Search Results (2,672)

Urban flexibility research is expanding across buildings, electric vehicles (EVs), distributed energy resources (DERs), storage, positive energy districts (PEDs), digital twins, and interoperability platforms. These strands are often reviewed separately, although urban distribution operators must manage their combined impacts on the same feeders. This paper presents a PRISMA 2020-aligned systematic review with evidence mapping and narrative synthesis of feeder-aware coordination in smart-city electricity systems. Searches of Scopus, Web of Science, IEEE Xplore, ScienceDirect, and citation chasing identified 312 records; 127 studies were included after screening and eligibility assessment, 101 entered the quantitative mapping sample, and 31 formed the deep-synthesis anchor core. Sparse contingency tables were analyzed with Monte-Carlo permutation chi-square tests and bootstrap confidence intervals for Cramér’s V, while ordinal variables were summarized with medians and interquartile ranges. Explicit feeder grounding was concentrated in grid-oriented and EV-oriented studies, whereas many AI/digital-twin and interoperability studies were less often validated against distribution-network operation. Economic and peak-flexibility indicators were reported far more often than interoperability, cybersecurity, or validation-maturity indicators in the anchor core. The synthesis also showed that deployment-oriented work depends on clearer treatment of standards, co-simulation workflows, regulatory instruments, and stakeholder roles. The evidence base is heterogeneous, English-only, and single-coded, so the quantitative results are descriptive rather than population-level. The review contributes a transparent three-layer corpus design (127 included/101 mapped/31 anchor), a domain-specific specialization of SGAM/IEEE 2030 for urban feeder orchestration, an operational digital-twin definition and validation ladder, a retrofittable benchmarking framework, and a practical roadmap for DSOs, municipalities, aggregators, EV operators, building managers, and ICT providers. Full article

Decarbonizing the building sector requires integrating on-site renewable generation with systematic energy management. Among the most widely adopted alternatives are photovoltaic (PV) systems in buildings; however, they are often implemented as a standalone technological intervention (size–install–estimate savings), without being formally incorporated into an Energy Management System (EnMS) aimed at continuous improvement. In this context, this research addresses this gap through an integrated methodological framework aligned with ISO 50001, in which PV is explicitly included in energy performance management through energy review, the definition of an Energy Baseline (EnB), and the monitoring of Energy Performance Indicators (EnPIs) within the PDCA cycle. The approach articulates the analytical sizing of the PV system based on electricity demand and solar resources; its validation through simulation to ensure operational consistency and a technical, economic, and environmental assessment that translates PV generation into a verifiable reduction in energy imported from the grid and, consequently, into traceable improvements in EnPI under an audit-compatible scheme. The methodology is demonstrated in a multi-family building in Chorrillos, Lima (Peru), where a 14.5 kWp rooftop PV system (25 modules of 580 Wp) is designed to maximize self-consumption during daylight hours. The results show technical performance consistent with the demand profile, economic viability under the conditions of the case, and environmental benefits from replacing grid electricity, along with offsets associated mainly with the manufacture of PV components. The residual gap between the Post-PV EnPIs and the ISO 50001 target confirms that PV integration is a necessary but not sufficient first-cycle action within a comprehensive building decarbonization strategy, with demand-side management and envelope improvements identified as subsequent PDCA cycle priorities. In summary, the central contribution is not the PV sizing itself, but its operational and traceable integration within ISO 50001, making PV a quantifiable, verifiable, and scalable energy improvement action for residential buildings in emerging economies. Full article

Smart building services are increasingly realised through combinations of sensors, actuators, communication infrastructures, software platforms, analytics, and artificial intelligence-based functions. These configurations enable adaptive control, real-time monitoring, contextual automation, predictive support, user interaction, and cross-domain coordination across heating, ventilation, air conditioning, lighting, energy management, security and access control, water management, and user-centric comfort services. At the same time, the European Union Smart Readiness Indicator provides a formal basis for assessing building smartness through technical domains, service functionalities, and multidimensional impact criteria. A systematic basis for translating real-world descriptions of smart building services and their enabling technology stacks into Smart Readiness Indicator-aligned assessment inputs remains underdeveloped. A PRISMA ScR informed review was conducted to identify principal smart building service domains, synthesise their core functionalities, and reconstruct the digital technologies through which these functionalities are realised. The synthesis shows that heating, ventilation, and air conditioning and lighting provide comparatively direct translation pathways to formal Smart Readiness Indicator domains, while energy management operates mainly as a supervisory and cross-domain layer. Security and access control, water management, and several user-centric services contribute meaningfully to building smartness but often show partial or extended formal correspondence. Monitoring and control emerge as a central cross-cutting layer because many higher-order smart building capabilities are expressed through visibility, supervision, orchestration, and digital representation. Building on this review, a methodological framework is established for translating smart building services into Smart Readiness Indicator-aligned assessments. The procedure uses the smart building service instance as the unit of analysis and links service identification, functionality formulation, technology stack reconstruction, formal domain correspondence, impact profiling, maturity classification, and building-level aggregation. This enables heterogeneous service descriptions to be converted into structured readiness profiles while preserving the distinction between operational functionality, enabling technology, formal assessment correspondence, and multidimensional impact contribution. Application of the framework to the IoT Building Cloud platform shows that a substantial share of smart building capability may derive from supervisory digital infrastructure rather than from isolated end-use control alone. The resulting readiness profile is characterised by strong representation in monitoring and control, information to occupants and operators, and maintenance awareness, together with more selective contributions to indoor environmental control and limited flexibility-related capability. The proposed framework supports Smart Readiness Indicator-aligned pre-assessment, comparative analysis, design stage reasoning, and digital tool development by providing a transparent bridge between smart building service descriptions and formal assessment-oriented interpretation. Full article

Global urbanization is shifting from incremental expansion to stock optimization, and old residential communities have become important spatial units for low-carbon transition. However, in existing built environments, traditional process-based inventory methods face practical constraints, including missing original drawings, complex site conditions, and severe vegetation obstruction. As a result, systematic accounting of buildings, landscapes, and natural carbon sinks remains difficult. This study integrates life cycle assessment (LCA), BIM reverse modeling, 3D point clouds, DesignBuilder simulation, inventory-based accounting, and i-Tree Eco to construct a life cycle carbon emission accounting framework for old residential communities. The framework links current-condition data reconstruction, quantity take-off, operational energy simulation, landscape inventory accounting, and vegetation carbon sequestration assessment. It is applied to Nanyuan Xincun in Hefei to quantify the community-scale carbon source–sink structure. The results show that Nanyuan Xincun presents a clear operation-led emission pattern, with the operation and maintenance phase accounting for 82.52% of total positive emissions. Within architectural engineering, operation and maintenance accounts for 82.91%, while material production accounts for 13.28%. Landscape engineering shows a more mixed structure, with operation and maintenance accounting for 52.95% and material production accounting for 36.49%. Vegetation carbon sequestration analysis shows that mature trees and shrubs are the main ecological carbon assets. Annual sequestration reaches 16.95 t-CO₂e/a, and trees and shrubs contribute 92.85% of total vegetation carbon storage. Under current vegetation conditions, annual sequestration is equivalent to 32.99% of annual landscape operation emissions, indicating considerable ecological compensation potential. Based on these findings, this study proposes four optimization pathways: operational energy reduction, low-carbon material substitution, construction and demolition waste recycling, and mature tree protection. These pathways provide data support for refined carbon management and low-carbon renewal in existing communities. Full article

Containerized mobile power supplies (CMPS), a critical energy replenishment carrier for all-electric ships, have caused severe economic losses via frequent fire and explosion accidents during temporary port storage in recent years. Existing literature focuses on battery thermal runaway under laboratory conditions and maritime transport risk analysis, but its conclusions are not directly applicable to port temporary storage. Port storage, featuring full-charge quiescent placement and high turnover, differs significantly from maritime transport, while its high-temperature and humid environment is distinct from laboratory settings. Furthermore, no system safety-based risk assessment framework exists, failing to deliver targeted mitigation strategies for practical operations. To address these issues, fault tree analysis (FTA), Bayesian network (BN), and attack–defense game theory were combined to build a systematic safety risk assessment framework. FTA clarified the hazard factors’ correlation mechanism; based on FTA, BN conducted a quantitative evaluation. Extended from BN results, attack–defense game theory identified key risk evolution paths and formulated targeted prevention and control measures. The main conclusions are as follows: Combined with similar accident features and port storage scenario attributes, internal correlations between hazard-inducing factors were clarified via FTA. Based on expert evaluations and BN calculation, the target port’s fire accident occurrence probability was determined as 2.41%, with two core root nodes identified via sensitivity analysis. Two critical risk evolution paths corresponding to IE1 (thermal runaway initiation) and IE2 (failure of protection and emergency response systems) were identified via game theory and traversal method, with occurrence probabilities of 1.50% and 1.77%, respectively. Targeted prevention and control measures adapted to the port storage scenario were proposed based on path triggering mechanisms. These findings provide theoretical support for port enterprises to improve CMPS fire prevention and emergency response capabilities, elevate port safety management levels, and promote the safe development of the all-electric vessel shipping industry. Full article

Food markets represent a public good essential for urban supply and as intergenerational spaces supporting the small-scale economy, yet they face growing challenges in adapting to sustainability regulations and circular economy requirements. This study examines the current state of sustainability in Madrid’s municipal markets through interviews and questionnaires administered to market managers, analyzing building characteristics, renewable energy systems, passive savings strategies, and energy costs across different market typologies. Results reveal that in December 2025, only 9% of markets had solar thermal installations, while merely 11% were planning photovoltaic solar panel projects—figures insufficient to meet current EU energy efficiency mandates. The findings demonstrate a significant gap between existing infrastructure and the requirements of the Directive (EU) 2023/1791, which supersedes previous directives. These results indicate an urgent need for accelerated implementation of renewable energy systems in market buildings to achieve sustainability targets. The study contributes baseline data for developing intervention strategies that can reduce energy consumption and align Madrid’s market network with European decarbonization goals for 2030 and 2050. Full article

Reducing carbon emissions during construction is essential for meeting dual carbon targets. Current green scheduling methods assume fixed emission factors, overlooking time-dependent variations driven by grid peak-valley patterns. Under interval duration uncertainty coupled with tight dynamic carbon budgets, conventional algorithms struggle with sparse feasible solutions and slow Pareto front convergence. We formulate a bi-objective interval RCPSP model with time-varying carbon emission factors that minimizes both interval makespan and total carbon emissions. A possibility degree measure converts scalar carbon budgets into linearized hard constraints. To solve this NP-hard problem, we propose the Knowledge-Driven Interval Multi-Objective Evolutionary Algorithm (KD-IMOEA), which integrates four components: Knowledge-Driven Initialization (KDI), Adaptive Time-window Carbon-aware Decoding (TCD), Carbon Budget-aware Repair Mutation (CBM), and Interval Pareto Elite Archive (IPA), forming an end-to-end carbon-aware optimization pipeline. We validate KD-IMOEA on J30 through J120 benchmark instances; results show it outperforms four established algorithms, including NSGA-II, in both convergence and distribution, with hypervolume (HV) gains up to 6.3%. A green building case study confirms that KD-IMOEA exploits spatiotemporal decoupling to identify float time and assign energy-intensive machinery to lower-carbon operating profiles. At the optimal compromise makespan of 169.5 days, this strategy cuts carbon emissions by 3.07% over traditional baselines, enabling management-driven emission savings without extending project duration. Full article

This study explores the potential of deliberative and polycentric governance models to address the complex challenges of the energy transition trilemma, balancing energy security, environmental sustainability, and energy equity. The article aims to develop an integrated deliberative–polycentric framework for managing the energy transition trilemma and to illustrate its implementation relevance through an applied example of heat pump deployment. The analysis primarily draws on evidence and examples from Europe and the United States, reflecting the regions most frequently discussed in the reviewed literature and policy materials. Drawing on an extensive literature review and desk-based analysis, the research adopts a non-empirical, theory-building approach grounded in interpretive policy analysis. The study synthesizes insights from scholarly works and policy documents to construct an integrated analytical framework. It argues that hybrid governance, merging the inclusivity and transparency of deliberative democracy with the flexibility and redundancy of polycentric systems—can enhance legitimacy, adaptability, and effectiveness in energy policymaking. Through thematic synthesis, key governance principles are identified, including multilevel coordination, stakeholder participation, transparency, and justice. The findings highlight that the synergy between deliberation and polycentricity offers a promising path toward more resilient, participatory, and just energy systems, while acknowledging the implementation challenges of such models. Full article

Artificial Intelligence (AI) and Digital Twins (DTs) can support the digital and energy transition in the construction sector; however, their application to the built environment presents both opportunities and limitations. This study aims to give a critical perspective on the topic analyzing the related key challenges, including error assessment, model interpretability, data availability, cybersecurity risks, organizational constraints, and lifecycle costs. Where AI is nowadays developed as a context-dependent tool set, it is most effective when embedded within integrated socio-technical systems rather than adopted as a universal solution. Instead, DTs can be intended as an enabling framework, integrating AI, Internet of Things (IoT), Big Data, and Building Management Systems (BMS) to enhance energy performance, indoor environmental quality, safety, maintenance, and decision-making at both building and urban scales. The direction international research on these topics is facing is clear as evidenced by the wide number of research papers published. The future of these technologies moves towards a simulative approach oriented towards the sustainable and fair development goals and will bring a broad transformation of the building environment where they are ever more integrated into each social and technical aspect. The work is supported by a case study developed at Sapienza University of Rome founded by the Italian National Recovery and Resilience Plan within Flagship Project 2 (FP2), “Energy Transition and Digital Transition in Urban Regeneration and Construction,” of the Rome Technopole ecosystem. Full article

A framework was developed in this study for setting and adjusting energy-saving targets for existing public-sector office buildings. Using self-reported energy data, we removed outliers and grouped buildings by average daily operating hours. We analyzed electricity use intensity distributions and assigned reduction rates based on each building’s percentile within its group, allowing for larger improvements from high-consumption buildings while limiting pressure on already efficient ones. The framework achieved an average annual energy-saving effect of about 1% and can inform future revisions of energy management policies and target values for public office buildings. Full article

This paper proposes a time-based no-cost category of energy conservation measures (ECMs) enabled by audit-driven building energy modeling. The study presents an audit-to-simulation framework applied to an academic building (Electrical Engineering Department, Assiut University, Egypt) following the audit levels and requirements of ASHRAE Standard 100-2024. The building operation is characterized via audit findings, high-resolution electrical monitoring, and occupancy profiling, then translated into a calibrated building energy model (BEM) developed using SketchUp, OpenStudio, and EnergyPlus. The validated BEM serves as a decision-support testbed to evaluate the proposed ECMs prior to implementation, enabling quantification of their impacts on annual and daily energy use, peak reduction, and load-profile shape. The proposed ECMs are classified into two subcategories: working-day ECMs and time-slot-modification ECMs. The first category involves adjusting the number of working days per week. The second category includes several scheduling-based strategies, namely seasonal time shifts, modification of lecture and tutorial session durations, rearrangement of lectures and tutorial sessions, and shifting peak-demand time slots. The simulation results show that modifying lecture and tutorial durations (ECM3) is the most effective measure, achieving 6.2% annual energy savings, followed by seasonal time shifts (ECM2) with 5.8%. For peak demand, reducing operation during peak periods (ECM5) lowers the daily peak load by 25.9%. The combined implementation of the proposed ECMs reduces annual energy consumption by up to 16% and daily peak demand by 29.4%. The findings highlight the substantial potential of structured audit-informed operational strategies in university buildings, emphasizing their role as low-risk, high-impact interventions for peak management and energy performance enhancement. Full article

The dual-inverter open-winding permanent magnet synchronous motor (OW-PMSM) drive system exhibits significant advantages for electric vehicles with dual energy sources, particularly in achieving coordinated energy management and efficient power allocation between the sources. Based on the dual-inverter OW-PMSM drive configuration, this paper proposes two stator current planning algorithms: one aims to minimize the electrical losses during motor operation and the other aims to maximize the power allocation range of the dual inverters, respectively. Building upon this, a geometric algorithm for stator voltage vector allocation is proposed to achieve smooth switching of the motor between the two algorithms. This enhances the tracking performance of the electromagnetic torque and d-axis current during motor operation, while ensuring that the motor operates within its steady-state range, thereby improving system stability. Finally, simulations and experiments are conducted on the proposed algorithm to verify its feasibility and advantages. Full article

Accurate photovoltaic cell temperature estimation is critical for maximizing energy management and improving digital twin fidelity in building-integrated solar systems. Classical models, NOCT (Nominal Operating Cell Temperature), King, Skoplaki, and PVsyst/Faiman, provide a practical baseline but exhibit significant limitations when applied to complex, real-world scenarios. These static and linear approaches fail to capture dynamic thermal phenomena such as thermal inertia, nonlinear irradiance effects, and wind-temperature interactions. This paper presents an advanced physical model that incorporates thermal memory effects, sophisticated wind modeling, transient cloud-response mechanisms, and non-linear thermal dependencies. Parameter calibration was performed using a differential evolution algorithm, automatically optimizing the model fit to one year of experimental data from a 2.79 kW pilot installation at the University of Extremadura. The validation results demonstrate consistent improvements across all seasons: RMSE reductions of up to 4.9% and MAE reductions of up to 14.4% compared to classical approaches, with particularly pronounced gains during the summer and autumn. The methodology is readily transferable to diverse installations and climatic contexts, providing a robust framework for developing high-accuracy PV digital twins and enabling early fault detection and operational optimization. Full article

Energy efficiency retrofits are widely promoted for public buildings, yet evidence from large-scale real-world projects remains limited compared with simulation-based assessments. This study leverages measured pre- and post-retrofit operational data from 530 public building retrofit projects across 11 provinces/municipalities in China to quantify realized energy-saving performance and screening-level cost-effectiveness across building types and climate zones. Wilcoxon and Kruskal–Wallis tests were employed to ensure statistical rigor. Retrofit measures were grouped into seven categories (e.g., HVAC, lighting, envelope, monitoring/management), and a median-based four-quadrant framework was employed to characterize investment–savings profiles by climate zone and building function. Across the full sample, mean energy use intensity decreased by 19.1%, with 99.2% of projects achieving positive savings. Savings varied markedly by building type: commercial and hotels achieved the highest savings intensities (26.5–28.0 kWh/(m²·a)), while education and cultural buildings generally showed lower gains, with some projects having < 10 kWh/(m²·a). Technology performance exhibited distinct climate and building suitability. Envelope retrofits were most effective in the Cold and Hot Summer–Cold Winter zones (13.30–22.06 kWh/(m²·a)) but yielded limited benefits in the Hot Summer–Warm Winter zone (~1.73 kWh/(m²·a)). HVAC and lighting upgrades delivered comparatively stable savings across climates and building types and dominated retrofit portfolios. Based on these findings, we propose a tiered strategy: prioritizing HVAC and envelope upgrades for high-load sectors while focusing on low-cost optimizations for educational facilities to mitigate investment risks. The findings provide large-scale empirical evidence to support climate- and building-specific retrofit prioritization and investment decision-making under real-world operating conditions. Full article

Home energy management systems (HEMS) aim to provide intelligent control of the thermal comfort inside smart buildings with the minimum energy cost, while satisfying the energy consumption requests and increasing the use of energy from renewable sources. The capabilities of these intelligent HEMS agents are restricted due to the personalized observability of the environment, resulting in limited knowledge gathering and potentially sub-optimal decisions. Furthermore, several buildings have recently been organized into small energy communities, with the ultimate goal of sharing intelligence between agents in federated learning schemes.In this context, we propose a personalized federated deep reinforcement learning method using Moreau envelopes (pFedMe) for joint energy cost and household comfort optimization in energy communities that consist of multiple smart homes. Specifically, a Twin-Delayed Deep Deterministic Policy Gradient (TD3) actor–critic model is introduced, dynamically observing the state of the smart home environment and suggesting control actions on the operation of the Energy Storage System and on the regulation of the indoor temperature. The TD3 actor–critic model leads to improved policy performance in the continuous control of these systems, mitigating the overestimation bias and improving the training stability of the intelligent agents. The efficiency of the proposed method is verified via simulations based on real data, achieving a beneficial trade-off between the energy cost and the thermal comfort compared to FedAvg and Fedprox baselines. The results show that the proposed pFedMe framework consistently outperforms FedAvg and FedProx in both convergence speed and overall reward, achieving an energy cost reduction of approximately 10% compared to the other schemes, while exhibiting marginal thermal comfort behavior. Full article