Search Results (202)

In this work, a comprehensive literature review on power-routing devices is presented, outlining their current design principles and potential uses. Additionally, a comprehensive loss model for Modular Multilevel Converters (MMCs) in the context of power routers (PRs), a promising technology for enhancing flexibility and efficiency in future smart and hybrid AC–DC grids. Despite their potential, large-scale PR deployment is still limited by the lack of accurate and validated loss models. To address this gap, a detailed analytical model based on the Marquardt approach is proposed, capturing both conduction and switching losses in converter-based PRs. The model is validated through analytical comparison and PLECS simulations, showing strong agreement with theoretical and experimental data. Four case studies are presented to assess the effect of parameters such as power factor, active and reactive power, and the number of submodules on the overall converter losses. The results demonstrate that PR efficiency improves with optimized converter design and proper parameter selection. Full article

As the proportion of renewable energy generation in the power grid continues to rise, the operational state of the power system changes frequently with fluctuations in renewable power output. However, the traditional fixed-weight multi-objective reactive power optimization method lacks the necessary flexibility and adaptability, as it is unable to dynamically adjust the priority levels of different objectives based on real-time operating conditions (such as load fluctuations and changes in network structure). As a result, its optimization decisions may deviate from the system’s most urgent economic or security needs. To address this issue, this paper proposes an adaptive multi-objective reactive power optimization control method. The proposed approach formulates the objective function as the weighted sum of system active power loss and voltage deviation at the grid connection point, with weight coefficients adaptively adjusted based on the voltage deviation at the grid connection point. First, the relationship between voltage fluctuations at the offshore wind farm grid connection point and active/reactive power output is analyzed, and a corresponding reactive power allocation model is established. Second, taking into account the input–output characteristics of wind turbine generators and static var compensators, a reactive power control model is constructed. Third, considering offshore operational constraints such as power and voltage limits, a weighted variation particle swarm optimization algorithm (WVPSO) is developed to solve for the reactive power control strategy. Finally, the proposed method is validated through tests using a practical offshore wind farm as a case study. The test results demonstrate that, compared with the traditional fixed-weight multi-objective reactive power optimization approach, the proposed method can rapidly adjust the priority of each optimization objective according to the real-time grid conditions, achieving effective coordinated optimization of both active power loss and voltage at the grid connection point, and the voltage deviation is kept within 5%, even with power system fluctuations. In addition, compared with the traditional PSO algorithm, for various test situations, WVPSO exhibits above 15% improvement in solution speed and enhanced solution accuracy. Full article

With the increasing penetration of distributed energy resources (DERs) and the rapid development of active distribution networks, the traditional centrally controlled operation mode can no longer meet the flexibility and autonomy requirements under the multi-dimensional coupling of sources, networks, loads, and storage. To achieve regional energy self-balancing and autonomous operation, this paper proposes a partitioned configuration method for energy storage systems (ESSs) in energy-autonomous distribution networks based on autonomous unit division. First, the concept and hierarchical structure of the energy-autonomous distribution network and its autonomous units are clarified, identifying autonomous units as the fundamental carriers of the network’s autonomy. Then, following the principle of “tight coupling within units and loose coupling between units,” a comprehensive indicator system for autonomous unit division is constructed from three aspects: electrical modularity, active power balance, and reactive power balance. An improved genetic algorithm is applied to optimize the division results. Furthermore, based on the obtained division, an ESS partitioned configuration model is developed with the objective of minimizing the total cost, considering the investment and operation costs of ESSs, power purchase cost from the main grid, PV curtailment losses, and network loss cost. The model is solved using the CPLEX solver. Finally, a case study on a typical multi-substation, multi-feeder distribution network verifies the effectiveness of the proposed approach. The results demonstrate that the proposed model effectively improves voltage quality while reducing the total cost by 20.89%, ensuring optimal economic performance of storage configuration and enhancing the autonomy of EADNs. Full article

The proliferation of power converters in modern energy production systems has led to increased harmonic content due to the commutation of active switching devices. This increase in harmonics contributes to lower system efficiency, reduced power factor, and consequently, a higher reactive power requirement. To address these issues, this paper presents both simulation and experimental results of various control strategies implemented on Parallel Voltage Source Inverters (PVSI) for harmonic mitigation. The proposed control strategies are categorized into direct and indirect control methods. The direct control techniques implemented include the instantaneous power method (PQ), the synchronous algorithm (DQ), the maximum principle method (MAX), the algorithm based on synchronization of current with the voltage positive-sequence component (SEC-POZ), and two methods employing the separating polluting components approach using a band-stop filter and a low-pass filter. The main innovation in these active power filter (APF) control strategies, compared to traditional or existing technologies, is the real-time digital implementation on high-speed platforms, specifically FPGAs. Unlike slower microcontroller-based systems with limited processing capabilities, FPGA-based implementations allow parallel processing and high-speed computation, enabling the execution of complex control algorithms with minimal latency. Additionally, the enhanced reference current generation achieved through the seven applied methods provides precise harmonic compensation under highly distorted and nonlinear load conditions. Another key advancement is the integration with Smart Grid functionalities, allowing IoT connectivity and remote diagnostics, which enhances system monitoring and operational flexibility. Following validation on an experimental test bench, these algorithms were implemented and tested on industrial APF prototypes powered by a standardized three-phase network supply. All control strategies demonstrated an effective reduction in total harmonic distortion (THD) and improvement in power factor. Experimental findings were used to provide recommendations for choosing the most effective control solution, focusing on minimizing THD and enhancing system performance. Full article

Quantum dots (QDs) have tremendous potential for next-generation displays due to their high color purity, photoluminescence efficiency, and power efficiency. In this work, we present a simple and cost-effective method for fabricating flexible single- and multiple-layer films, and they can be detached and attached to the outside of OLEDs as a light-scattering and color-conversion layer. Light extraction efficiency is enhanced by forming low-density structures by using the reactive ion etching (RIE) process. As a result, the QD/PDMS composite film allowed for color conversion and achieved an excellent light extraction efficiency of up to 9.2%. Furthermore, the QD/PDMS composite film and greenish-blue OLED produced white light (CIEx,y = 0.28, 0.41), demonstrating the potential for application in broad areas, from flexible displays to lighting. The method provides a simple and cost-effective alternative to conventional processes. Full article

Governments in many countries worldwide are pursuing determined policies of science, technology, and infrastructure modernization. By examining the closure of the Fujiapo Coach Bus Station in Wuhan, China, this study sheds light on the socio-cultural effects of large-scale infrastructure decommissioning caused by modernization. A qualitative analysis of 26,163 comments from the internet platform Douyin, as well as 26 interviews, is used to deeply understand interpretative contests contained in respondents’ narratives about the decommissioned infrastructure and suggest an extended application of Bijker’s interpretive flexibility framework of social construction of technology (SCOT) as an analytical framework. By theorizing infrastructure transitional disconnectivity as a dynamic catalyst that reactivates interpretive contests through the three dimensions of temporal compression, memory capitalization, and power reconfiguration, this research demonstrates how experiences of infrastructure disconnectivity events expand the interpretive flexibility’s closure assumption, drawing implications about the necessity of socio-cultural considerations for balanced strategies when navigating infrastructure transitions. Full article

This paper investigates the role of reactive power support from Distributed Generation (DG) units in improving voltage compliance and maximizing active power injection in medium-voltage distribution networks. Using the IEEE 34-Node Test Feeder as a case study, a simplified single-phase equivalent model was developed, excluding voltage regulators, shunt capacitors, and step-down transformers to focus on the intrinsic voltage behavior of the feeder. An AC Optimal Power Flow (OPF) model was formulated in Pyomo and solved with Interior Point Optimizer (IPOPT) to evaluate two operational scenarios: (i) DG injecting a fixed 1 MW of active power without reactive power support, and (ii) DG injecting the same active power with optimized reactive power dispatch within $\pm 0.5$ MVAr, subject to apparent power constraints. Simulation results show that allowing reactive power flexibility increases the number of feasible DG connection points, improves minimum bus voltages, and reduces the occurrence of voltage limit violations. The findings suggest that modest reactive power capabilities can significantly enhance the hosting capacity of radial distribution feeders without requiring costly network reinforcements. Full article

The rapid growth of renewable energy integration in modern power systems brings new challenges in terms of stability and quality of electricity supply. Hybrid AC/DC microgrids represent a promising solution to integrate photovoltaic panels (PV), wind turbines, fuel cells, and storage units with flexibility and efficiency. However, maintaining adequate power quality (PQ) under variable conditions of generation, load, and grid connection remains a critical issue. This paper presents the modelling, implementation, and validation of a hybrid AC/DC microgrid equipped with a fuzzy-logic-based energy management system (EMS). The study combines PQ assessment, measurement architecture, and supervisory control for technical compliance and economic efficiency. The microgrid integrates a combination of PV array, wind turbine, proton exchange membrane fuel cell (PEMFC), battery storage system, and heterogeneous AC/DC loads, all modelled in MATLAB/Simulink using a physical-network approach. The fuzzy EMS coordinates distributed energy resources by considering power imbalance, battery state of charge (SOC), and dynamic tariffs. Results demonstrate that the proposed controller maintains PQ indices within IEC/IEEE standards while eliminating short-term continuity events. The proposed EMS prevents harmful deep battery cycles, maintaining SOC within 30–90%, and optimises fuel cell activation, reducing hydrogen consumption by 14%. Economically, daily operating costs decrease by 10–15%, grid imports are reduced by 18%, and renewable self-consumption increases by approximately 16%. These findings confirm that fuzzy logic provides an effective, computationally light, and uncertainty-resilient solution for hybrid AC/DC microgrid EMS, balancing technical reliability with economic optimisation. Future work will extend the framework toward predictive algorithms, reactive power management, and hardware-in-the-loop validation for real-world deployment. Full article

Current power systems are facing noticeable power quality (PQ) performance deterioration, which has been attributed to nonlinear loads, distributed generation, and extensive renewable energy infiltration (REI). These conditions cause voltage sags, harmonic distortion, flicker, and disadvantageous power factors. The traditional PI/PID-based scheme of control, when applied to Flexible AC Transmission Systems (FACTSs), demonstrates low adaptability and low anticipatory functions, which are required to operate a grid in real-time and dynamic conditions. Artificial Intelligence (AI) opens proactive, reactive, or adaptive and self-optimizing control schemes, which reformulate FACTS to thoughtful, data-intensive power-system objects. This literature review systematically studies the convergence of AI and FACTS technology, with an emphasis on how AI can improve voltage stability, harmonic control, flicker control, and reactive power control in the grid formation of various types of grids. A new classification is proposed for the identification of AI methodologies, including deep learning, reinforcement learning, fuzzy logic, and graph neural networks, according to specific FQ goals and FACTS device categories. This study quantitatively compares AI-enhanced and traditional controllers and uses key performance indicators such as response time, total harmonic distortion (THD), precision of voltage regulation, and reactive power compensation effectiveness. In addition, the analysis discusses the main implementation obstacles, such as data shortages, computational time, readability, and regulatory limitations, and suggests mitigation measures for these issues. The conclusion outlines a clear future research direction towards physics-informed neural networks, federated learning, which facilitates decentralized control, digital twins, which facilitate real-time validation, and multi-agent reinforcement learning, which facilitates coordinated operation. Through the current research synthesis, this study provides researchers, engineers, and system planners with actionable information to create a next-generation AI-FACTS framework that can support resilient and high-quality power delivery. Full article

With the large-scale integration of single-phase distributed photovoltaic systems into distribution grids, issues such as mismatched generation and load, overvoltage, and three-phase imbalance may arise in the distribution network. A multi-objective optimization method for flexible distribution networks incorporating a four-leg soft open point (F-SOP) is proposed based on fuzzy chance constraints. First, a mathematical model for the F-SOP’s loss characteristics and power control was established based on the three-phase four-arm topology. Considering the impact of source load uncertainty on voltage regulation, a multi-objective complementary voltage regulation architecture is proposed based on fuzzy chance constraint programming. This architecture integrates F-SOP with conventional reactive power compensation devices. Next, a multi-objective collaborative optimization model for distribution networks is constructed, with network losses, overall voltage deviation, and three-phase imbalance as objective functions. The proposed model is linearized using second-order cone programming. Finally, using an improved IEEE 33-node distribution network as a case study, the effectiveness of the proposed method was analyzed and validated. The results indicate that this method can reduce network losses by 30.17%, decrease voltage deviation by 46.32%, and lower three-phase imbalance by 57.86%. This method holds significant importance for the sustainable development of distribution networks. Full article

Nanomaterials (NMs) possess unique physicochemical features that set them apart from bulk counterparts. Their adjustable properties provide remarkable flexibility, giving rise to a wide array of variants. However, these attributes can also trigger complex biological interactions, particularly the generation of reactive oxygen species (ROS), which are central to nanomaterial-induced cytotoxicity. The ambivalent nature of ROS, essential for physiological signaling yet harmful when dysregulated, can lead to substantial health consequences. The scarcity of reliable toxicity and safety data, together with the inadequacies of conventional testing methods, highlights the urgent need for more effective strategies to assess nanomaterial-related hazards and risks. Given the intricate interplay between NMs and biological systems, computational approaches, particularly machine learning (ML), have emerged as powerful tools to model ROS dynamics, predict cytotoxic outcomes, and optimize nanomaterial design. This review highlights recent advances in applying ML to predict both the generation and neutralization of ROS by diverse NMs and to identify the critical determinants underlying ROS-mediated toxicity. These insights provide new opportunities for predictive nanotoxicology and the development of safer, application-tailored NMs. Full article

With the continuous increase in the penetration of single-phase microgrids in low-voltage distribution networks (LVDNs), the phase asymmetry of source–load distribution has made the problem of three-phase imbalance increasingly prominent. To address this issue, this paper proposes an active–reactive power coordinated optimization model for distribution network–microgrid clusters considering three-phase imbalance mitigation. The model is formulated within a master–slave game framework: in the upper level, the distribution network acts as the leader, formulating time-of-use prices for active and reactive power based on day-ahead forecast data with the objective of minimizing operating costs. These price signals guide the flexible loads and photovoltaic (PV) inverters of the lower-level microgrids to participate in mitigating three-phase imbalance. In the lower level, each microgrid responds as the follower, minimizing its own operating cost by determining internal scheduling strategies and power exchange schemes with the distribution network. Finally, the resulting leader–follower game problem is transformed into a unified constrained model through strong duality theory and formulated as a mixed-integer second-order cone programming (MISOCP) problem, which is efficiently solved using the commercial solver Gurobi. Simulation results demonstrate that the proposed model fully exploits the reactive power compensation potential of PV inverters, significantly reducing the degree of three-phase imbalance. The maximum three-phase voltage unbalance factor decreases from 3.98% to 1.43%, corresponding to an overall reduction of 25.87%. The proposed coordinated optimization model achieves three-phase imbalance mitigation by leveraging existing resources without the need for additional control equipment, thereby enhancing power quality in the distribution network while ensuring economic efficiency of system operation. Full article

The high proportion of renewable energy connected to the grid has resulted in insufficient consumption capacity in distribution networks, while the construction of new-type power distribution systems has imposed higher reliability requirements. With its flexible power synchronization control capabilities, grid-forming energy storage systems possess the ability to both promote the consumption of distributed energy resources in new-type distribution networks and enhance their reliability. However, current control methods are still hindered by drawbacks such as high computational complexity and a singular optimization objective. To address this, this paper proposes an optimized strategy for unified active–reactive power coordinated control in high-proportion renewable energy distribution networks with the participation of multiple grid-forming energy storage systems. Firstly, to optimize the parameters of grid-forming energy storage systems more accurately, this paper employs an improved iterative self-organizing data analysis technique algorithm to generate typical scenarios consistent with the scheduling time scale. Quantile regression (QR) and Gaussian mixture model (GMM) clustering are utilized to generate typical scenarios for renewable energy output. Subsequently, considering operational constraints and equipment state constraints, a unified active–reactive power coordinated control model for the distribution network is established. Meanwhile, to ensure the optimality of the results, this paper adopts an improved northern goshawk optimization (NGO) algorithm to solve the model. Finally, the effectiveness and feasibility of the proposed method are validated and illustrated through an improved IEEE-33 bus test system tested on MATLAB 2024B. Through analysis, the proposed method can reduce the average voltage fluctuation by 6.72% and increase the renewable energy accommodation rate by up to 8.64%. Full article

Hybrid cascaded multi-terminal HVDC systems represent a significant advancement in HVDC transmission technology. A notable real-world implementation of this concept is the bipolar hybrid cascaded multi-terminal high voltage direct current (MTDC) project in China, which successfully transmits hydropower from Baihetan to Jiangsu. This system combines MMCs for system support with LCCs for high-power transmission, offering both flexibility and efficiency in long-distance power delivery. This research explores the characteristics of main DC fault types in such systems, classifying faults based on sections and modes while analyzing their unique outcomes depending on DC fault locations. By focusing on the DC-side terminal behavior of the MMCs and LCCs, the main response processes to asymmetrical DC faults are investigated in detail. This study offers a detailed analysis of asymmetrical DC faults in bipolar HVDC systems, proposing a new classification based on fault characteristics such as current, voltage, active power, and reactive power. A supporting theoretical analysis is also presented. It identifies specific control demands needed for effective fault mitigation. PSCAD/EMTDC simulation results demonstrate that DC faults with similar characteristics can be consistently grouped into distinct categories by this new classification method. Each category is further linked to specific control demands, providing a strong basis for developing advanced protection strategies and practical solutions that enhance the stability and reliability of hybrid cascaded HVDC systems. Full article

Climate change is transforming energy use at the residential level by increasing temperature fluctuations and sustaining extreme weather events. This study proposes a climate-reactive, multi-objective approach to integrate the demand response (DR) with distributed generation (DG) and power quality improvement under a multi-objective framework of an integrated climate-adaptive approach to residential energy management. A cognitive neural network combination model with bidirectional long short-term memory networks (bidirectional) and a self-attention mechanism was used to successfully predict temperature-sensitive loads. The hybrid deep learning solution, which applies convolutional and bidirectional long short-term memory (LSTM) networks with attention, predicted the temperature-dependent load profiles optimized with an enhanced modified grey wolf optimizer (MGWO). The results of the experimental studies indicated significant gains in performance: in energy expenditure, the studies reduced it by 32.7%; in peak demand, they were able to reduce it by 45.2%; and in self-generated renewable energy, the results were 28.9% higher. The solution reliability rate provided by the MGWO was 94.5%, and it converged more quickly, thus providing better diversity in the Pareto-optimal frontier than that of traditional metaheuristic algorithms. Sensitivity tests with climate conditions of +2 °C and +4 °C showed strategy changes as high as 18.3%, thus establishing the flexibility of the system. Empirical evidence indicates that the energy and peak demand are to be cut, renewable integration is enhanced, and performance is strong in fluctuating climate conditions, highlighting the adaptability of the system to future resilient smart homes. Full article