Search Results (109)

Severe weather events increasingly threaten the reliability of low-voltage distribution networks (LVDNs). Existing fault restoration strategies primarily focus on medium-voltage networks, leaving LVDNs highly vulnerable during prolonged grid outages. To enhance the off-grid operation capability of LVDNs, this paper proposes a coordinated planning model integrating flexible interconnection devices and grid-forming energy storage systems (GFM-ESSs). First, this study develops a hierarchical planning framework for LVDNs to jointly optimize the allocation of soft open points (SOPs) and grid-forming energy storage systems (GFM-ESSs), with the aim of reducing both interruption-related losses and equipment investment in distribution transformer areas. Second, active support capability evaluation indices are formulated for LVDNs, facilitating a quantitative assessment of how the placement and sizing of SOPs and GFM-ESSs influence the effectiveness of active support. The nonconvex model is relaxed and efficiently solved using second-order cone programming (SOCP). Case study results demonstrate that the proposed method leverages the spatial power transfer ability of flexible interconnections to reduce the required GFM-ESS capacity, thereby achieving optimal economic efficiency and enhanced active support performance. Furthermore, quantitative analysis reveals that placing the GFM-ESS at intermediate nodes yields the best active support effect. Ultimately, the coordinated planning scheme effectively mitigates voltage limit violations and ensures a highly reliable power supply during severe grid outages. Full article

This paper investigates harmonic compensation for an LCL-filtered cascaded H-bridge (CHB) STATCOM operating in medium-voltage distribution networks under grid-frequency deviations and nonlinear loads. A hybrid current control strategy is proposed by combining a deadbeat (DB) inner-current loop with a Thiran all-pass filter-based frequency-adaptive repetitive controller (FARC). Weighted average inductor current (WAIC) feedback is adopted to reduce the third-order LCL filter to an equivalent first-order plant, thereby simplifying the current loop design while retaining the dominant low-frequency dynamics. The Thiran all-pass fractional delay filter is then embedded in the repetitive controller to realize a noninteger-period internal model at a fixed sampling frequency. This enables the controller to maintain harmonic compensation accuracy when the grid frequency deviates from its nominal value. A 10 kV LCL-filtered CHB STATCOM model is developed in MATLAB/Simulink, and the proposed method is compared with a conventional repetitive controller (CRC) under nominal frequency, frequency drift, nonlinear loading, harmonic load-switching conditions and grid impedance variation. Simulation results show that the proposed controller reduces the grid-current THD from 4.35% to 3.88% at 50 Hz, from 5.20% to 2.37% at 49.6 Hz, and from 6.51% to 3.56% at 50.4 Hz. In the tested frequency range of 49.5–50.5 Hz, the proposed method also maintains the power factor close to unity. These quantitative results demonstrate improved frequency robustness, harmonic suppression, and current-tracking performance compared with the CRC scheme, indicating that the proposed method can enhance STATCOM-based power quality compensation and support more reliable and efficient operation of medium-voltage distribution networks. Full article

This paper presents a lightweight method for assessing the resilience of power distribution systems that integrates climate and infrastructure data through impact chains and a probabilistic approach, while minimizing data integration and implementation complexity. The method is demonstrated for heatwave hazards by combining network characteristics, failure probabilities of heat-sensitive components (e.g., medium-voltage cable joints), and location-specific climate projections to generate spatial maps of failure risk and network resilience. These maps support the identification and prioritization of critical components requiring intervention. Critical segments are then further analyzed using probabilistic resilience metrics to compare alternative adaptation strategies. Overall, this work contributes a practically applicable, low-complexity methodology for identifying the weakest portions of distribution networks, along with a more in-depth probabilistic approach for assessing their climate resilience. The comprehensive framework is illustrated through a case study of a representative portion of the Italian electricity distribution system in the urban area of Rome. It is implemented in a test environment that reflects realistic distribution network data structures and automatically integrates climate data from established online repositories. Full article

Steel tape armored multicore cables are critical components in the transmission and distribution of power in medium- and low-voltage networks. It is difficult to measure current in the individual conductors of multicore cables because they are enclosed within multilayer protective structures (e.g., armor). The magnetic field–current inversion method provides a noncontact alternative for measuring conductor currents, derived from externally measured magnetic fields. However, the nonlinear magnetization effects of the steel tape armor disrupt the linear relationship between the magnetic field and currents, making accurate measurements challenging. To address this issue, we propose a noncontact current measurement method that incorporates the nonlinear effects of the armor layer. This method involves pre-calibrating the coefficient matrices, determining the angle formed between the magnetic sensor array and the multicore cable, and applying nonlinear fitting. This achieves a current measurement accuracy less than 5% and 5° in relative error and phase error, respectively. The proposed method avoids computationally intensive inverse operations, thereby enabling the realization of lightweight, low-cost current measurement terminals for practical field applications. Full article

Rapid electrification of road transport and growing shares of variable renewable generation are pushing urban low-voltage feeders toward their operating limits. Uncoordinated electric vehicle (EV) charging can create transformer overloads, voltage violations, and unfair delays, while most existing smart charging schemes either ignore distribution network constraints or treat fairness and risk in an ad hoc way. This paper proposes a city-scale hierarchical scheduling framework that coordinates EV charging and vehicle-to-grid (V2G) services under renewable variability. In the upper layer, a LinDistFlow-based optimal power flow computes feeder-constrained power envelopes and shadow prices over a rolling horizon, capturing transformer and voltage limits under photovoltaic (PV) uncertainty. In the lower layer, each station solves a queue-aware receding-horizon optimization that allocates charging/V2G set points across plugs using α-fair and lexicographic objectives, with conditional value-at-risk (CVaR) constraints on waiting times and state-of-charge (SoC) shortfalls. A digital twin of a medium-sized city with 24 stations (238 plugs) on five feeders and PV shares between 25% and 55% is used for evaluation. Compared with uncoordinated charging and myopic baselines, the proposed scheduler reduces feeder peak loading and PV curtailment while improving user experience and equity: average waits and 90% CVaR of waits are lowered, the Gini coefficient of waiting times drops (e.g., from 0.31 to 0.22), and SoC shortfalls are significantly reduced, all while respecting voltage limits. Each receding-horizon step executes in under 30 s on commodity hardware, indicating that the framework is practical for real-time deployment in city-scale smart charging platforms. Full article

The growing penetration of Electric Vehicles (EVs) in power distribution networks presents both challenges and opportunities for grid operators and planners. This paper provides a comprehensive review of recent advances in optimization techniques and machine learning (ML) approaches for the efficient management of EV charging and integration in low- and medium-voltage distribution systems. Optimization methods are analyzed with reference to their objectives—such as load flattening, voltage regulation, loss minimization, and infrastructure cost reduction—and their capability to handle multi-objective, stochastic, and real-time constraints. Concurrently, the role of ML is explored in load forecasting, user behavior modeling, anomaly detection, and adaptive control strategies. Particular attention is given to hybrid approaches that combine optimization algorithms (e.g., MILP, heuristic methods) with data-driven models (e.g., neural networks, reinforcement learning), highlighting their effectiveness in enhancing grid flexibility and resilience. This review adopts a unified system-level perspective that links EV management objectives, optimization techniques, and machine learning-based solutions within distribution networks. In addition, particular attention is devoted to data availability, reproducibility, and practical deployment aspects, with the aim of identifying current limitations and providing actionable insights for future research and real-world applications. This study aims to support the development of intelligent energy management strategies for EVs, fostering a sustainable and reliable evolution of distribution networks. Full article

The high integration of renewables like distributed photovoltaic (PV) into medium- and low-voltage distribution networks causes bidirectional power flows, increased voltage fluctuations, and operational uncertainty. Traditional power flow models struggle to balance efficiency and accuracy for multi-period optimization. This paper proposes a dual-objective voltage optimization method based on a Holomorphic Embedding time-series power flow model. First, a recursive relationship for nodal voltage power series expansion is derived, revealing the linear superposition of first-order coefficients with power injection changes and the rapid decay of higher-order terms. A linearized analytical model neglecting higher-order terms is built, improving the computational efficiency of time-series power flow calculations while maintaining accuracy. Then, integrating energy storage systems and static var compensators, a dual-objective optimization model minimizing voltage deviation and daily operational cost is formulated. Tests on a practical 91-node rural distribution system show that the proposed power flow model maintains a voltage error below 0.25% compared to the Newton–Raphson method across various PV integration scenarios, and the optimization reduces computation time by about 61.3% versus the Second-Order Cone Programming method, validating its advantages in precision and efficiency for balancing voltage quality and economy. Full article

High-penetration integration of distributed photovoltaics (PV) into distribution networks introduces significant challenges regarding voltage limit violations and fluctuations. To address these issues, this manuscript proposes a hierarchical coordinated voltage control strategy for medium- and low-voltage distribution networks utilizing a cloud-edge collaboration architecture. The research methodology involves constructing a multi-objective optimization model at the cloud layer to minimize network losses and voltage deviations, solved via an improved Honey Badger Algorithm (HBA). Simultaneously, at the edge layer, a multi-mode coordinated control strategy incorporating Virtual Synchronous Generator (VSG) technology is developed to provide fast reactive power support and inertial response. Through simulation analysis on an IEEE 33-node test system, the findings demonstrate that the proposed strategy significantly mitigates voltage fluctuations and enhances the hosting capacity of distributed energy resources. The study concludes that the cloud-edge framework effectively decouples control time-scales, ensuring both global economic operation and local transient stability. These results are significant for advancing the resilient operation of active distribution networks with high renewable penetration. Full article

As the automation and intelligence of low-voltage distribution networks continue to advance, the inter-layer coupling between medium- and low-voltage distribution networks is increasingly strengthened, making traditional fixed-point iteration methods inadequate for distributed power flow calculation in such a collaborative framework. To address this issue, this paper proposes a distributed power flow calculation method for medium- and low-voltage distribution networks based on edge intelligence. First, a cooperative operational framework for medium- and low-voltage distribution networks is designed by integrating edge intelligence technology. Then, a distributed power flow calculation model is established, and its fixed-point iterative characteristics are analyzed. A convergence index calculation method based on small perturbations is proposed, followed by an iterative algorithm based on continuous intersection estimation. Finally, simulation case studies validate the proposed method in terms of accuracy, convergence, and computational efficiency, demonstrating its capability to meet the modeling and analytical needs of power flow calculation in medium- and low-voltage distribution networks, providing methodological support for the development of distributed intelligent power grids. Full article

To overcome the limitations of lengthy laboratory testing cycles and insufficient on-site responsiveness, this study developed an online rapid monitoring device for volatile organic compounds (VOCs) in soil–water–air systems based on photoionization detection (PID) technology. The device integrates modular sensor units, incorporates an electromagnetic valve-controlled multi-medium adaptive switching system, and employs an internal heating module to enhance the volatilization efficiency of VOCs in water and soil samples. An integrated system was developed featuring “front-end intelligent data acquisition–network collaborative transmission–cloud-based warning and analysis”. The effects of different temperatures on the monitoring performance were investigated to verify the reliability of the designed system. A polynomial fitting model between concentration and voltage was established, showing a strong correlation (R² > 0.97), demonstrating its applicability for VOC detection in environmental samples. Field application results indicate that the equipment has operated stably for nearly three years in a mining area of Shandong Province and an industrial park in Anhui Province, accumulating over 600,000 valid data points. These results demonstrate excellent measurement consistency, long-term operational stability, and reliable data acquisition under complex outdoor conditions. The research provides a distributed, low-power, real-time monitoring solution for VOC pollution control in mining and industrial environments. It also offers significant demonstration value for standardizing on-site emergency monitoring technologies in multi-media environments and promoting the development of green mining practices. Full article

This paper proposes an integrated sensor for voltage and current distribution network insulators, based on electric field coupling and TMR magnetic sensing, to address the issues of traditional voltage and current separation measurement, insulator safety after primary and secondary fusion, uncertainty in voltage measurement gain, and interference resistance in TMR current measurements. Through simulation and optimization, the design of the embedded voltage-sensing unit in the insulator is achieved, ensuring uniform electric field distribution, determining the transfer function, and minimizing partial discharge, thereby ensuring insulator safety and improving voltage measurement accuracy. Additionally, a self-integrating circuit design is used to widen the low-frequency dynamic range and increase the voltage division ratio. Moreover, an open-type two-stage magnetic ring current sensor based on TMR is proposed, with optimized magnetic ring dimensions to detect currents from low to medium ranges, addressing eccentricity errors and improving sensitivity, immunity to interference, and magnetic field uniformity. The experimental results show that this integrated sensor can effectively ensure measurement accuracy, stability, and dynamic range. Full article

The integration of distributed energy resources into distribution networks creates operational challenges, including voltage instability and power quality issues. While battery energy storage systems (BESSs) can address these challenges, research has focused primarily on transmission-level applications or single services. This paper bridges this gap through a comprehensive review of BESS technologies and control strategies for multi-service ancillary support in distribution networks. Real-world case studies demonstrate BESS effectiveness: Hydro-Québec’s $1.2$ MW system maintained voltage within 5% and responded to frequency events in under 10 ms; Germany’s hybrid 5 MW M5BAT project optimized multiple battery chemistries for different services; and South Africa’s Eskom deployment improved renewable hosting capacity by 15–70% using modular BESS units. The analysis reveals grid-forming inverters and hierarchical control architectures as critical enablers, with model predictive control optimizing performance and droop control ensuring robustness. However, challenges like battery degradation, regulatory barriers, and high costs persist. This paper identifies future research directions in degradation-aware dispatch, cyber-resilient control, and market-based valuation of BESS flexibility services. By combining theoretical analysis with empirical results from international deployments, this study provides utilities and policymakers with actionable insights for implementing BESS in modern distribution grids. Full article

Large-scale integration of distributed energy resources (DERs) into distribution networks causes topological-operational situations with multidirectional power flows. One of the main components of distribution networks is the power transformer, which does not have the capabilities for real-time control of distribution network parameters with DERs. The use of solid-state transformers (SSTs) for connecting medium-voltage (MV) and low-voltage (LV) distribution networks of both alternating and direct current has great potential for constructing new distribution networks and enhancing the existing ones. Electricity losses in distribution networks can be reduced through the establishment of MV and LV DC networks. In hybrid AC-DC distribution networks, the SSTs can be especially effective, ensuring compensation for voltage dips, fluctuations, and interruptions; regulation of voltage, current, frequency, and power factor in LV networks; and reduction in the levels of harmonic current and voltage due to the presence of power electronic converters (PECs) and capacitors in the DC link. To control the operating parameters of hybrid distribution networks with solid-state transformers, it is crucial to develop and implement advanced control systems (CSs). The purpose of this review is a comprehensive analysis of the features of the creation of CSs SSTs when they are used in hybrid distribution networks with DERs to identify the most effective principles and methods for managing SSTs of different designs, which will accelerate the development and implementation of CSs. This review focuses on the design principles and control strategies for SSTs, guided by their architecture and intended functionality. The architecture of the solid-state transformer control system is presented with a detailed description of the main stages of control. In addition, the features of the SST CS operating under various topologies and operating conditions of distribution networks are examined. Full article

Soft open point (SOP) offers a viable alternative to traditional tie switches for optimizing power flow distribution between connected feeders, thereby improving power quality and enhancing the reliability of distribution networks (DNs). Among existing medium-voltage (MV) SOP demonstration projects, the modular multilevel converter (MMC) back-to-back voltage source converter (BTB-VSC) is the most commonly adopted configuration. However, MMC BTB-VSC suffers from high cost and significant volume, with device requirements increasing substantially as the number of feeders grows. To address these challenges, this paper proposes a novel star-connected cascaded H-bridge (CHB) STATCOM SOP (SCS-SOP). The SCS-SOP integrates the static synchronous compensator (STATCOM) and low-voltage (LV) BTB-VSC into a single device, enabling reactive power support within feeders and active power exchange between feeders, while achieving reduced component cost and volume, simplified power decoupling control, and increasing power quality management capabilities. The topology derivation, configuration, operational principles, and control strategies of the SCS-SOP are elaborated. Finally, simulation and experimental models of a two-port 3 Mvar/300 kW SCS-SOP are developed, with results validating the theoretical analysis. Full article

This study investigates the impact of Time-of-Use (TOU) scheduling and battery energy storage systems (BESS) on voltage stability in a typical Malaysian medium-voltage distribution network with high photovoltaic (PV) system penetration. The analyzed network comprises 110 nodes connected via eight feeders to a pair of 132/11 kV, 15 MVA transformers, supplying a total load of 20.006 MVA. Each node is integrated with a 100 kW PV system, enabling up to 100% PV penetration scenarios. A hybrid mitigation strategy combining TOU-based load shifting and BESS was implemented to address voltage violations occurring, particularly during low-load night hours. Dynamic simulations using DIgSILENT PowerFactory were conducted under worst-case (no load and peak load) conditions. The novelty of this research is the use of real rural network data to validate a hybrid BESS–TOU strategy, supported by detailed sensitivity analysis across PV penetration levels. This provides practical voltage stabilization insights not shown in earlier studies. Results show that at 100% PV penetration, TOU or BESS alone are insufficient to fully mitigate voltage drops. However, a hybrid application of 0.4 MWh BESS with 20% TOU load shifting eliminates voltage violations across all nodes, raising the minimum voltage from 0.924 p.u. to 0.951 p.u. while reducing active power losses and grid dependency. A sensitivity analysis further reveals that a 60% PV penetration can be supported reliably using only 0.4 MWh of BESS and 10% TOU. Beyond this, hybrid mitigation becomes essential to maintain stability. The proposed solution demonstrates a scalable approach to enable large-scale PV integration in dense rural grids and addresses the specific operational characteristics of Malaysian networks, which differ from commonly studied IEEE test systems. This work fills a critical research gap by using real local data to propose and validate practical voltage mitigation strategies. Full article