Search Results (181)

Ground source heat pump (GSHP) systems contribute to sustainable building decarbonization while confronting two intertwined challenges: long-term ground thermal imbalance in shallow borefields and the requirement for coordinated operation between thermal systems and electrical grid dynamics. Hybrid shallow–deep borefield configurations have been proposed to mitigate thermal imbalance for sustainable geothermal resource exploitation, yet their grid-interactive demand–response potential remains unexplored. Here, we develop a coupled thermal–electrical model for a shallow–deep mixed GSHP (SDBHE) system equipped with water-tank thermal storage, validated against scaled sand-tank experiments (3.5–8.3% error), and assess its year-round performance under time-of-use electricity tariffs for a 200,000 m² residential district in cold-climate conditions. The SDBHE system reduces the required shallow borehole count by 28% and total drilling length by 22% compared with a shallow-only baseline, saving 11% on operational electricity costs over 10 years. Integrating water-tank thermal storage with a 50% load-shifting strategy yields an additional 10.9–11% cost reduction without degrading the system’s coefficient of performance. Under higher load-shifting ratios, the combined capital and operational savings reach 19–29%, with the optimal allocation assigning the incremental high-price-period load preferentially to deep boreholes (COP 6.29 versus 5.25 for shallow). These results demonstrate that integrating shallow and deep geothermal tiers with thermal storage enables both capital-efficient borefield design and economically viable demand-side grid participation. The findings are bound by the cold-climate residential context and the rule-based control scheme; field-scale validation and lifecycle cost analysis are needed to generalize the conclusions. Full article

The Smart Readiness Indicator (SRI) provides a European framework for assessing the capacity of buildings to optimise operation, respond to occupants and interact with energy grids. Its current catalogue covers nine domains: heating, cooling, domestic hot water, ventilation, lighting, dynamic envelope, electricity, electric vehicle charging, and monitoring and control. This Perspective argues that this boundary is too narrow for complex buildings, where services such as lifts, security, fire safety, swimming pools, irrigation, outdoor lighting, kitchens, laundries, refrigeration, laboratories, and information and communication technology (ICT) rooms may significantly affect energy use, water use, safety, resilience, maintenance and user experience. The paper proposes five additional service families for SRI 2.0: mobility and accessibility; safety and security; water and outdoor environmental services; outdoor and site infrastructure; and special energy-intensive and facility services. These should be introduced as optional, building-type-specific modules assessed through familiar SRI functionality levels. This would make the SRI more realistic, actionable and relevant. Full article

Building- and residential-scale energy systems are becoming increasingly complex due to the growing use of renewable energy sources, variable generation profiles, and uncertainties in user demand. This study comprehensively examines the role of electrical, thermal, and hydrogen-based energy storage technologies in building-scale hybrid energy systems and evaluates these systems alongside energy management strategies. In this context, lithium-ion batteries, supercapacitors, flywheel systems, thermal energy storage solutions, and hydrogen-/fuel cell-based architectures are discussed in terms of their technical characteristics, intended uses, limitations, and complementary aspects. The reviewed studies show that individual storage technologies remain limited in their ability to meet all operational requirements, whereas hybrid storage architectures offer significant advantages in terms of power quality, energy flexibility, energy storage system lifetime, renewable energy utilization, and long-duration energy supply security. Furthermore, energy management systems are shown to be critical not only for cost minimization but also for user comfort, grid interaction, forecasting accuracy, uncertainty management, and the coordination of storage units operating at different timescales. Consequently, achieving high efficiency, low-carbon operation, and energy autonomy in building- and residential-scale systems requires the integrated design of multilayered hybrid storage approaches that are supported by intelligent energy management. Full article

AI-driven growth is pushing data-center electricity demand from 415 TWh (2024) toward 945 TWh by 2030. Demand-response research has matured on operational levers but treats the physical envelope as largely exogenous, leaving construction-phase decisions that bound achievable flexibility unmodeled. This article integrates three previously separate bodies of literature (data-center demand response, grid-interactive efficient buildings, and stochastic optimal control under irreversibility) into a single framework that prices construction-phase flexibility as a portfolio of real options, pairing elicitation-derived (FAHP) weights with simulation-derived (Sobol) variance indices. None of the individual techniques is new; the contribution is their synthesis and the finding that architectural and site decisions carry the dominant financial leverage in the model, whereas a literature-grounded synthetic-persona prior (twenty-five LLM-simulated personas) prioritizes mechanical-electrical systems; a divergence we frame as a screening diagnostic between an LLM prior and the model. The Flex-by-Design Readiness Index (FDRI) is a nineteen-dimension taxonomy across architectural, MEP, and site-urban layers, weighted by Fuzzy AHP. The FDRI–ROV model formalizes the decision as stochastic optimal control under irreversibility. Calibrated to PJM, ERCOT, and CAISO (2024–2026) for a 100 MW plant at N = 10,000 paths over thirty years, it yields +$79 M net option value at Full FDRI for PJM (additive upper bound; substitution-corrected ≈ +$41 M, 1.7× CapEx; 2.4× CapEx PJM, 2.1× ERCOT, 2.8× CAISO); on both the additive (2.4–2.8×) and corrected (1.7×) bases the pre-registered H2 threshold of Vtotal/Ctotal ≥ 3× is not met. Sobol decomposition places architectural and site layers at ST ≈ 0.56 each versus MEP at 0.15, exposing waste-heat-export and regulatory-avoided-cost dimensions as under-recognized leverage. Out-of-sample validation against four hyperscale projects yields 11% MAPE, reported as an n = 4, single-period proof of concept.A pro-rata extrapolation across all ~43 GW of incremental U.S. capacity gives a nominal ~$34 billion through 2035, but this applies the single most optimistic scenario uniformly; applying the substitution-corrected per-plant value with competition, policy, and adoption decay multipliers, the defensible 2035 opportunity is ≈$3–$18 billion (central ≈$7 billion), with $34 billion retained only as an undecayed ceiling. Full article

We formulate and analyze a 26-dimensional nonlinear dynamical system governing a doubly-fed induction generator (DFIG) wind energy conversion system coupled to an infinite bus through a dynamic transmission line. Seven interacting subsystems—aerodynamics, a two-mass drivetrain, a fourth-order machine, rotor- and grid-side converter controllers, a phase-locked loop, and a pitch regulator—are assembled into a single vector field $\dot{\mathbf{x}}=\mathbf{f}(\mathbf{x},\mathbf{u})$ on ${\mathbb{R}}^{26}$, derived in dimensionless coordinates. Strict positivity of the determinant $\mathrm{\Delta }=L_s{L}_r-L_m^2=\sigma L_s{L}_r$ for every physically admissible machine renders the flux–current map invertible, so the right-hand side is well defined; the nodal Kirchhoff constraint forms a semi-explicit differential-algebraic relation that we eliminate to obtain an explicit ordinary differential equation. The central contribution is a constructive scheme for the equilibria: the 26 stationarity conditions $\mathbf{f}({\mathbf{x}}^{\star },\mathbf{u})=\mathbf{0}$ are solved by an iterative voltage-matching procedure converging to a residual below ${10}^{-11}$ per unit—essentially machine precision—which removes the spurious start-up transients common in reported simulations. Analytically chosen feedback gains induce a hierarchy of well-separated time scales, placing the closed loop in the multiple-time-scale class; the separation is made quantitative through explicit small parameters ${\epsilon }_i$ formed from the ratios of subsystem time constants. Numerical integration of a GE $3.6$ MW configuration confirms the construction: under stationary forcing, the rotor speed stays within $1.32\times {10}^{-5}$ pu of the equilibrium, and under a large-amplitude wind program ($11\to 14\to 9$ m/s) spanning the full operating envelope, it is regulated to within $0.065\%$, while the DC-link voltage deviation remains below $2.4\times {10}^{-5}$ pu and the power balance closes with residual below ${10}^{-3}$ pu, the ≈2% mechanical–electrical gap being the modeled losses. Linearization about the computed equilibrium yields a Jacobian whose spectrum lies entirely in the open left half-plane, establishing local asymptotic stability and exposing the individual electromagnetic, torsional, and control modes. The model furnishes a rigorously initialized, analytically transparent basis for linearization, spectral stability analysis, and bifurcation study. Its practical value is that a consistent equilibrium and a certified spectrum remove the start-up transients and undocumented tuning that otherwise let initialization artifacts masquerade as genuine dynamics, so that the model can serve as a trustworthy building block for weak-grid and wind-farm stability studies. Full article

Commercial building demand response can support sustainable grid operation by providing demand-side flexibility for renewable-rich power systems; however, baseline-based measurement and verification usually report only event-period gross load reduction. Post-event rebound from heating, ventilation, air-conditioning, and lighting controls can cause gross-only reporting, resulting in overstating the net reduction at the building–grid interface. Standard baseline estimators have not been systematically evaluated in the post-event window. This study formulates a rebound-aware net-reduction accounting framework that reports the event-period gross reduction, post-event rebound, and rebound-adjusted net effect under the same eligible-day baseline structure. The framework was evaluated on the paired ComStock 2025 Release 2 Actual Meteorological Year 2018 profiles for commercial buildings in California, Texas, and New York with five baseline estimators and three rebound-window definitions. In a balanced 3960-building sample, the median true gross reduction was 62.7 kWh and the 2 h rebound was 5.49 kWh. The pooled median post-minus-event mean absolute error was −0.209 kWh per 15 min interval. The material gross-as-net reporting risk affected 70.9% of building-event-method cases, but rebound-aware net reporting was conditionally valuable rather than uniformly superior. These results show that rebound diagnostics can improve transparent and auditable demand response reporting for sustainable grid-interactive building programs. Full article

Amid the global drive for carbon peaking and carbon neutrality, integrating renewable energy into building energy systems to mitigate photovoltaic (PV) intermittency and realize low-carbon energy supply has become a critical research frontier. This study proposes a novel dual-storage renewable energy system integrating solar energy storage system (SES), hydrogen energy storage system (HES), and an alkaline fuel cell (AFC). The model was validated using a two-story single-family residence as the case study, with residential load profiles and Xi’an’s climatic conditions considered under real-world scenarios. An adaptive energy management strategy is developed to dynamically coordinate PV utilization, hydrogen dispatch, and grid interaction, while recovering AFC waste heat to enhance overall efficiency. Targeting minimized lifecycle cost (LCC) and levelized cost of energy (LCOE), the GenOpt multi-objective optimization model optimizes key design parameters. Key results show 74.2% annual renewable energy penetration, 68.5% carbon reduction versus conventional systems, and robust seasonal operation: PV dominates summer supply (81.3% self-sufficiency), while AFC compensates in winter (62.4% hydrogen contribution). The system reduces annual grid dependence by 43.7% with a minimum LCOE of ~12.9 USD/MWh, bridging technical feasibility and economic practicality to provide actionable insights for building-scale renewable integration. Full article

The transition toward a net-zero economy requires buildings to evolve from passive consumers into Grid-Interactive Efficient Buildings (GEBs). Traditional Building Energy Management Systems (BEMSs) lack the dynamic intelligence needed to control stochastic energy flows and solve multi-objective optimization problems. To systematically map this technological shift, this study conducts a Systematic Literature Review (SLR) following PRISMA guidelines, analyzing a curated corpus of 144 studies (135 primary technical papers and 9 review articles). Due to the significant diversity in methodological approaches within cyber-physical testbeds and IoT architectures discovered through the literature review process, a qualitative narrative and architectural synthesis was conducted rather than a quantitative meta-analysis. Based on this framework, this review examines emerging paradigms for Cognitive Buildings based on Artificial Intelligence of Things (AIoT), edge computing, and semantic interoperability. This review discusses the evolution of algorithms from predictive Deep Learning (DL) and Deep Reinforcement Learning (DRL) to newer approaches such as Agentic AI and Physics-Informed Neural Networks (PINNs). These new methods address the fundamental “sim-to-real” gap while ensuring thermodynamic consistency and safety in physical actuation. It also presents strategic applications in multi-objective optimization of HVAC systems, demand response, energy arbitrage, and predictive maintenance. Moreover, this review tackles major real-world deployment issues by introducing Federated Learning for data privacy, Transfer Learning for portfolio scaling, and TinyML for overcoming the computational carbon paradox of “Green AI.” By quantifying this paradox, the review contrasts the massive computational carbon footprint of cloud-based model training against the milliwatt-class efficiency of localized edge deployments. Overall, this review outlines potential research directions toward the development of autonomous Cognitive Digital Twins (CDTs) and Human-Centric Personal Comfort Models (PCMs). Full article

Community multi-building energy management is a key approach for reducing carbon emissions from the building sector and alleviating peak grid pressure. However, load coupling among buildings and coordinated energy-storage operation make control-policy design highly challenging. To address the limitation of the standard multi-agent proximal policy optimization (MAPPO) algorithm, in which the centralized critic simply concatenates building observations and therefore struggles to model inter-building interactions, this paper proposes an improved MAPPO algorithm with a multi-head-attention-enhanced centralized critic, referred to as a multi-head-attention MAPPO (MHA-MAPPO). Without changing the decentralized execution framework, the proposed method improves the critic network in three aspects. First, a dual-branch gated embedding module is designed to adaptively fuse local building observations and global interaction information. Second, an interaction-attention path is constructed to explicitly capture pairwise dependencies among buildings through multi-head attention. Third, a context-attention path is introduced to extract high-level community-level global features by means of learnable query vectors. These improvements enable the critic to estimate the joint-state value more accurately and provide more reliable advantage estimates for all agents. Experiments in the CityLearn environment show that, compared with the original MAPPO, MHA-MAPPO improves the mean evaluation reward by approximately 19.2%, reduces the reward standard deviation by one order of magnitude, and decreases peak net load and total net load by approximately 15.4% and 35.5%, respectively. The results verify the effectiveness of multi-head attention for coordinated multi-building scheduling. The proposed method provides a useful reference for improving multi-agent reinforcement learning algorithms in community energy management. Full article

The aim of this article is to identify how research on smart buildings has evolved in the context of the energy transition, with particular emphasis on energy flexibility, grid interaction, market integration, and policy barriers. The study addresses a gap in previous reviews, which have often focused on individual technological domains, building automation, or smart-readiness assessment, while paying less attention to the conditions under which smart buildings become active energy-system resources. A systematic review protocol based on the PRISMA logic was combined with bibliometric mapping and qualitative synthesis. Bibliographic data were retrieved from Scopus on 28 February 2026 and covered 663 English-language journal articles published between 2015 and February 2026. A core set of 63 studies was selected through explicit cluster-based and relevance-based criteria for in-depth qualitative synthesis. The results show a gradual shift from component-level efficiency research towards system-level studies in which smart buildings are analyzed as flexible demand-side assets, distributed energy nodes, and participants in emerging market mechanisms. At the same time, the evidence base remains uneven: many studies rely on simulation or case-specific modeling, while empirical validation, interoperability, occupant behavior, business models, and regulatory implementation remain less mature. The article contributes by distinguishing observed bibliometric patterns from conceptual interpretation and by integrating technological, economic, behavioral, and regulatory evidence into a framework explaining the persistent implementation gap in smart building deployment. Full article

The building sector accounts for nearly 30% of global energy use and 28% of CO₂ emissions, with residential buildings in Canada contributing about 17% of national energy demand. In cold regions such as Labrador, approximately 82% of this consumption is associated with space heating and domestic hot water, making heating the dominant residential load, while fossil-fuel furnaces and electric baseboard heaters remain common. These conditions highlight the need for efficient and sustainable heating alternatives for cold-climate residential buildings. This study examines the design and performance of a hybrid solar photovoltaic (PV) and geothermal heat pump (GTHP) system for a typical detached home in L’Anse-au-Loup, Labrador, Newfoundland and Labrador, Canada (51.52° N, 56.84° W), with the goal of improving energy efficiency and reducing dependence on the electrical grid. Heating and cooling loads were developed using the Hourly Analysis Program (HAP 6.1), while system operation and economic performance were assessed through the Hybrid Optimization Model for Electric Renewables (HOMER Pro 3.18.3). The proposed design combines a rooftop PV array, a ground-source heat pump, and second-life lithium-ion batteries repurposed from retired electric vehicles to lower costs and support short-term energy storage. The system is modelled under grid-connected conditions to reflect realistic operation for northern households. Results show that the hybrid system can meet annual electrical and thermal needs while reducing grid consumption by more than half. Annual carbon emissions decrease by roughly 4–5 tonnes, and repurposed batteries offer a cost-effective alternative to new storage. Overall, the study demonstrates that PV–GTHP systems can provide reliable, efficient, and practical energy solutions for cold-climate homes. Full article

Against the backdrop of global energy transition and the widespread adoption of Hydrogen-Blended Natural Gas (HBNG), the safety of urban gas pipeline networks faces severe challenges. This paper systematically reviews the research progress of numerical simulation in the field of natural gas pipeline safety, focusing on its core supporting roles throughout the “Leakage–Dispersion–Evolution–Consequence” disaster chain. First, it analyzes the kinetic modeling of high-pressure leakage holes and property corrections based on real gas equations of state, elaborating on the numerical characterization of HBNG multi-component transport. Second, it compares the dispersion mechanisms and environmental coupling modeling methods in typical scenarios such as buried porous media, confined spaces in utility tunnels, underwater environments, and urban building clusters. Third, it reviews leakage monitoring technologies based on physical field simulation and data-driven approaches (e.g., Convolutional Neural Network, Long Short-Term Memory), emphasizing the value of numerical simulation in constructing digital twin training sets. Furthermore, it explores the dynamic evolution of explosion flame–shock wave interactions and the evaluation models for secondary disaster consequences. Finally, the current research status of grid-based risk pre-warning and emergency response strategies is summarized. In conclusion, numerical simulation is not only a robust method for precisely quantifying and characterizing complex physical mechanisms but also a critical technological foundation for building smart and resilient energy cities. Future research should focus on the deep coupling of multi-physics fields, physics-informed learning, and the development of system-level integrated defense systems. Full article

Rapid urbanization reshapes urban land systems and intensifies surface thermal heterogeneity, yet nonlinear day–night land surface temperature (LST) responses to grey–green spatial organization and building morphology remain insufficiently understood, particularly in thermally stressed areas across the urban–rural gradient. Using Xi’an, China, as a case study, this study develops a priority-area-based land–climate interaction framework. Priority areas were defined as grid cells where elevated LST coincided with relatively strong local explanatory relationships between LST and land-cover or morphological variables. Multiscale geographically weighted regression (MGWR), gradient boosting decision trees (GBDTs), SHAP-based interpretation, and threshold sensitivity analysis were combined to identify dominant drivers, nonlinear response patterns, and interaction structures of daytime and nighttime LST. The results show pronounced day–night differentiation: daytime hotspots were concentrated in the built-up core, whereas nighttime hotspots extended toward the urban–rural fringe. Daytime LST was mainly associated with building coverage and grey-space organization, while nighttime LST was more strongly related to mean building height and the cooling contribution of green-space coverage. The analysis further identified localized empirical response ranges for built-up intensity, grey-space connectivity, building height, and green-space coverage within the priority areas. These findings clarify how land-cover configuration and building morphology jointly shape day–night surface thermal responses and provide context-specific evidence for land-use planning and targeted urban heat mitigation. Full article

Buildings are the primary energy consumption layer of Renewable Energy Communities (RECs) and a key target for net-zero policy under the EPBD recast. This scoping review applies the PRISMA-ScR framework to map Digital Twin (DT) architectures for building-scale and community-scale energy management in REC configurations. A Scopus search yielded a final analytical corpus of 102 studies, coded through an eight-dimensional thematic matrix covering lifecycle phases, digitalization objectives, enabling technologies, DT capability dimensions, and data realism. DT is the dominant enabling technology (55.9%), followed by IoT (23.5%) and machine learning (22.5%). Research is concentrated in the Planning and Design phase (77.5%) and markedly underrepresented in Implementation and Commissioning (16.7%). Notably, only 10.8% of studies integrate real-time operational data, exposing a significant gap between simulation-based research and the deployment conditions required under current EPBD mandates. The evidence base supports building energy monitoring, demand forecasting, and flexible grid operation but remains limited for retrofit verification, standardized net-zero KPIs, and operational workflows in existing stock. Critical DT capability gaps persist in Data Services (7.8%) and User Experience (18.6%). Overall, DT architectures show genuine potential for grid-interactive, net-zero building management, yet the field presents unresolved structural challenges for large-scale real-world deployment. Full article

Smart meters increasingly operate as grid-edge sensing and communication nodes, extending their role beyond conventional digital billing by generating records for local energy trading. In such settings, smart meter-derived records may support coordination, participant interaction, validation, billing, and settlement across different trading architectures. Once these records leave the metering edge, their security and privacy risks depend on how they are routed, reused, protected, and interpreted across centralized, transactive, and peer-to-peer trading workflows. In this review, we examine smart meter-based energy trading through a record-centric and framework-oriented lens. We first clarify the role of smart meters and smart meter-derived records, then compare three representative trading frameworks in terms of data-path structure, coordination pattern, trust organization, and validation or settlement positioning. Building on the comparison, we identify three lifecycle-based layers of issues: record integrity and temporal consistency, insecure transmission and interface access security, and confidentiality and privacy exposure. We also review existing mitigation mechanisms and remaining limitations for each issue layer. We conclude that future work should prioritize lifecycle-wide record governance, temporal continuity, privacy–accountability co-design, and deployable protection across hybrid trading environments. Full article