validated on the IEEE 118-bus test system. The results show that the IACBS algorithm outperforms the existing methods in both computational efficiency and robustness. It achieves lower generation costs and faster convergence times. Additionally, the CSCOPF model effectively prevents power grid disruptions during critical incidents, ensuring that wind farms remain operational within predefined safety limits, even in fault scenarios. These findings suggest that the CSCOPF model provides a reliable solution for optimizing power flow in renewable energy-integrated systems, significantly contributing to grid stability and operational safety. [ABSTRACT FROM AUTHOR]

Pumped storage units serve as a crucial support for power systems to adapt to large-scale and high-proportion renewable energy sources by providing a stable and flexible energy supply. However, due to the coupling effects of electric power load demands and the complex multi-source factors within the water–mechanical–electrical system, the interrelationship between unit parameters becomes more intricate, posing significant threats to the operational reliability and health status of the units. The complexity of fault diagnosis is further aggravated by the intricate and varied nature of fault characteristics, as well as the challenges in signal extraction under conditions of strong electromagnetic interference and high noise levels. To address these issues, this paper proposes a novel data-model hybrid-driven strategy that analyzes vibration signals to achieve rapid and accurate fault diagnosis of the units. Firstly, the spectral kurtosis theory is employed to enhance the traditional empirical mode decomposition, achieving optimal decomposition and noise reduction effects for vibration signals. Secondly, the intrinsic mode functions (IMFs) obtained from the decomposition are reconstructed, and the entropy values of effective IMFs are calculated as fault feature vectors. Subsequently, the CNN-LSTM model is utilized for fault diagnosis. The effectiveness and feasibility of the proposed method are verified through actual operational data from pumped storage units in a specific region. Through analysis, the fault diagnosis accuracy of the method proposed in this paper can be maintained above 95%, demonstrating robustness in complex engineering environments and effectively ensuring the safe and stable operation of pumped storage units. [ABSTRACT FROM AUTHOR]

Tokamaks are a very promising option to exploit nuclear fusion as a programmable and safe energy source. A very critical issue for the practical use of tokamaks consists of the power flow required to initiate and sustain the fusion process, in particular in the poloidal field coils. This flow can be managed by introducing a DC energy storage based on supercapacitors. Because such storage may be the most expensive and largest part of the poloidal power supply system, an excessive size would cancel its potential advantages. This paper presents innovative strategies to optimize the DC storage in poloidal power supply systems. The proposed solution involves the sharing of the DC storage between different coil circuits. The study is supported by novel analytical formulas and by a circuital model developed for this application. The obtained results show that this method and the related algorithms can noticeably reduce the overall size of the storage and the power exchange with the grid, providing a practical contribution toward the feasibility and the effectiveness of nuclear fusion systems. [ABSTRACT FROM AUTHOR]

The development of zero-energy buildings (ZEBs) is a critical pillar for designing the sustainable cities of the future. Photovoltaics (PVs) play a significant role in the design of ZEBs, especially in cases with fully electrified buildings. The goal of this analysis was to investigate different advanced PVs with integrated cell cooling techniques that can be incorporated into buildings aiming to transform them into ZEBs. Specifically, the examined cooling techniques were radiative PV cells, externally finned PVs and the combination of PVs with phase-change materials. These ideas were compared with the conventional PV design for the climate conditions of Athens, Barcelona, Munich and Stockholm. At every location, two different building typologies, B1 (a five-story building) and B2 (a two-story building), were investigated and the goal was to design zero-energy buildings. In the cases that the roof PVs could not cover the total yearly electrical load, building-integrated photovoltaics (BIPVs) were also added in the south part of every building. It was found that in all the cases, it is possible to design ZEB with the use of roof PVs, except for the cases of B1 buildings in Munich and Stockholm, there is also a need to exploit BIPVs. Moreover, a significant electricity surplus was reported, especially at the warmest locations (Athens and Barcelona). Among the examined cooling techniques, the application of the fins in the back side of the PVs was determined to be the most effective technique, with radiative cooling to follow with a slightly lower performance enhancement. The application of PCM was found to be beneficial only in hot climate conditions. [ABSTRACT FROM AUTHOR]

With the growing integration of renewable energy into medium- and low-voltage distribution networks, the distribution substation area (DSA) has emerged, encompassing energy storage and loads. This paper introduces an energy interaction framework for multiple DSAs aimed at enhancing local renewable energy consumption. The energy interaction issue among various DSAs is modeled as a Nash bargaining problem to encourage energy exchanges. However, the variability in pricing and internal demand response may influence scheduling decisions, necessitating further investigation. To address price forecast errors, scenarios are developed using a stochastic programming approach to represent price uncertainties while adjusting the DSA's load accordingly. Optimal power flow constraints are integrated into the model to bolster power system operation security. Additionally, the transmission capacity can impact scheduling outcomes and operational costs. The influence of transmission limitations on operational strategies is examined within the allowable capacity. To solve this issue, the bargaining model is divided into two subproblems, and an enhanced alternating direction multiplier method (ADMM) is used to maintain the privacy of DSAs. The simulation results obtained using the IEEE-33 bus system indicate that energy interaction among multiple DSAs significantly lowers operating costs and facilitates the integration of renewable energy. [ABSTRACT FROM AUTHOR]

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Copyright of Technisches Messen is the property of De Gruyter 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.)

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]

This study presents short-term electricity load forecasting for the New England area by processing initial data through correlation assessment and data clustering. This method is combined with artificial neural networks to improve accuracy and forecast performance. Data preprocessing focuses on two main issues: identifying correlations between variables to eliminate less relevant factors and retaining highly correlated variables to reduce noise, as well as reducing the data sample size before training the neural network. This evaluation aims to determine the factors that have a significant impact on electricity load. These factors can include previous load values, weather conditions, time, types of electricity usage, and others. This technique ensures that reducing the size in both dimensions of the large dataset does not result in the loss of critical information, maintaining the accuracy of computational programs and the performance of neural network training at high levels. The neural network is trained to classify and cluster data based on previously identified correlated characteristics. As a result, the forecasting model can make more accurate predictions about future electricity loads. Experimental results show that the proposed method achieved more than 97 % accuracy, outperforming traditional methods in both speed and load forecasting accuracy. The new dataset had 63% fewer samples compared to the initial dataset. [ABSTRACT FROM AUTHOR]

Recent developments in aircraft electrical technology, such as the design and production of more electric aircraft (MEA) and major steps in the development of all-electric aircraft (AEA), have had a significant impact on aircraft's electrical power systems (EPSs). However, the EPSs of the latest aircraft produced by the main players in the market, Airbus with the Neo series and Boeing with the NG and MAX series are still completely traditional and based on the constant speed constant frequency (CSCF) configuration. For alternating current ones, the EPS is composed of the following: prime movers, namely the aircraft turbofan engine (TE); the electrical power source, i.e., the integrated drive generator (IDG); the command and control system, the generator control unit (GCU); the transmission and the system distribution system; the protection system, i.e., the CBs (circuit breakers); and the electrical loads. This paper presents the analysis of this system using the Simscape package from Simulink v 8.7, a MATLAB v 9.0 program, which is actually the development of some systems designed in two previous personal papers. For the first time in the literature, a complete MATLAB modelled EPS system was presented, i.e., the aircraft turbofan engine model, driving the constant speed drive system (CSD) (model presented in the first reference as a standalone type and with different parameters), linked to the synchronous generator (SG) (model presented in second reference for lower power and rotational speed) in the so-called integrated drive generator (IDG) and electrical loads. [ABSTRACT FROM AUTHOR]

The paper contains a detailed analysis of the formation of an auroral spiral based on hitherto not published observations by the all‐sky camera in Kilpisjärvi, Northern Finland. We conclude that spirals appearing during a substorm form by a modification of the interface between tail and magnetosphere, the location of the generator current of the westward electrojet. Driven by the arriving flow bursts, this current is subject to ruptures by the appearance of a sequence of hook‐like structures. These structures can move eastward with speeds up to 3 km/s. The propagation is attributed to a constructive magnetic fracture process driven from behind by the power of the arriving flow bursts. Poleward bending and extension of a hook‐like structure, followed by a turning to the west and then equatorward, is the first step in spiral formation. It becomes the primary spiral arm, if a poleward arm grows out of weaker auroral structures, poleward and eastward of it. We suggest that the upward field‐aligned currents related to the bright spiral arms are largely balanced by adjacent downward currents. The electric fields associated with the connecting Pedersen currents are consistent with the counter‐clockwise motion. An important additional ingredient in the observed configuration is an eastward directed flow field, which is the generator of an additional upward current and possibly crucial for the spiral formation. Electric field data from literature throw confusing light on the propagation of a spiral, whether like a vessel in the ocean or by incorporating the magnetic flux ahead of it. Plain Language Summary: Auroral spirals are a fascinating phenomenon in the night sector during substorms. An event, observed by the all sky camera in Kilipisjärvi, is interpreted in the light of the physical processes at substorm onset. The forces exerted by an arriving flow burst on the magnetic field of the near‐dipolar magnetosphere generate an eastward directed current, which is the generator of the auroral electrojet (AEJ). This current can be broken into sections by an instability suggested to occur at strong flow gradients. Out of such sections, a spiral may form in two steps. First, a hook‐like structure, arising from the eastern end of the section, will extend poleward, then westward and finally equatorward thus forming the primary spiral arm. The poleward arm grows out of residues of the rupture process by draping themselves around the upper end of the primary arm. The luminous spiral arms, generated by upward currents, must be closely accompanied by downward currents located in the dark regions of the spiral. The electric fields, connected with the current closure in the ionosphere, are consistent with the counter‐clockwise sense of spiral formation. The creation of convolved auroral forms such as spirals serves an enhanced dumping of the entering energy. Key Points: Ruptures of the generator current lead to the formation of hook‐like structures out of which a spiral can evolveA primary spiral arm extending poleward in a counter‐clockwise sense is completed into a spiral by reformation of residues from the ruptureThe upward field‐aligned currents, which are associated with the luminous structures, are largely balanced by adjacent downward currents [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]

In the current century, electrical networks have witnessed great developments and continuous increases in the demand for fossil fuels based energy, leading to an excessive rise in the total production cost (TPC), as well as the pollutant (toxic) gases emitted by thermal plants. Under this circumstances, energy supply from different resources became necessary, such as renewable energy sources (RES) as an alternative solution. This latter, however, characterized with uncertainty nature in its operation principle, especially when operator system wants to define the optimal contribution of each resource in an effort to ensure economic and enhanced reliability of grid. This paper presents an Enhanced version of Kepler optimization algorithm (EKOA) to solve the problem of stochastic optimal power flow (SOPF) in a most efficient way incorporating wind power generators and solar photovoltaic with different objective functions, the stochastic nature of wind speed and solar is modeled using Weibull and lognormal probability density functions respectively. To prove the effectiveness of the proposed EKOA, various case studies were carried out on two test systems IEEE 30-bus system and Algerian power system 114-bus, obtained results were evaluated in comparison with those obtained using the original KOA and other methods published in the literatures. Thus, shows the effectiveness and superiority of the efficient EKOA over other optimizers to solve complex problem. The incorporation of RES resulted in a significant 2.39% decrease in production cost, showcasing EKOA's efficiency with a $780/h, compared to KOA's $781/h, for IEEE 30-bus system. For the DZA 114-bus system revealed even more substantial reductions, with EKOA achieving an impressive 12.6% reduction, and KOA following closely with a 12.4% decrease in production cost. [ABSTRACT FROM AUTHOR]

In this paper, the problem of simultaneous charging of Electrical Vehicles (EVs) in distribution networks (DNs) is examined in order to depict congestion issues, increased power losses, and voltage constraint violations. To this end, this paper proposes an optimal EV charging schedule in order to allocate the charging of EVs in non-overlapping time slots, aiming to avoid overloading conditions that could stress the DN operation. The problem is structured as a linear optimization problem in GAMS, and the linear Distflow is utilized for the power flow analysis required. The proposed approach is compared to the one where EV charging is not optimally scheduled and each EV is expected to start charging upon its arrival at the residential charging spot. Moreover, the analysis is extended to examine the optimal siting of small-sized residential Photovoltaic (PV) systems in order to provide further relief to the DN. A mixed-integer quadratic optimization model was formed to integrate the PV siting into the optimization problem as an additional optimization variable and is compared to a heuristic-based approach for determining the sites for PV installation. The proposed methodology has been applied in a typical low-voltage (LV) DN as a case study, including real power demand data for the residences and technical characteristics for the EVs. The results indicate that both the DN power losses and the voltage profile are further improved in regard to the heuristic-based approach, and the simultaneously scheduled penetration of EVs and PVs could yield up to a 66.3% power loss reduction. [ABSTRACT FROM AUTHOR]

AbstractThis study shed light on the simultaneous effects of bistability and electrical resistor on the equivalent damping of a piezoelectric cantilever beam energy harvester. Damping coefficient has a direct effect on the frequency bandwidth of an oscillating system. This paper, extends the experimental-analytical balanced energy (BE) method to obtain the damping change due to bistability of a piezoelectric energy harvester. By using extended Hamilton principle and Euler-Lagrange formulation with nonlinear magnetic field relations, the governing equations of the system are derived. Then the equivalent BE viscous damping model was adopted and the equivalent damping term is achieved. In BE method, the test signals are registered into the proposed BE algorithm, then the equivalent viscous damping coefficient of the system will be obtained. In the end, the effects of bistability and electrical resistor of the power circuit of piezoelectric patch is discussed. According to the results, by increasing the bistability of the system, the equivalent damping of the system will also increase. Besides by escalation the value of electrical load in piezoelectric power circuit, the value of the equivalent damping coefficient reaches a maximum value. This behavior is originated from the extracted power of the system. [ABSTRACT FROM AUTHOR]

With the development of the digital economy, the power demand for data centers (DCs) is rising rapidly, which presents a challenge to the economic and low‐carbon operation of the future distribution system. To this end, this paper fully considers the multiple flexibility of DC and its impact on the active distribution network, and establishes a collaborative planning model of DC and active distribution network. Differing from most existing studies that apply robust optimization or stochastic optimization for uncertainty characterization, this study employs a novel interval optimization approach to capture the inherent uncertainties within the system (including the renewable energy source (RES) generation, electricity price, electrical loads, emissions factor and workloads). Subsequently, the planning model is reformulated as the interval multi‐objective optimization problem (IMOP) to minimize economic cost and carbon emission. On this basis, instead of using a conventional deterministic‐conversion approach, an interval multi‐objective optimization evolutionary algorithm based on decomposition (IMOEA/D) is proposed to solve the proposed IMOP, which is able to fully preserve the uncertainty inherent in interval‐typed information and allow to obtain an interval‐formed Pareto front for risk‐averse decision‐making. Finally, an IEEE 33‐node active distribution network is utilized for simulation and analysis to confirm the efficacy of the proposed approach. [ABSTRACT FROM AUTHOR]

The integration of renewable sources, such as hydro, wind, and solar power, into electrical systems has profoundly transformed the sector's dynamics. The inherent intermittency of these energy sources, due to the uncertainty associated with inflows, winds, and solar irradiation, introduces considerable challenges in the operation and planning of the electrical system. In this context, demand response emerges as a promising solution to handle the fluctuations in renewable generation and maintain system stability and reliability. Therefore, this study presents a new approach to the demand response program through the modeling of an optimal power flow problem to minimize operational costs, considering the uncertainties in hydro, wind, and solar generation by applying the Conditional Value-at-Risk (CVaR) risk metric. The mathematical modeling of the problem was conducted, and the problem was solved using the MINOS solver. To validate the model, simulations were carried out using modified IEEE systems of 14, 30, 57, and 118 buses, considering operation planning for the next 24 h. Furthermore, sensitivity analyses were performed by altering the CVaR parameters. As a result of the simulations, the total operational cost, electrical losses, and hourly generation at each bus by source type were determined, highlighting how CVaR impacts the operation of this type of system. [ABSTRACT FROM AUTHOR]

Accurate electrical load forecasting is crucial for the stable operation of power systems. However, existing forecasting models face limitations when handling multidimensional features and feature interactions. Additionally, traditional metaheuristic algorithms tend to become trapped in local optima during the optimization process, negatively impacting model performance and prediction accuracy. To address these challenges, this paper proposes a short-term electrical load forecasting method based on a parallel Temporal Convolutional Network–Gated Recurrent Unit (PTCN-GRU) model, optimized by an improved Dung Beetle Optimization algorithm (IDBO). This method employs a parallel TCN structure, using TCNs with different kernel sizes to extract and integrate multi-scale temporal features, thereby overcoming the limitations of traditional TCNs in processing multidimensional input data. Furthermore, this paper enhances the optimization performance and global search capability of the traditional Dung Beetle Optimization algorithm through several key improvements. Firstly, Latin hypercube sampling is introduced to increase the diversity of the initial population. Next, the Golden Sine Algorithm is integrated to refine the search behavior. Finally, a Cauchy–Gaussian mutation strategy is incorporated in the later stages of iteration to further strengthen the global search capability. Extensive experimental results demonstrate that the proposed IDBO-PTCN-GRU model significantly outperforms comparison models across all evaluation metrics. Specifically, the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) were reduced by 15.01%, 14.44%, and 14.42%, respectively, while the coefficient of determination (R2) increased by 2.13%. This research provides a novel approach to enhancing the accuracy of electrical load forecasting. [ABSTRACT FROM AUTHOR]

This paper presents a dynamic homotopy technique that can be used to calculate a preliminary result for a power flow problem (PFP). This result can then be used as an initial estimate to efficiently solve the PFP using either the classical Newton-Raphson (NR) method or its fast decoupled version (FDXB) while still maintaining high accuracy. The preliminary stage for the dynamic homotopy problem is formulated and solved by employing integration techniques, where implicit and explicit schemes are studied. The dynamic problem assumes an initial condition that coincides with the initial estimate for a traditional iterative method such as NR. In this sense, the initial guess for the FPF is adequately set as a flat start, which is a starting for the case when this initialization is of difficult assignment for convergence. The static homotopy method requires a complete solution of a PFP per homotopy pathway point, while the dynamic homotopy is based on numerical integration methods. This approach can require only one LU factorization at each point of the pathway. Allocating these points properly helps avoid several PFP resolutions to build the pathway. The hybrid technique was evaluated for large-scale systems with poor conditioning, such as a 109,272-bus model and other test systems under stressed conditions. A scheme based on the implicit backward Euler scheme demonstrated the best performance among other numerical solvers studied. It provided reliable partial results for the dynamic homotopy problem, which proved to be suitable for achieving fast and highly accurate solutions using both the NR and FDXB solvers. [ABSTRACT FROM AUTHOR]

The transportation industry is experiencing a switch towards electrification. Availability of electric vehicle (EV) charging infrastructure is very critical for broader acceptance of EVs. The increasing use of OBCs, due to their cost-effectiveness and ease of installation, necessitates addressing key challenges. These include achieving high efficiency and power density to overcome space limitations and reduce charging times. Additionally, the growing interest in bidirectional power flow, allowing EVs to supply power back to the grid, highlights the importance of innovative OBC solutions. This review article provides a thorough analysis of the current advancements, challenges, and prospects in EV on-board charger technology. It aims to offer a comprehensive review of OBC architectures, components, technologies, and emerging trends, guiding future research and development. Addressing these challenges is essential to enhance the efficiency, reliability, and integration of OBCs within the broader EV ecosystem. [ABSTRACT FROM AUTHOR]

This paper presents the results of an experimental study designed to evaluate the effect of repeated stretching cycles on the electrical resistance change in a NiTi alloy wire. In particular, tests were carried out to determine the effect of the type of loading on resistance change in the investigated wires. Wires with a diameter of 100 μm were used in the research. The experiment was carried out on a dedicated test stand designed for this purpose. During the test, the samples were subjected to 40 identical tensile cycles. The electrical resistance, sample elongation, and tensile force during successive stretching cycles were measured. The conducted research demonstrated the impact of elongation and reorientation of the structure on the resistance change in NiTi alloy thin wires. The research included a comparison of the effect of two different types of loading on the electrical resistance change in the sample. During cyclic stretching of a NiTi alloy sample with constant displacement, a decrease in electrical resistance was observed after each successive stretching cycle. Alternatively, when stretching with a constant force, the value of electrical resistance increased. In both types of loads, the greatest change in resistance value was observed at the initial cycles. [ABSTRACT FROM AUTHOR]

One portion of the distribution system is the distribution feeder. Buses carrying more loads and having long‐distance route lines are significant problems due to the presence of power loss and the increase in the overall cost of the transportation process. Hence, the overall power loss minimization is taken as the major objective. The best sizing and allocation of capacitors in a bank in the distribution line are used to minimize the total loss. BWO technique is utilized in this optimization process. The backward‐forward sweep load flow is used for the computation of the power flow in MATLAB. The sensitive buses have been chosen according to the factor of loss sensitivity (LSF) and using BWO. The validity of the test has been made on the standard IEEE 34 and 85 bus radial distribution system. The simulation results in the 34‐bus radial system are much different from the results of PSO, GWO, WO, and IWO. In the 85‐bus radial system, the results are seen with the outcomes of PSO, WO, and BFOA. In both cases, the results of the proposed technique are found to be better than the existing methods. Therefore, the result shows that BWO can be effective for future selection to improve large distribution networks by sizing and locating the capacitors optimally. [ABSTRACT FROM AUTHOR]

In recent years, the interest in electric direct current (DC) technologies (such as converters, batteries, and electric vehicles) has increased due to their potential in energy efficiency and sustainability. However, the vast majority of electric systems and networks are based on alternating current (AC) as they also have certain advantages regarding cost-effective transport and robustness. In this paper, an AC/DC optimal power flow method for hybrid microgrids and several key performance indicators (KPIs) for its techno-economic assessment are presented. The combination of both calculations allows users to determine the viability of their hybrid microgrids. AC/DC networks have been modeled considering their most common elements. For the power flow method, polynomial optimization is formulated considering four different objective functions: the minimization of energy losses, voltage deviation, and operational costs and the maximization of the microgrid generation. The power flow method and the techno-economic analysis are implemented in Python and validated in the Centro de Desarrollo de Energias Renovables (CEDER) demonstrator for TIGON. The results show that the calculated power flow variables and those measured at CEDER are practically the same. In addition, the KPIs are obtained and compared for four operating scenarios: baseline, no battery, battery flexibility, and virtual battery (VB) flexibility. The last scenario results in the most profitable option. [ABSTRACT FROM AUTHOR]

Introduction: In response to the issues of complexity and low efficiency in line loss calculations for actual distribution networks, this paper proposes a fast power flow calculation method for distribution networks based on Neo4j graph models and a hierarchical forward-backward sweep parallel algorithm. Methods: Firstly, Neo4j is used to describe the distribution network structure as a simple graph model composed of nodes and edges. Secondly, a hierarchical forward-backward sweep method is adopted to perform power flow calculations on the graph model network. Finally, during the computation of distribution network subgraphs, the method is combined with the Bulk Synchronous Parallel (BSP) computing model to quickly complete the line loss analysis. Results and Discussion: Results from the IEEE 33-node test system demonstrate that the proposed method can calculate network losses quickly and accurately, with a computation time of only 0.175s, which is lower than the MySQL and Neo4j graph methods that do not consider hierarchical parallel computing. [ABSTRACT FROM AUTHOR]