Search Results (153)

The current literature on electrical system maintenance highlights three technology domains—Building Information Modeling (BIM), Digital Twin (DT), and extended reality (XR)—that have independently demonstrated strong potential for improving lifecycle information management, predictive analytics, and operational support. However, their convergence remains largely underexplored, particularly in electrical system maintenance. This paper provides a structured review of BIM–DT–XR convergence in electrical system lifecycle management, examining their roles across lifecycle phases and their integration through literature synthesis and cross-domain implementation evidence. BIM is analyzed as a basis for modeling and integrating facility management with electrical asset lifecycles; DT as a framework for dynamic system representation and applications in electrical and power systems; and XR as a means of visualizing and interacting with BIM-DT environments. Cross-domain implementation evidence from an industrial electrical facility and a tertiary smart-building pilot shows that BIM–DT–XR integration is technically feasible at pilot scale. However, the analysis identifies five structural integration gaps: semantic misalignment between building-oriented IFC and grid-oriented CIM ontologies; fragmented standard adoption; inconsistent data governance and naming practices; validation approaches focused on syntactic rather than dynamic model fidelity; and the separation of XR visualization from predictive DT capabilities. The implementation evidence further indicates that real-world deployment remains constrained by data quality limitations, integration complexity, cost factors, and interoperability with legacy systems. The review concludes that, despite the maturity of individual technologies, their effective application depends on advances in semantic alignment, lifecycle data governance, validation of dynamic models, and scalable integration frameworks, enabling the transition toward integrated, interoperable, and lifecycle-aware infrastructures for electrical system maintenance. Full article

Vehicle-to-grid (V2G) technology has emerged as a key enabler for coupling large-scale electric vehicle (EV) deployment with the operation of smart energy systems. By allowing bidirectional power and information exchange between EVs and the grid, V2G transforms EVs from passive loads into distributed energy resources capable of supporting grid flexibility, reliability, and renewable energy integration. However, the practical realization of V2G remains challenged by technical complexity, system coordination, user participation, and regulatory constraints. This paper presents a comprehensive review of V2G integration from a system-level perspective. Rather than focusing solely on individual technologies, the review examines how V2G is embedded within smart energy systems, emphasizing the interactions among EVs, aggregators, grid operators, energy markets, and end users. Key enabling technologies, including bidirectional charging, aggregation mechanisms, communication frameworks, and data-driven control strategies, are discussed in relation to their system-level roles and limitations. The impacts of V2G on grid operation, energy management, and market participation are analyzed, with particular attention to reliability, battery lifetime, and user trust. Furthermore, this review identifies critical open issues that hinder large-scale deployment, spanning infrastructure readiness, standardization, economic incentives, and cybersecurity. Emerging application scenarios, such as building-integrated V2G, fleet-based services, and artificial intelligence (AI) supported coordination, are also discussed to illustrate potential evolution pathways. By synthesizing technological developments with system-level impacts and unresolved challenges, this paper aims to provide a structured reference for researchers, system planners, and policymakers seeking to advance the integration of V2G into future smart energy systems. Full article

Free-space multi-agent path planning remains challenging at large scales. Most existing methods either offer optimality guarantees but do not scale beyond a few dozen agents or rely on grid-world assumptions that do not generalize well to continuous space. In this paper, we propose a hybrid, rule-based planning framework that combines Priority Inheritance with Backtracking (PIBT) with a novel safety-aware path smoothing method. Our approach extends PiBT to eight-connected grids and selectively applies string-pulling-based smoothing while preserving collision safety through local interaction awareness and a fallback collision resolution step based on Safe Interval Path Planning (SIPP). This design allows us to reduce overall path lengths while maintaining real-time performance. We demonstrate that our method can scale to over 500 agents in large free-space environments, outperforming existing any-angle and optimal methods in terms of runtime, while producing near-optimal trajectories in sparse domains. Our results suggest this framework is a promising building block for scalable, real-time multi-agent navigation in robotics systems operating beyond grid constraints. Full article

Continuous representations in brain navigation system are manifested as spatially structured patterns of population activity, such as a single-peaked bump moving along a ring manifold in head-direction system and hexagonal lattice patterns underlying spatial representation in grid-cell systems. These phenomena are commonly modelled within the framework of continuous attractor networks (neural dynamical field), yet the mechanisms by which activation-function nonlinearities interact with connectivity structure to determine pattern selection and dynamics remain incompletely understood. This paper separately analyses the interactions between non-resonant and resonant modes using a multiscale unfolding approach. We show that, when the critical modes satisfy a resonance condition, the quadratic nonlinearity of the activation function induces a three-mode coupling that fundamentally alters the structure of the amplitude equations and becomes the dominant mechanism governing spatial pattern selection. Building on this analysis, we introduce a weak asymmetric component in the connectivity and analytically derive the resulting pattern drift velocity, which is subsequently confirmed by numerical simulations. Finally, we apply these dynamical mechanisms to input-driven scenarios, illustrating that similar dynamical mechanisms can account for activity-bump tracking in head-direction models and lattice translations in grid-cell models. Overall, this work provides an analytically tractable framework for studying pattern dynamics in neural field models relevant to spatial representations, and may inform biomimetic approaches to spatial representation and navigation. Full article

Understanding the intricate interrelationships among ecosystem services (ESs) is fundamental to advancing sustainable ecological management. This study focuses on the Taihu Basin and examines five representative ESs, including water yield (WY), carbon sequestration (CS), soil retention (SR), habitat quality (HQ), and crop production (CP), for the years 2000, 2010, and 2020. Spatial distribution characteristics and spatiotemporal dynamics were quantified through the combined application of the InVEST model, a food production model, and ArcGIS. Spearman correlation analysis and K-means clustering were then applied to characterize trade-offs and synergies among ESs and to delineate ecosystem service bundles at multiple spatial scales, including 1 km × 1 km grids, 10 km × 10 km grids, and the county level, while GeoDetector was used to identify the associated driving mechanisms. The results indicated that (1) between 2000 and 2020, the spatial distribution pattern of the ESs in the Taihu Basin underwent significant changes, with WY and SR increasing by 48.97% and 51.89%, respectively, while HQ, CS, and CP decreased by 17.2%, 15.5%, and 47.6%. (2) From an overall perspective of trade-offs and synergies, the interactions among ESs shifted from trade-offs (r < 0) to synergies (r > 0) as the scale increased. From the perspective of the spatial characteristics of trade-offs and synergies, the intensity of these interactions varied significantly with increasing scale, but the trend remained relatively stable. (3) The Taihu Basin can be categorized into six ES bundles (ESBs). ESB 1, ESB 3, ESB 4, and ESB 5 have relatively stable ES structures, whereas ESBs 2 and 6 display significant variations. (4) The primary factors influencing ESs vary significantly across different spatial scales, with land use/land cover (LULC) and the proportions of arable land, forestland, and buildings exhibiting strong explanatory power. This highlights the critical role of coupled natural and anthropogenic processes in shaping the spatial patterns of ESs. This study considers the spatiotemporal variation and scale dependence of ecosystem services, providing management recommendations tailored to different regions and spatial scales, and offering a scientific basis for regional ecological planning and watershed governance. Full article

Identifying real-world saturation points and grid-hosting capacity in mixed-use urban Renewable Energy Communities (RECs) requires dynamic spatial evaluation. To address this, this paper introduces a novel simulation framework that integrates GIS spatial analysis with an iterative heuristic selection algorithm. The proposed method evaluates the energetic interaction between a primary generation node and surrounding consumers, utilizing a dynamic function to calculate the collective Self-Consumption Rate (SCR). Applied to the Flaminio Stadium in Rome, the model incrementally aggregates users to determine the optimal cluster size for economic feasibility. The results demonstrate that the heuristic selection algorithm successfully refined the community from an initial pool of 854 buildings to an optimal cluster of 734. This targeted selection eliminated energy surplus and achieved a near-perfect collective SCR of 99.8%. Furthermore, by strategically reducing the required installed PV capacity by 52.6%, the initial capital investment dropped from € 89.9 million to € 42.6 million, significantly de-risking the project while maintaining a competitive payback period of approximately 13 years. Ultimately, this study presents a scalable spatial optimization tool that empowers decision makers to transform large-scale urban infrastructure into the energetic and economic engines of district wide decarbonization Full article

The growing need for sustainable and lightweight building materials has accelerated research on alternatives to conventional concretes, out of which Lightweight Expanded Clay Aggregate (LECA) concrete has emerged as a promising solution. However, the high porosity and nonlinear mechanical behavior of LECA concrete complicate the accurate prediction of compressive strength through conventional empirical models. The main focus of the paper is on identifying a comprehensive machine learning-based framework for modeling and predicting the 28-day compressive strength of LECA-based lightweight concrete. The dataset was created and preprocessed by using statistical normalization and correlation analysis. In this study, five supervised machine learning models—Multiple Linear Regression (MLR), Support Vector Regression (SVR), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost)—were developed and fine-tuned using a grid-search strategy combined with ten-fold cross-validation. The quality of the prediction made by each model was evaluated by means of standard performance indicators, such as the coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). After the evaluation, the models were subsequently compared and ranked according to the Gray Relational Analysis (GRA) method. The comparative assessment shows that CatBoost demonstrated the most reliable performance, achieving an R² of 0.907, RMSE of 3.41 MPa, MAE of 2.47 MPa, and MAPE of 10.05%, outperforming the remaining algorithms. To interpret the significance of features, SHAP (Shapley Additive exPlanations) analysis was applied, which identified water and LECA content as the dominant factors influencing compressive strength, followed by the cement and fine aggregate proportions. The findings reveal that the ensemble-based gradient boosting model is capable of capturing intricate nonlinear interactions, as observed in the heterogeneous matrix of LECA concrete. Full article

This study applies an integrated analytical framework combining GeoDetector and multiscale geographically weighted regression (MGWR) to examine how the spatial distribution of cultural heritage values in the Chungcheong region of South Korea (Chungcheongnam-do and Chungcheongbuk-do) relates to regional socio-spatial contexts. Using the Korea Heritage Service’s heritage basic survey data (coordinates, attributes, and value assessments), we aggregated heritage value scores to a 1 km grid and modeled six value dimensions—historical, artistic, academic, social, rarity, and conservation—as separate dependent variables. We then integrated socio-spatial indicators derived from statistical grid maps published by the National Geographic Information Institute (official land price, building density, green space, road accessibility, total population, working-age population share, and aging rate). GeoDetector was first used to identify key determinants and interaction effects by value dimension, and MGWR was then used to estimate local effect heterogeneity and variable-specific operating scales. Results show that heritage values are better explained by multi-factor configurations—urbanization, land value, green space, accessibility, and demographic structure—whose importance varies by value dimension, and that the same factor can exert different directions and strengths across local contexts. By linking “what matters” (key determinants) with “where and at what scale it matters” (local effects and bandwidths), this study provides quantitative evidence to support place-based conservation and utilization strategies. The proposed GeoDetector–MGWR framework is transferable to other regions where spatial heritage inventories and comparable socio-spatial indicators are available. Full article

Understanding how the built environment relates to urban ecological resilience is essential for resilience-oriented planning in high-density cities. Using Wuhan, China, as a case study, we constructed a 1 km grid-based Ecological Resilience Index (ERI) by integrating ecosystem resistance, adaptability, and recovery, and we confirmed significant spatial autocorrelation in ERI. We then applied a Bayesian-optimized XGBoost model (v2.0.3) with block-based spatial cross-validation to improve robustness under spatial dependence, and used SHAP to interpret nonlinear, threshold-like patterns and interactions among predictors. The results indicate that building coverage ratio (BCR), nighttime light intensity (NTL), elevation (ELE), mean building height (MBH), and precipitation (PRE) were the most influential predictors of ERI. SHAP main effects indicate clear non-monotonic and threshold-like response patterns across key predictors. SHAP interaction analysis further suggests that, under high BCR, the SHAP interaction term tends to be positive when MBH is below approximately 10 m, whereas the interaction between high NTL and low MBV is predominantly negative. This study provides fine-scale empirical evidence to inform the optimization of three-dimensional urban morphology to support urban ecological resilience. Full article

Against the backdrop of climate change and rapid urbanization, assessing urban flood resilience requires spatially continuous and interpretable approaches capable of capturing nonlinear interactions between natural and human systems. This study proposes a high-resolution framework for mapping urban flood resilience in the built-up areas of Chongqing, China, grounded in the geography–ecology–society–infrastructure systems (GESIS) concept. A Flood Resilience Index is constructed at a 50 m grid resolution using ten core indicators and objective weighting based on combined entropy and coefficient-of-variation methods. Three machine learning models—multilayer perceptron (MLP), random forest, and XGBoost—are then trained to reproduce the resilience surface by integrating these indicators with additional historical flood-exposure variables, with SHAP used for model interpretation. The MLP model achieves the best performance (R² ≈ 0.78) and generates spatially coherent resilience patterns. Impervious surface fraction and building density exert dominant negative effects, whereas elevation and ecological connectivity contribute positively. The results reveal pronounced nonlinear thresholds in key drivers, indicating that flood resilience cannot be inferred from monotonic factor effects alone. By combining objective weighting, explainable machine learning, and historical exposure information, this framework supports both accurate prediction and policy-relevant interpretation of urban flood resilience for sustainable urban planning in mountainous megacities. Full article

In recent years, the design of public spaces surrounding school buildings has gained growing attention in urban planning and child-friendly city agendas. This paper examines the role of tactical urbanism in creating more Liveable School Surroundings (LSS) and introduces the LSS framework as a new lens for interpreting school-adjacent spaces as threshold environments where safety, autonomy, sustainable mobility, social interaction, and play converge. Methodologically, it develops a 12-indicator evaluation grid structured around four dimensions and applies it to a systematic comparative analysis of 30 interventions implemented in Milano, Bologna, and Torino. The analysis provides new empirical evidence on the effectiveness of tactical urbanism in this domain. Findings show that tactical interventions can rapidly enhance perceived safety and social interaction, often outperforming permanent solutions in terms of spatial reconfiguration and activation, while revealing limitations in the domains of play, climatic comfort, and cycling integration. The comparative analysis also reveals the modest scale of Italian initiatives compared to international programs, underscoring the need for stronger governance and long-term planning tools. By positioning tactical urbanism as an experimental device and a strategic lever for school-centered public space regeneration, the study offers an original contribution to international debates on child-friendly planning and proximity-based urban policies. Full article

Agentic artificial intelligence (AI) is emerging as a paradigm for next-generation smart grids, enabling autonomous decision-making, adaptive coordination, and resilient control in complex cyber–physical environments. Unlike traditional AI models, which are typically static predictors or offline optimizers, agentic AI systems perceive grid states, reason about goals, plan multi-step actions, and interact with operators in real time. This review presents the latest advances in agentic AI for power systems, including architectures, multi-agent control strategies, reinforcement learning frameworks, digital twin optimization, and physics-based control approaches. The synthesis is based on new literature sources to provide an aggregate of techniques that fill the gap between theoretical development and practical implementation. The main application areas studied were voltage and frequency control, power quality improvement, fault detection and self-healing, coordination of distributed energy resources, electric vehicle aggregation, demand response, and grid restoration. We examine the most effective agentic AI techniques in each domain for achieving operational goals and enhancing system reliability. A systematic evaluation is proposed based on criteria such as stability, safety, interpretability, certification readiness, and interoperability for grid codes, as well as being ready to deploy in the field. This framework is designed to help researchers and practitioners evaluate agentic AI solutions holistically and identify areas in which more research and development are needed. The analysis identifies important opportunities, such as hierarchical architectures of autonomous control, constraint-aware learning paradigms, and explainable supervisory agents, as well as challenges such as developing methodologies for formal verification, the availability of benchmark data, robustness to uncertainty, and building human operator trust. This study aims to provide a common point of reference for scholars and grid operators alike, giving detailed information on design patterns, system architectures, and potential research directions for pursuing the implementation of agentic AI in modern power systems. Full article

In the context of large-scale renewable energy integration driven by China’s dual-carbon goals, and under distribution network scenarios with continuously increasing shares of wind and photovoltaic generation, this paper proposes a source–grid–load–storage coordinated planning method embedded with a multi-agent game mechanism. First, the interest transmission pathways among distributed generation operators (DGOs), distribution network operators (DNOs), energy storage operators (ESOs), and electricity users are mapped, based on which a profit model is established for each stakeholder. Building on this, a coordinated planning framework for active distribution networks (DN) is developed under the assumption of bounded rationality. Through an evolutionary-game process among DGOs, DNOs, and ESOs, and in combination with user-side demand response, the model jointly determines the optimal network reinforcement scheme as well as the optimal allocation of distributed generation (DG) and energy storage system (ESS) resources. Case studies are then conducted to verify the feasibility and effectiveness of the proposed method. The results demonstrate that the approach enables coordinated planning of DN, DG, and ESS, effectively guides users to participate in demand response, and improves both planning economy and renewable energy accommodation. Moreover, by explicitly capturing the trade-offs among multiple stakeholders through evolutionary-game interactions, the planning outcomes align better with real-world operational characteristics. Full article

About 16% of typhoons making landfall in China strike Hainan Island, where near-surface extreme winds in dense urban areas exhibit a strong spatiotemporal heterogeneity that is difficult to capture with current observations and mesoscale models. Focusing on Haikou during Super Typhoon Yagi (2024)—the strongest autumn typhoon to hit China since 1949—we developed a multiscale ERA5–WRF–PALM framework to conduct 30 m resolution large-eddy simulations. PALM results are in reasonable agreement with most of the five automatic weather stations, while performance is weaker at the most sheltered park site. Mean near-surface wind speeds increased by 20–50% relative to normal conditions, showing a coastal–urban gradient: maps of weighted cumulative exposure to strong winds (≥Beaufort force 8) show much longer and more intense events along open coasts than within built-up urban cores. Urban morphology exerted nonlinear effects: wind speeds followed a U-shaped relation with both the open-space ratio and mean building height, with suppression zones at ~0.5–0.7 openness and ~20–40 m height, while clusters of super-tall buildings induced Venturi-like acceleration of 2–3 m s⁻¹. Spatiotemporal analysis revealed banded swaths of high winds, with open areas and islands sustaining longer, broader extremes, and dense street grids experiencing shorter, localized events. Methodologically, this study provides a rare, systematically evaluated application of a multiscale ERA5–WRF–PALM framework to a real typhoon case at 30 m resolution in a tropical coastal city. These findings clarify typhoon–city interactions, quantify morphological regulation of extreme winds, and support risk assessment, urban planning, and wind-resilient design in coastal megacities. Full article

Buildings account for nearly 32% of global energy consumption and serve as key demand-side flexibility resources in power systems with high renewable penetration. However, their utilization is constrained by the lack of an integrated framework that can jointly quantify energy-adjustable margin (BAM) and response duration (RD) under realistic operational and thermal comfort constraints. This study presents a coupled data–physical simulation framework integrating a Particle Swarm Optimization–Long Short-Term Memory–Random Forest (PSO-LSTM-RF) hybrid load forecasting model with EnergyPlus(24.1.0)-based building simulation. The PSO-LSTM-RF model achieves high-accuracy short-term load prediction, with an average R² of 0.985 and mean absolute percentage errors of 1.92–5.75%. Predicted load profiles are mapped to physically consistent baseline and demand-response scenarios using a similar-day matching mechanism, enabling joint quantification of BAM and RD under explicit thermal comfort constraints. Case studies on offices, shopping malls, and hotels reveal significant heterogeneity: hotels exhibit the largest BAM (up to 579.27 kWh) and longest RD (up to 135 min), shopping malls maintain stable high flexibility, and offices show moderate BAM with minimal operational disruption. The framework establishes a closed-loop link between data-driven prediction and physics-based simulation, providing interpretable flexibility indicators to support demand-response planning, virtual power plant aggregation, and coordinated optimization of source–grid–load interactions. Full article