Search Results (76)

Australia has the highest per capita penetration of rooftop solar PV systems in the world. Integration of these systems has led to reverse power flow and associated voltage rise problems in residential low-voltage (LV) distribution networks. Furthermore, random, uncontrolled connection of single-phase solar systems can exacerbate voltage unbalance in these networks. This paper investigates the application of a Static Synchronous Compensator (STATCOM) for the improvement of voltage regulation in four-wire LV distribution feeders through reactive power management as a means of mitigating voltage regulation and unbalance challenges. To demonstrate the performance of the STATCOM with varying loads and PV output, a Q-V droop curve is applied to specify the level of reactive power injection/absorption required to maintain appropriate voltage regulation. A practical four-wire feeder from New South Wales, Australia, has been used as a case study network to analyse improvements in system performance through the use of the STATCOM. The outcomes indicate that the STATCOM has a high degree of efficacy in mitigating voltage regulation and unbalance excursions. In addition, compared to other solutions identified in the existing literature, the STATCOM-based solution requires no sophisticated communication infrastructure. Full article

Addressing the imperative for energy transition amid depleting fossil fuels, distributed generation (DG) is increasingly integrated into distribution networks (DNs). This integration necessitates low-carbon dispatching solutions that reconcile economic and environmental objectives. To bridge the gap between conventional “electricity perspective” optimization and emerging “carbon perspective” requirements, this research integrated Carbon Emission Flow (CEF) theory to analyze spatiotemporal carbon flow characteristics within DN. Recognizing the limitations of the single-objective approach in balancing multifaceted demands, a multi-objective optimization model was formulated. This model could capture the spatiotemporal dynamics of nodal carbon intensity for low-carbon dispatching while comprehensively incorporating diverse operational economic costs to achieve collaborative low-carbon and economic dispatch in DG-embedded DN. To efficiently solve this complex constrained model, a novel Q-learning enhanced Moth Flame Optimization (QMFO) algorithm was proposed. QMFO synergized the global search capability of the Moth Flame Optimization (MFO) algorithm with the adaptive decision-making of Q-learning, embedding an adaptive exploration strategy to significantly enhance solution efficiency and accuracy for multi-objective problems. Validated on a 16-node three-feeder system, the method co-optimizes switch configurations and DG outputs, achieving dual objectives of loss reduction and carbon emission mitigation while preserving radial topology feasibility. Full article

In this paper, we address the problem of intelligent operation of Distribution Static Synchronous Compensators (D-STATCOMs) in power distribution systems to reduce energy losses and CO₂ emissions while improving system operating conditions. In addition, we consider the entire set of constraints inherent in the operation of such networks in an environment with D-STATCOMs. To solve such a problem, we used three master–slave methodologies based on sequential programming methods. In the proposed methodologies, the master stage solves the problem of intelligent D-STATCOM operation using the continuous versions of the Monte Carlo (MC) method, the population-based genetic algorithm (PGA), and the Particle Swarm Optimizer (PSO). The slave stage, for its part, evaluates the solutions proposed by the algorithms to determine their impact on the objective functions and constraints representing the problem. This is accomplished by running an Hourly Power Flow (HPF) based on the method of successive approximations. As test scenarios, we employed the 33- and 69-node radial test systems, considering data on power demand and CO₂ emissions reported for the city of Medellín in Colombia (as documented in the literature). Furthermore, a test system was adapted in this work to the demand characteristics of a feeder located in the city of Talca in Chile. This adaptation involved adjusting the conductors and voltage limits to include a test system with variations in power demand due to seasonal changes throughout the year (spring, winter, autumn, and summer). Demand curves were obtained by analyzing data reported by the local network operator, i.e., Compañía General de Electricidad. To assess the robustness and performance of the proposed optimization approach, each scenario was simulated 100 times. The evaluation metrics included average solution quality, standard deviation, and repeatability. Across all scenarios, the PGA consistently outperformed the other methods tested. Specifically, in the 33-node system, the PGA achieved a 24.646% reduction in energy losses and a 0.9109% reduction in CO₂ emissions compared to the base case. In the 69-node system, reductions reached 26.0823% in energy losses and 0.9784% in CO₂ emissions compared to the base case. Notably, in the case of the Talca feeder—particularly during summer, the most demanding season—the PGA yielded the most significant improvements, reducing energy losses by 33.4902% and CO₂ emissions by 1.2805%. Additionally, an uncertainty analysis was conducted to validate the effectiveness and robustness of the proposed optimization methodology under realistic operating variability. A total of 100 randomized demand profiles for both active and reactive power were evaluated. The results demonstrated the scalability and consistent performance of the proposed strategy, confirming its effectiveness under diverse and practical operating conditions. Full article

Uncoordinated electric vehicle (EV) charging can significantly complicate power system operations. In this paper, we develop a hybrid EV charging method that seamlessly integrates centralized EV charging and distributed control schemes to address EV energy demand challenges. The proposed method includes (1) a centralized day-ahead optimal scheduling mechanism and EV shifting process based on mixed-integer linear programming (MILP) and (2) a distributed control strategy based on a genetic algorithm (GA) that dynamically adjusts the charging rate in real-time grid scenarios. The MILP minimizes energy imbalance at overloaded slots by reallocating EVs based on supply–demand mismatch. By combining full and minimum charging strategies with MILP-based shifting, the method significantly reduces network stress due to EV charging. The centralized model schedules time slots using valley-filling and EV-specific constraints, and the local GA-based distributed control adjusts charging currents based on minimum energy, system availability, waiting time, and a priority index (PI). This PI enables user prioritization in both the EV shifting process and power allocation decisions. The method is validated using demand data on a radial feeder with residential and commercial load profiles. Simulation results demonstrate that the proposed hybrid EV charging framework significantly improves grid-level efficiency and user satisfaction. Compared to the baseline without EV integration, the average-to-peak demand ratio is improved from 61% to 74% at Station-A, from 64% to 80% at Station-B, and from 51% to 63% at Station-C, highlighting enhanced load balancing. The framework also ensures that all EVs receive energy above their minimum needs, achieving user satisfaction scores of 88.0% at Stations A and B and 81.6% at Station C. This study underscores the potential of hybrid charging schemes in optimizing energy utilization while maintaining system reliability and user convenience. Full article

An algorithm is proposed to recover out-of-service loads (OOSLs) in smart distribution systems (SDSs) after exposure to cyber-attacks (CAs) resulting in interruptions of in-service loads (INSLs). The proposed algorithm is implemented in three steps. The first step is based on building the SDS in matrix form to be data input to the proposed algorithm. The second step is concerned with classifying the SDS into three zones: the attacked zone, the primary neighbor zone, and the secondary neighbor zone. The third step is performing five maneuvering processes (MPs) to recover the OOSL without breaking the electric limitations (ELs). The ELs are related to the maximum branch current, the node voltage, the load priority, the radiality maintenance of the SDS, the minimum system total power loss, the instruction sequence of the automatic-communication-switches (ACS), and the minimum number of ACSs. The proposed algorithm was tested under a 70-bus SDS with four electric supply feeders. The proposed algorithm achieved supply recovery for all OOSLs with efficiency of 100% after the occurrence of a CA on a single or double ACS without breaking the ELs. The proposed algorithm succeeded in achieving supply recovery for 97.6%, 97.1%, and 96.4% of the OOSLs after the simultaneous occurrence of a CA on three, four, and five ACSs, respectively, without breaking the ELs. The advantages of the proposed algorithm are a lack of dependency on the system size, a short electric supply recovery time within the range of 190–199 ms, a lack of dependency on distributed generation (DG), and the achievement of self-healing in the SDS following a single and two simultaneous CAs, as well as almost achieving self-healing under exposure to three, four, and five simultaneous CAs. Full article

This paper analyzes the impact of electromobility on distribution grids and voltage stability. In line with current legislation and the European Commission’s plans for the future of electromobility, the aim is to increase the share of electric vehicles to 50% by 2050. However, achieving this target can be challenging due to the characteristics and features of the electric vehicle charging stations and the associated charging methods, which can lead to constraints within the network. The analysis includes the integration of single-phase and three-phase chargers on a radial feeder, as well as the determination of the maximum number of vehicles that can be accommodated on a given feeder without compromising voltage stability. Five scenarios are evaluated using the DigSilent software package to gain a better understanding of the impact of electromobility on the distribution grid. Full article

This paper addresses the challenge of restoring electrical service in distribution systems (DS) under contingency scenarios using a genetic algorithm (GA) implemented in MATLAB. The proposed methodology seeks to maximize restored load, considering operational constraints such as line loadability, voltage limits, and radial topology preservation. It is evaluated with simulations on the IEEE 34-bus test system under four contingency scenarios that consider the disconnection of specific branches. The algorithm’s ability to restore service is demonstrated by identifying optimal auxiliary line reconnections. The method maximizes restored load, achieving between 97% and 99% load reconnection, with an average of 98.8% across the four cases analyzed. Bus voltages remain above 0.95 pu and below the upper limit. Furthermore, test feeder results demonstrate that line loadability is mostly below 60% of the post-reconfiguration loadability. Full article

This paper addresses the optimal conductor selection (OCS) problem in radial distribution networks, aiming to minimize the total costs associated with conductor investment and energy losses while ensuring voltage regulation and power balance as well as observing thermal limits. The problem is formulated as a mixed-integer nonlinear programming (MINLP) model and solved using a hybrid branch-and-bound (B&B), interior-point optimizer (IPO) approach within the Julia-based JuMP framework. Numerical validations on 27-, 33-, and 69-bus test feeders demonstrate cost-efficient conductor configurations. A multi-objective analysis is employed to construct the Pareto front, offering trade-offs between investment and operating costs. The impact of distributed energy resources (DERs) is also assessed, showing cost reductions when said resources provide reactive power support. The results confirm that the proposed MINLP approach outperforms conventional metaheuristics in terms of accuracy and reliability. Full article

Two-dimensional leaky-wave antennas offer effective, compact, single-feeder, easy-to-fabricate solutions to the longstanding problem of realizing a simultaneously directive and low-profile radiating device. These traveling-wave antennas have been thus proposed as wideband, reconfigurable, or frequency-scanning radiating structures in different application contexts, spacing from the microwave to terahertz frequency range. These diverse contexts call for a comprehensive guide to characterizing and designing two-dimensional leaky-wave antennas. In this work, a review of numerical techniques for the analysis of either quasi-uniform or radially periodic leaky-wave antennas is proposed in order to provide the reader with straightforward yet effective design guidelines. Theoretical results are corroborated through full-wave simulations of realistic three-dimensional models of the considered devices, thus demonstrating the effectiveness of the proposed methods. Full article

This paper addresses the problem of finding the optimal position and sizing of battery energy storage (BES) devices using a two-stage optimization technique. The primary stage uses mixed integer linear programming (MILP) to find the optimal positions along with their sizes. In the secondary stage, a relatively new algorithm called mountain gazelle optimizer (MGO) is implemented to find the technical feasibility of the solution, such as voltage regulation, energy loss reduction, etc., provided by the primary stage. The main objective of the proposed bi-level optimization technique is to improve the voltage profile and minimize the power loss. During the daily operation of the distribution grid, the charging and discharging behaviour is controlled by minimizing the voltage at each bus. The energy storage dispatch curve along with the locations and sizes are given as inputs to MGO to improve the voltage profile and reduce the line loss. Simulations are carried out in the MATLAB programming environment using an Australian radial distribution feeder, with results showing a reduction in system losses by 8.473%, which outperforms Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), and Cuckoo Search Algorithm (CSA) by 1.059%, 1.144%, and 1.056%, respectively. During the peak solar generation period, MGO manages to contain the voltages within the upper boundary, effectively reducing reverse power flow and enhancing voltage regulation. The voltage profile is also improved, with MGO achieving a 0.348% improvement in voltage during peak load periods, compared to improvements of 0.221%, 0.105%, and 0.253% by GWO, WOA, and CSA, respectively. Furthermore, MGO’s optimization achieves a reduction in the fitness value to 47.260 after 47 iterations, demonstrating faster and more consistent convergence compared to GWO (47.302 after 60 iterations), WOA (47.322 after 20 iterations), and CSA (47.352 after 79 iterations). This comparative analysis highlights the effectiveness of the proposed two-stage optimization approach in enhancing voltage stability, reducing power loss, and ensuring better performance over existing methods. Full article

Current trends in the use of energy storage, electric mobility, and the integration of a large number of distributed generations at the distribution level can have positive effects on reducing loads and losses in the network. An excessive and uncontrolled level of integration of the above trends leads to various problems related to the power quality. Distortion of the voltage and current waveforms caused by nonlinear loads is manifested through harmonics and can be classified as one of the most essential parameters of electricity quality. Reducing harmonics thus becomes the primary goal for improving the quality of electricity at the distribution level. This paper, along with a detailed analysis of the literature, provides an overview of different views on the problems of optimal allocation of active filters and emphasizes the importance that the problem of optimal allocation of active filters should be based on the variability of the harmonic spectrum as a function of time. Installing devices for reducing harmonics in the network, in terms of improving the quality of electricity, is one of the essential elements from both a technical and an economic point of view and can solve these challenges. Therefore, it is necessary to develop methods for solving the problem of determining the position, size and parameters of filters, as well as the number of buses on which such devices can be integrated. Applying optimization techniques enables the development of more realistic models for applying active power filters. The research in this paper is directed toward developing a co-simulation optimization model to determine optimal settings of the parallel APF due to harmonic reduction in a real low-voltage network using particle swarm optimization for 24 h intervals. The research in this paper was conducted on a real radial low-voltage feeder, where at each node, the variability of production and/or consumption is taken, which is obtained on the basis of real measurements and tests. Based on this, the position and dimensioning of the shunt active power filters (APFs) depend on the load range within a 24 h interval at all nodes in the observed time interval. Furthermore, the paper emphasizes the importance of observing Voltage Total Harmonic Distortion (THD_V) on the busbars in the depth of the feeder as well as the importance of observing THD_V in each phase. Full article

This study examines the impact of Inverter-Based Distributed Generators (IIDGs) on short-circuit currents detected by the main relay at the head of a radial feeder. It highlights how changes in fault currents induced by these inverter technologies can significantly affect the effectiveness and reliability of network protection systems. Key variables, such as the level of IIDGs penetration and the relative location of faults with respect to the relay, have been identified as influential factors. The significance of these findings lies in their contribution to a deeper understanding of how inverter fault responses and the integration of IIDGs alter fault currents. To mitigate the adverse effects associated with the insertion of IIDGs, a fault-detection and tripping strategy based on the inverse-time overcurrent curve is proposed. The suggested strategies not only improve fault detection accuracy but also ensure an appropriate response to variations in network conditions. Full article

The radial distribution of material feeding onto a screen surface is an important factor affecting vibration screening performance, and it is also the main basis for the optimization of the operating parameters of a vibration screening system. In this paper, based on near-infrared properties, a real-time measurement method for the mass flow rate of grain vibration feeding was proposed. A laser emitter and a silicon photocell were used as the measuring components, and the corresponding signal processing circuit mainly composed of a T-type I/V convertor, a voltage follower, a low-pass filter, and a setting circuit in series was designed. Calibration test results showed that the relationship between grain mass flow rate and output voltage could be described using the Gaussian regression model, and the coefficient of determination was greater than 0.98. According to the working principle of the grain cleaning system of combine harvesters, the dynamic characteristics of grain vibration feeding were analyzed using discrete element method (DEM) simulations, and the monitoring range of the sensor was determined. Finally, grain mass flow rate measurement tests were carried out on a vibration feeding test rig. The results indicated that the grain mass measurement error could be controlled within 5.0% with the average grain mass flow rate in the range of 3.0–5.0 g/mm·s. The proposed measurement method has potential application value in the uniform feeding control systems of vibration feeders. Full article

The paper provides a novel approach for controllably meshing traditional medium-voltage networks by means of a fast-charging parking station with multiple points of delivery connected to different radial feeders. Regulating power flows at each point of delivery while the charging service is being provided, which means actively controlling power exchanges between radial distribution feeders can significantly increase the hosting capacity of the power system. Remarkable benefits are expected when the distribution networks to which the charging infrastructure is connected differ in terms of main characteristics, e.g., rated voltage level, end-user type and operating profiles, and the number and type of renewable plants. The paper focuses on technical targets, such as loss reduction and power quality in terms of admitted voltage deviation from the rated value. The power exchanges between distribution feeders are made possible by a controlled DC link, where bi-directional DC/DC converters are connected so as to charge or discharge vehicles according to the Vehicle-To-Grid approach. A multiplexer topology in which several vehicles can be alternatively connected to the same DC/DC converter is modeled. The proposed concept can contribute to network flexibility by controllably meshing distribution feeders and, jointly, by modulating charging processes according to assigned charging constraints. Full article

The distribution network is a crucial component of the power system as it directly connects to users and serves the purpose of distributing power and balancing the load. With the integration of new energy sources through distributed generation (DG), the distribution network has undergone a transformation from a single power radial network into a complex multi-source network. Consequently, traditional fault location methods have proven inadequate in this new network structure. Therefore, the focus of this paper is to investigate fault location techniques specifically tailored for DG integration into distribution networks. This paper analyzes how fault conditions impact the characteristics of single-phase grounding faults and extracts spectral feature quantities to describe differences in zero-sequence currents under various fault distances. This paper also proposes a fault location method based on centroid frequency and a BPNN (back propagation neural network). The method uses centroid frequency to describe the features of zero-sequence currents; to simulate the mapping relationship between fault conditions and spectral features, BPNN is employed. The mapping relationship differs for different lines and distribution networks. When a line faults, the spectral features are calculated, along with the transition resistance and fault closing angle. The corresponding mapping relationship is then called upon to complete distance measurements. This location method can be applied to various types of distribution lines and fault conditions with high accuracy. Even with insufficient training samples, sample expansion can ensure accuracy in fault distance measurement. We built a distribution network consisting of four feeders with different types and lengths of each line on Simulink and verified the effectiveness of the proposed method by setting different fault conditions. The results suggest that the method has a clear advantage over other frequency domain-based approaches, especially for hybrid lines and feeder lines with branches in distribution networks. Additionally, the method achieves a measurement accuracy within a range of 100 m. Full article