Search Results (622)

The penetration rate of variable energy sources in the current power grid is increasing, with the aim being to expand the use of these energy sources and to replace the traditional black start power supply. This study investigates the black start of a photovoltaic storage joint system based on the system’s short-circuit ratio threshold. Firstly, the principles and control modes of the photovoltaic (PV) system, energy storage system (ESS), and high-voltage direct current (DC) transmission system are studied separately to build an overall model; secondly, computational determinations of the short-circuit ratio under different scenarios are introduced to analyze the strength of the system, and the virtual inertia and virtual damping of the PV system are configured based on this; finally, the change trend of the storage system’s state of charge (SOC) is computed and observed, and the limits of what the system can support in each stage are determined. An electromagnetic transient simulation model of a black start system is constructed in PSCAD/EMTDC, and according to the proposed recovery strategy, the system frequency is maintained in the range of 49.4~50.6 Hz during the entire black start process; the fluctuation in maximum frequency after the recovery of the DC transmission system is no more than 0.1%; and the fluctuation in photovoltaic power at each stage is less than 3%. In addition, all the key indexes meet the requirements for black start technology, which verifies the validity of the strategy and provides theoretical support and a practical reference for the black start of a grid with variable energy sources. Full article

This paper presents a method for optimizing the inertia constants and damping coefficients of interconnected virtual synchronous generators (VSGs) using a genetic algorithm. The goal of optimization is to find a balance between minimizing the rate of change of frequency (RoCoF) and enhancing controllability. Five controllability-based metrics are tested: the minimum eigenvalue, the sum of the two smallest eigenvalues, the maximum eigenvalue, the trace, and the determinant of the controllability Gramian matrix. The approach includes the oscillatory modes’ damping ratio constraints to ensure the small-signal stability of the entire system. The results of optimization on the IEEE 9-bus system with three VSGs show that the proposed method improves controllability, reduces RoCoF, and maintains the desired oscillation damping. The proposed approach was tested through time-domain simulations. Full article

To meet the European Green Deal targets, the construction sector must improve building thermal performance via advanced insulation systems. Eco-friendly sandwich panels offer a promising solution. Therefore, this work aims to develop and validate a new eco-friendly composite sandwich panel (basalt fibres and recycled extruded polystyrene) with enhanced multifunctionality for lightweight and energy-efficient building façades. Two panels were produced via vacuum infusion—a reference panel and a multifunctional panel incorporating phase change materials (PCMs) and silica aerogels (AGs). Their performance was evaluated through lab-based thermal and acoustic tests, numerical simulations, and on-site monitoring in a living laboratory. The test results from all methods were consistent. The PCM-AG panel showed 16% lower periodic thermal transmittance (0.16 W/(m²K) vs. 0.19 W/(m²K)) and a 92% longer time shift (4.26 h vs. 2.22 h), indicating improved thermal inertia. It also achieved a single-number sound insulation rating of 38 dB. These findings confirm the panel’s potential to reduce operational energy demand and support long-term climate goals. Full article

The stochastic variance-reduced gradient (SVRG) theory is particularly well-suited for addressing gradient variance in deep neural network (DNN) training; however, its direct application to DNN training is hindered by adaptation challenges. To tackle this issue, the present paper proposes a series of strategies focused on adaptive alternating learning rates to effectively adapt SVRG for DNN training. Firstly, within the outer loop of SVRG, both the full gradient and the learning rate specific to DNN training are computed. For two distinct formulas used for calculating the learning rate, an alternating strategy is introduced that employs them alternately across iterations. This approach allows for simultaneous provision of diverse guidance information regarding parameter change rates and gradient change rates during DNN weight updates. Additionally, a threshold method is utilized to correct the learning rate into an appropriate range, thereby accelerating convergence. Secondly, in the inner loop of SVRG, DNN weights are updated using mini-batch average gradient along with the proposed learning rate. Concurrently, mini-batch average gradients from each iteration within the inner loop are refined and aggregated into a single gradient exhibiting reduced variance through an inertia strategy. This refined gradient is then relayed back to the outer loop to recalculate the new learning rate. The efficacy of the proposed algorithm has been validated on models including LeNet, VGG11, ResNet34, and DenseNet121 while being compared against several classic and advanced optimizers. Experimental results demonstrate that the proposed algorithm exhibits remarkable training robustness across DNN models with diverse characteristics. In terms of training convergence, the proposed algorithm demonstrates competitiveness with state-of-the-art algorithms, such as Lion, developed by the Google Brain team. Full article

The integration of renewable energy sources (RESs) into power systems, particularly in microgrids, is becoming a prominent trend aimed at reducing dependence on traditional energy sources. Replacing conventional synchronous generators with grid-connected RESs through power electronic converters has significantly reduced the inertia of microgrids. This reduction negatively impacts the dynamics and operational performance of microgrids when confronted with uncertainties, posing challenges to frequency and voltage stability, especially in a standalone operating mode. To address this issue, this research proposes enhancing microgrid stability through frequency control based on virtual inertia (VI). Additionally, the Iterative Learning Control (ILC) method is employed, leveraging iterative learning strategies to improve the quality of output response control. Accordingly, the ILC-VI control method is introduced, integrating the iterative learning mechanism into the virtual inertia controller to simultaneously enhance the system’s inertia and damping coefficient, thereby improving frequency stability under varying operating conditions. The effectiveness of the ILC-VI method is evaluated in comparison with the conventional VI (C-VI) control method through simulations conducted on the MATLAB/Simulink platform. Simulation results demonstrate that the ILC-VI method significantly reduces the frequency nadir, the rate of change of frequency (RoCoF), and steady-state error across iterations, while also enhancing the system’s robustness against substantial variations from renewable energy sources. Furthermore, this study analyzes the effects of varying virtual inertia values, shedding light on their role in influencing response quality and convergence speed. This research underscores the potential of the ILC-VI control method in providing effective support for low-inertia microgrids. Full article

Governance challenges, success factors, and stakeholder dynamics are central to the implementation of Positive Energy District (PED) Labs, which aim to develop energy-positive and sustainable urban areas. In this paper, a qualitative analysis combining expert surveys, participatory workshops with practitioners from the COST Action PED-EU-NET network, and comparative case studies across Europe identifies key barriers, drivers, and stakeholder roles throughout the implementation process. Findings reveal that fragmented regulations, social inertia, and limited financial mechanisms are the main barriers to PED Lab development, while climate change mitigation goals, strong local networks, and supportive policy frameworks are critical drivers. The analysis maps stakeholder engagement across six development phases, showing how leadership shifts between governments, industry, planners, and local communities. PED Labs require intangible assets such as inclusive governance frameworks, education, and trust-building in the early phases, while tangible infrastructures become more relevant in later stages. The conclusions emphasize that robust, inclusive governance is not merely supportive but a key driver of PED Lab success. Adaptive planning, participatory decision-making, and digital coordination tools are essential for overcoming systemic barriers. Scaling PED Labs effectively requires regulatory harmonization and the integration of social and technological innovation to accelerate the transition toward energy-positive, climate-resilient cities. Full article

Grid-forming (GFM) converters have been critical in DER-dominant power systems, ensuring stability, but their performance is highly sensitive to grid conditions such as system strength. Testing GFM converters under a wide range of grid strengths (from strong high-inertia systems to very weak grids) and fault scenarios is challenging, as traditional test facilities and static grid simulators have limitations. To address this problem, this paper proposes a converter-based power line emulator that provides a flexible, programmable grid environment for GFM converter testing. The emulator uses power electronic converters to mimic transmission line characteristics, allowing for the adjustment of effective grid strength (e.g., short-circuit ratio changes). The proposed approach is validated through detailed PSCAD simulations, demonstrating its ability to provide scalable weak-grid emulation and comprehensive validation of GFM converter control strategies and stability under various grid conditions. This research highlights that the converter-based emulator offers enhanced flexibility and cost-effectiveness over traditional testing setups, making it an effective tool for GFM converter performance test. Full article

We examined the associations between individual differences in intensive longitudinal data-derived affective dynamics (i.e., positive and negative affect variability and inertia and positive affect–negative affect bipolarity) and concurrent stress, drinking levels, and affect-regulation drinking motives across three time points spanning early adulthood. This allowed us to evaluate the stability of the affective dynamics and whether their associations with alcohol outcomes varied across this critical developmental period. Moderate-to-heavy college drinkers (N = 1139, 51% women) reported on their affective states, stress, drinking levels, and drinking motives daily for 30 days using a web-based daily diary in three assessment waves: during college and at two post-college waves, approximately 5 and 10 years after the initial assessment. Findings indicated moderate stability of the affect dynamic indicators, except for inertia. Negative affect variability showed the strongest positive association with mean daily stress. Individuals who demonstrated stronger affect bipolarity had lower drinking levels and higher enhancement motivation. None of the other dynamic indicators were consistently related to the drinking outcomes in the predicted direction after controlling for mean affect levels, and we found little evidence for changes in these effects across time. Our results add to the inconsistent literature regarding the associations between affective dynamics and alcohol-related outcomes. Full article

Looking toward the future, governments around the world have started to change their energy mix due to climate change. The new energy mix will consist mainly of Inverter-Based Resources (IBRs), such as wind and solar power. This transition from a synchronous to a non-synchronous grid introduces new challenges in stability, resilience, and synchronization, necessitating advanced control strategies. Among these, Grid-Forming (GFM) control techniques have emerged as an effective solution for ensuring stable operations in microgrids and large-scale power systems with high IBRs integration. This paper presents a systematic review of GFM control techniques, focusing on their principles and applications. Using the PRISMA 2020 methodology, 75 studies published between 2015 and 2025 were synthesized to evaluate the characteristics of GFM control strategies. The review organizes GFM strategies, evaluates their performance under varying operational scenarios, and emphasizes persistent challenges like grid stability, inertia emulation, and fault ride-through capabilities. Furthermore, this study examines real-world implementations of GFM technology in modern power grids. Notable projects include the UK’s National Grid Pathfinder Program, which integrates GFM inverters to enhance stability, and Australia’s Hornsdale Power Reserve, where battery energy storage with GFM capabilities supports grid frequency regulation. Full article

Solar energy is one of the most promising solutions for improving building energy efficiency. Among passive heating systems, the combination of a Trombe wall, phase change materials (PCM), and multi-alveolar structures (MAS) stands out. This configuration enhances the wall’s ability to absorb solar heat and distribute it evenly throughout the interior. This study evaluated thermal comfort by examining the effects of phase change materials and multi-alveolar structures combined with a Trombe wall on the thermal behavior of a building and improving the thermal inertia of brick walls. Numerical simulations using Visual FORTRAN were conducted to evaluate the thermal properties of different configurations under the climatic conditions recorded in Hammam Sousse, Tunisia. The results show that the integration of the Trombe wall and PCM has a significant impact on interior temperature stability, energy consumption, and overall thermal comfort. The combined effect of the MAS and PCM with the Trombe wall improved heat gain in winter and spring, reaching a low thermal damping factor of 40% in March, reducing heating power, and optimizing thermal comfort for occupants. Full article

The increasing incorporation of renewable energy into power grids has significantly reduced system inertia and damping, posing challenges to frequency stability and power quality. To address this issue, an adaptive virtual synchronous generator (VSG) control strategy is proposed, which dynamically adjusts virtual inertia and damping in response to real-time frequency variations. Virtual inertia is modulated by an exponential function according to the frequency variation rate, while damping is regulated via a hyperbolic tangent function, enabling minor support during small disturbances and robust compensation during severe events. Control parameters are optimized using an enhanced particle swarm optimization (PSO) algorithm based on a composite performance index that accounts for frequency deviation, overshoot, settling time, and power tracking error. Simulation results in MATLAB/Simulink under step changes, load fluctuations, and single-phase faults demonstrate that the proposed method reduces the frequency deviation by over 26.15% compared to fixed-parameter and threshold-based adaptive VSG methods, effectively suppresses power overshoot, and eliminates secondary oscillations. The proposed approach significantly enhances grid transient stability and demonstrates strong potential for application in power systems with high levels of renewable energy integration. Full article

This research presents a multi-scale framework designed for assessing the energy performance and climate vulnerability of three existing residential buildings in a small Central Italian municipality. By integrating dynamic energy simulations with high-resolution climate projections, the study investigated how the selected building typologies responded to changing environmental conditions. Validation against Energy Performance Certificates (EPCs) confirmed the framework’s robustness in accurately capturing energy consumption patterns and assessing retrofit potential. The results revealed a general reduction in heating demand accompanied by an increase in cooling requirements under future climate scenarios, with notable differences across building types. The reinforced concrete building showed greater sensitivity to rising temperatures, particularly in cooling demand, likely due to its lower thermal inertia. In contrast, masonry buildings achieved more substantial energy savings following retrofit interventions, reflecting their initially poorer thermal performance and outdated systems. Retrofit measures yielded significant energy reductions, especially in older masonry structures, with savings reaching up to 44%, underscoring the necessity of customised retrofit strategies. The validated methodology supports future wider applicability in regional energy planning and aligns with integrated initiatives aimed at balancing climate adaptation and cultural heritage preservation. Full article

One of the most challenging problems in data analysis is visualizing patterns and extracting insights from multi-dimensional datasets that vary over time. The complexity of data and variations in the correlations between different features adds further difficulty to the analysis. In this paper, we provide a framework to analyze the temporal dynamics of such datasets. We use machine learning clustering techniques and examine the time evolution of data patterns by constructing the corresponding cluster trajectories. These trajectories allow us to visualize the patterns and the changing nature of correlations over time. The similarity and correlations of features are reflected in common cluster membership, whereas the historical dynamics are described by a trajectory in the corresponding (cluster, time) space. This allows an effective visualization of multi-dimensional data over time. We introduce several statistical metrics to measure duration, volatility, and inertia of changes in patterns. Using the Hamming distance of trajectories over multiple time periods, we propose a novel metric, the Hamming diversification index, to measure the spread between trajectories. The novel metric is easy to compute, has a simple machine learning implementation, and provides additional insights into the temporal dynamics of data. This parsimonious diversification index can be used to examine changes in pattern similarities over aggregated time periods. We demonstrate the efficacy of our approach by analyzing a complex multi-year dataset of multiple worldwide economic indicators. Full article

In the United States, the Clozapine Risk Evaluation and Mitigation Strategy (REMS) program was implemented to ensure safe prescription and monitoring; however, its administrative complexity has often resulted in unintended barriers to access. Clozapine remains the most effective antipsychotic for treatment-resistant schizophrenia (TRS), yet its use continues to be constrained by outdated regulatory frameworks, cultural inertia, and clinical hesitancy. This perspective article revisits the pharmacokinetic foundations of clozapine, re-examines its association with fatal outcomes, and critiques the persistence of obsolete monitoring systems such as the U.S. REMS program. Drawing on recent consensus publications endorsed by over 120 international clozapine experts, this article outlines the proposed changes to the U.S. prescription information and contextualizes them within broader global practices. This article argues that many barriers to clozapine use stem not from evidence, but from regulatory conservatism and the perpetuation of clinical myths. The dismantling of the REMS program in early 2025 represents a pivotal moment, yet further reforms are urgently needed to align regulatory guidance with contemporary science. Ultimately, this article is a call to rediscover the clinical value of clozapine and to translate decades of knowledge into regulatory and clinical action. Full article

Smart governance—the integration of digital technologies into urban governance—is increasingly recognized as a transformative approach to addressing complex urban challenges such as rapid urbanization, climate change, social inequality, and resource constraints. As a foundational pillar of the smart city paradigm, it enhances decision-making, service delivery, transparency, and civic participation through data-driven tools, digital platforms, and emerging technologies such as AI, IoT, and blockchain. While often positioned as a pathway toward sustainability and inclusivity, existing research on smart governance remains fragmented, particularly regarding its relationship to urban sustainability. This study addresses that gap through a systematic literature review using the PRISMA methodology, synthesizing theoretical models, empirical findings, and diverse case studies. It identifies key enablers—such as digital infrastructure, data governance, citizen engagement, and institutional capacity—and highlights enduring challenges including digital inequity, data security concerns, and institutional inertia. In response to this, the study proposes a multidimensional framework that integrates governance, technology, and sustainability, offering a holistic lens through which to understand and guide urban transformation. This framework underscores the importance of balancing technological innovation with equity, resilience, and inclusivity, providing actionable insights for policymakers and planners navigating the complexities of smart cities and urban development. By aligning smart governance practices with the United Nations’ sustainable development goals (SDG)—particularly SDG 11 on sustainable cities and communities—the study offers a strategic roadmap for fostering resilient, equitable, and digitally empowered urban futures. Full article