Search Results (503)

Fixed external shading is often optimized in isolation, although annual loads also depend on glazing, thermal storage, the window area, the climate, and plant efficiency. We tested whether these variables produced one transferable optimum or climate-conditioned near-optimal sets, defined as designs within 5% of each climate’s grid minimum. A balanced overhang depth-to-window-height ratio (D/H), solar heat gain coefficient (SHGC), and effective thermal-capacity factorial grid was simulated with EnergyPlus across six climates and four window-to-wall ratio (WWR) profiles, including a 60% high-glazing extension and 50% internal gain sensitivity. Across 4608 annual simulations, SHGC dominated cooling-dominated climates, whereas capacity dominated mixed and heating-dominated reference cases; halving gains changed five of six sampled optima. Physical archetypes preserved five of six best combinations, while heating, ventilation, and air-conditioning (HVAC) weighting changed the dominant factor in two climates and the best design in four. A non-fitted two-node resistance-capacitance (RC) model maintained $\rho \ge 0.965$ in 18 Phoenix/Helsinki parameter combinations but required climate-specific shortlist widths to recall near-optimal sets. These results support the reporting of climate-conditioned priorities and near-optimal sets, with reduced models, HVAC weighting, and gain scenarios used as diagnostics rather than final predictions. Full article

Thermal runaway propagation in large-capacity battery energy storage systems can be driven by casing conduction, vented products, and pack-level thermal coupling. This study experimentally evaluated whether a static dielectric-liquid boundary can prevent propagation between 314 Ah rectangular cells. Three-cell modules were tested in air and in two liquids at relative immersion heights of 10–20 mm, followed by two non-circulating 1P52S pack tests. Propagation was assessed using trigger-cell temperature and venting, adjacent-cell voltage and venting, and post-test state. Under AIR-0, adjacent-cell voltage fell to 0 V and propagation occurred; the trigger cell reached its primary peak of 613 °C at 1560 s, followed by a secondary-heating maximum of 971 °C at 1893 s. All six immersed module tests maintained stable adjacent-cell voltages, with no adjacent-cell venting or propagation, while trigger-cell maximum temperatures were 403–498 °C. Using a common window from trigger-cell venting to its primary peak, the apparent equivalent surface-averaged heat-flux indicator was 31.2 kW/m² in AIR-0 and 22.5–29.3 kW/m² under immersion, corresponding to a reduction of approximately 6.1–27.8%. In both pack tests, no fire, explosion, adjacent-cell venting, or pack-level propagation occurred. Under the tested module and pack configurations, no propagation from the trigger cell to adjacent cells was observed with static immersion, supporting its potential as a passive safety boundary for large-capacity battery storage systems. Full article

Trombe walls are widely used passive solar heating systems for cold winter climates, but suffer from large diurnal temperature swings and limited heat delivery after sunset. This study numerically investigates replacing the air in the double-glazing gap of a Trombe wall with a phase change material (PCM). A two-dimensional transient CFD model of a 4 m × 3 m room with a Trombe wall was developed in ANSYS Fluent using the enthalpy porosity method and the realizable k–ε turbulence model. Grid independence was confirmed with a 36,000-cell mesh, and the air-filled reference case was validated against published Trombe wall data within 9%. Hourly ambient temperature and solar heat flux records for a recent cold, clear winter day in Yazd, Iran, were imposed as boundary conditions. The PCM-filled cavity was cooler than the air-filled case shortly after sunrise, by about 1–1.6 °C, as the PCM resumed absorbing heat after releasing its reserve overnight. The two configurations converged around midday, after which the PCM-filled cavity became progressively warmer, reaching a margin of 4–6 °C in the early evening, and easing somewhat overnight while remaining the warmer of the two. The PCM-filled cavity also drove higher buoyancy induced velocities. The PCM layer’s nominal storage capacity was about 9.4 MJ per meter of wall length, over four orders of magnitude more than air, 89% as latent heat. Mean room temperature rose by 1.1 °C; the standard deviation and peak-to-peak swing increased accordingly, and time within the 20–26 °C comfort band fell from 59.8% to 47.0%, reflecting heat concentrated in the evening rather than spread evenly. Overall, the PCM-filled cavity increases heat availability specifically after sunset, when heating demand is greatest, rather than producing a general improvement in steady state thermal comfort, and does so without additional energy input. Full article

Buildings account for roughly 37% of energy-related CO₂ emissions, and space cooling already consumes nearly 10% of global electricity. Cooling demand is rising fastest in tropical cities, where air-conditioning could reach 45% of peak load, especially in India by 2050. This review critically examines thermal energy storage (TES) as a flexibility resource across three distinct scales: individual buildings, district heating and cooling networks, and city-level multi-energy systems. Using a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-based search of Scopus, Web of Science, and IEEE Xplore with primary and supplementary strings, 4447 records were identified, of which 174 were included. Each quantitative study was classified by validation level (simulation, laboratory, pilot, or operational) and by the centrality of thermal storage. Sensible, latent, and thermochemical storage technologies are compared using energy density (10–500 kWh/m³), efficiency (40–95%), cycle stability, and technology readiness. The review then evaluates the role of artificial intelligence (AI), machine learning, and Internet of Things platforms in forecasting, predictive control, and operational optimization of TES networks. Thirteen method families, grouped into AI and machine learning methods, optimization methods, control methods, and digital enabling technologies, are assessed against six explicitly defined criteria with evidence-coded scores. Among 47 quantitative studies, 37 (78.7%) are simulation-only, and only four (8.5%) report operational data. Direct TES-AI studies report simulated energy savings of 8–64% and peak load reductions of about 35%, whereas field-validated intelligent control reports 17% energy savings in a single real building experiment. The review also identifies inherent drawbacks of artificial intelligence-based operations, including limited interpretability, high data and computational demands, concept drift, and cyber vulnerabilities that increased peak electric load by 17.4% in a simulated attack. A structural imbalance in the literature is evident: most validated deployments remain at the building-scale, whereas urban-scale evidence is confined to district cooling, aquifer and pit storage, and multi-energy hub studies; no study reports the coordinated operation of distributed TES assets across multiple districts. A conceptual framework and a staged roadmap linking building, district, and urban scales are proposed. Priority research needs include urban-scale pilots in tropical climates, techno-economic assessment, interpretable and drift-robust AI, and interoperability standards that support United Nations’ Sustainable Development Goals 7, 11, and 13. Full article

The increasing adoption of renewable energy technologies in residential buildings necessitates intelligent energy management strategies capable of improving energy efficiency while maintaining occupant comfort. This study presents a smart home energy management framework integrating a Heating, Ventilation, and Air Conditioning (HVAC) system, rooftop photovoltaic (PV) generation, and a battery energy storage system (BESS) using a computationally efficient rule-based control strategy. The framework was developed and evaluated in MATLAB/Simulink, where HVAC thermal dynamics, household load demand, PV generation, and battery state-of-charge (SOC) behavior were coordinated through hysteresis-based temperature control and SOC-constrained battery management. Simulation results demonstrated that the proposed controller effectively maintained indoor temperature within the prescribed comfort band while enhancing renewable energy utilization. The integrated PV–battery system achieved a PV self-consumption rate of 100%, supplied 90.64% of the total household energy demand from renewable sources, and reduced grid electricity imports from 0.921 kWh to 0.103 kWh, corresponding to an 88.81% reduction in grid dependency. The battery operated safely within an SOC range of 34.18–50.00%, resulting in a utilization swing of 15.82 percentage points. These findings demonstrate that the proposed rule-based framework provides a practical, low-complexity, and reliable solution for improving renewable energy utilization, reducing grid reliance, and supporting sustainable smart residential energy management. Full article

To quantitatively evaluate the environmental performance of a Chinese solar greenhouse (CSG) in Northern China during the autumn–winter season and explore its matching relationship with the growth requirements of autumn–winter tomato production, continuous monitoring data collected throughout the tomato growing season were analyzed. The growth period was divided into four stages: Seedling, Flowering, Swelling, and Fruiting. An indicator framework was established to analyze air temperature, solar radiation, and humidity inside the CSG. Results showed that total indoor growing degree days (GDD) reached 1326.77 °C·d by harvest, substantially higher than outdoor conditions (476.30 °C·d). Stage-wise GDD exhibited a bimodal pattern, with the Seedling and Fruiting stages contributing 37% each, whereas the Swelling stage contributed only 10.38%. Notably, the relative GDD index (RGI) revealed a stage-specific thermal mismatch, peaking at 1.64 during the Seedling stage (indicating thermal surplus) and dropping to 0.67 during the Fruiting stage (reflecting heat insufficiency in deep winter). Across all growth stages, indoor daily mean accumulated radiation ranged from 7.05 to 8.12 MJ·m⁻²·d⁻¹, and transmittance increased from 61% to 85%. Nevertheless, indoor radiation supply remained strongly constrained by the seasonal decline in outdoor radiation. Humidity regulation displayed pronounced diurnal asymmetry and stage-specific differences. Relative humidity (RH) suitability rates across the four growth stages were 57%, 16%, 12%, and 17%, respectively. Low humidity was prominent during the Seedling stage, with RH below 60% accounting for 32% of the time. High humidity dominated the Swelling and Fruiting stages, with RH above 80% accounting for 67% and 72% of the time, respectively, accompanied by frequent short-term low-humidity fluctuations. Overall, although CSGs in Northern China can effectively buffer external low temperatures during autumn–winter production, they still face challenges such as stage-specific imbalances in solar thermal energy storage and release, weak indoor light availability (especially during winter), and insufficient synergistic humidity regulation. Full article

Fuzzy multi-criteria decision-making is important for technology assessment when expert judgments contain uncertainty, hesitation, and inconsistent evidence. This study develops a T-Spherical Fuzzy-Valued Neutrosophic Set (T-SFVNS)-based MEREC-EDAS framework for evaluating low-carbon cooling and energy-management technologies for data centers. Expert linguistic assessments are represented by T-Spherical Fuzzy-Valued Neutrosophic Numbers and aggregated before a score function is used at the explicit scalarization boundary. Standard MEREC then derives objective criterion weights from criterion-removal effects, and standard EDAS ranks alternatives by their positive and negative distances from the average score profile. The application evaluates nine technologies against ten criteria using assessments from thirty domain specialists. The corrected MEREC calculation assigns the greatest weights to carbon reduction potential (0.127), electricity demand reduction (0.125), maintenance complexity (0.124), operational cost efficiency (0.123), and cooling efficiency (0.123). The final ranking is Direct-to-Chip Liquid Cooling, Liquid Immersion Cooling, AI-Enabled Energy Management, Water-Side Free Cooling, Free-Air Cooling, Rear-Door Heat Exchanger Cooling, Hot/Cold Aisle Containment, Renewable-Powered Cooling, and Thermal Storage-Assisted Cooling. Weight perturbation, q-parameter, leave-one-expert-out, alternative-deletion, dominated-alternative, and multi-method comparisons show that the leading tier is robust, although the exact order of the two liquid-cooling technologies is sensitive in some scenarios. The findings provide a transparent and reproducible decision-support basis while explicitly acknowledging the information compression and rank-reversal limitations of score-based MCDM. Full article

This study presents an integrated multidisciplinary framework for evaluating the reuse of abandoned underground mining infrastructure in Bosnia and Herzegovina as sustainable underground energy storage systems that support the energy transition and decarbonization. The research focuses on the Central Bosnia and Tuzla coal basins, using case studies from the Zenica and Tuzla mining regions to assess Underground Pumped Hydroelectric Energy Storage (UPHES), Compressed Air Energy Storage (CAES), and gravity-based energy storage technologies. The methodology integrates geological and geotechnical characterization, thermo-hydro-mechanical (THM) analysis, thermodynamic calculations, and Multi-Criteria Decision Analysis (MCDA) to evaluate technical, operational, and safety performance. Methane mitigation, smart ventilation, thermal stability, and geomechanical behavior under cyclic loading were also considered. The results indicate that sedimentary coal basins are well suited for UPHES and gravity-based storage systems, with UPHES capacities reaching 1.75 GWh per cycle under optimized conditions, while the separately evaluated solid-mass gravity storage system provides a capacity of 6.15 MWh. Evaporite formations in the Tuzla Basin offer favorable conditions for CAES because of the low permeability and plasticity of halite, enabling storage capacities exceeding several GWh. THM analysis confirmed acceptable geomechanical stability during cyclic operation, while the economic assessment based on the Levelized Cost of Storage (LCOS) demonstrated the long-term competitiveness of Abandoned Mine Energy Storage (AMES) compared with battery technologies. Overall, the findings highlight abandoned mines as strategic low-carbon assets for renewable energy integration and regional post-mining transition. Full article

The sustained growth of cloud computing and AI training workloads drives data center expansion. Optimizing their control is therefore critical for reducing operational costs. Edge real-time control is indispensable for guaranteeing thermal safety, data sovereignty, and offline availability. Yet deploying Deep Reinforcement Learning (DRL) in production Heating, Ventilation, and Air Conditioning (HVAC) environments confronts cold-start risks, edge–cloud computational asymmetry, and multi-objective conflicts spanning energy efficiency, electricity cost, and thermal safety. To address these challenges, this paper proposes an edge-cloud collaborative physics-informed reinforcement learning framework for production data center HVAC control. The framework integrates a physics-informed cold-start solution using Adaptive Particle Swarm Optimization (APSO) to generate physically constrained initial policies on a gray-box digital twin without expert demonstration data, a three-time-scale edge–cloud architecture coordinating minute-level edge Soft Actor-Critic (SAC) real-time inference, weekly edge APSO online model identification, daily cloud Non-dominated Sorting Genetic Algorithm III (NSGA-III) thermal storage scheduling, and a constraint-aware safe projection layer that embeds thermal safety hard constraints directly into the neural network policy. The framework is validated through a seven-month production deployment spanning the complete summer-to-winter transition, comprising approximately $3.2$ million sensor records and evaluated with rigorous statistical methods. Full article

The semi-underground granary is a new type of energy-saving grain storage facility that can use shallow geothermal energy to reduce energy consumption during grain storage. However, unclear temperature fields and the lack of grain pile temperature control methods are not conducive to the design and application of semi-underground granaries. In this study, the temperature fields and temperature control methods for grain piles in a semi-underground granary were numerically investigated by using an experimentally verified COMSOL model and a collaborative simulation method combining steady-state heat transfer and dynamic heat transfer. Multiple grain storage temperature control methods for the semi-underground granary were presented to improve grain storage safety, including an intermediate floor slab, an embedded-pipe wall, floor burial depth, and envelope insulation. The results showed that there was significant spatial heterogeneity in the temperature field distribution of the grain pile in the semi-underground granary. The large thermal inertia of the soil and the stable low-temperature soil environment reduced the influence of outdoor air temperature variations on the grain pile temperature field. Installing an intermediate floor slab could achieve natural low-temperature grain storage in the underground section of the semi-underground granary. An embedded-pipe wall could effectively solve the problem of local temperature increases in grain piles caused by heat transfer through the granary walls. The floor burial depth of the semi-underground granary was a key influencing factor of heat transfer through the granary wall. Granary wall thickness had a significant impact on the thermal performance of the walls and the grain pile temperature field due to changes in wall insulation. These results can provide beneficial suggestions for guiding the design of grain storage temperature control methods in semi-underground granaries. Full article

Steel production involves energy-intensive thermal processes, where improvements in heat management can contribute to enhanced efficiency and reduced environmental impact. This study analyses the cooling and heating processes of steel billets in small-to-medium-sized steelworks, with the aim of identifying potential energy, time, and cost savings. Currently, billets leaving the casting process cool down freely in an open-air storage area from approximately their casting temperature to ambient conditions, and they are subsequently reheated to 1265 °C before rolling. A numerical model based on the finite difference alternating-direction implicit (ADI) method has been developed in MATLAB R 2025a to simulate these processes. The numerical implementation was verified against the analytical lumped-capacitance solution under the assumption of uniform billet temperature during slow cooling. For loading times of 15–30 min, the billets retained temperatures of 1112–806 °C after 15 days of insulated storage. Compared with reheating from 25 °C, the predicted heat savings were 672–936 MJ per billet, corresponding to fuel savings of 600–836 kWh_LHV, gross fuel-cost savings of EUR 18–25 per billet, and reheating-time reductions of 17.7–33.3 min. An improvement scenario was then analysed, consisting of placing 36 billets from each casting batch into an insulated container to reduce heat losses after casting. The results show that with reasonably well-insulated containers, billet temperatures can be maintained above 800 °C for up to 15 days. This increase in the inlet temperature to the reheating furnace reduces both the required energy and processing time. These results suggest that relatively simple thermal management strategies can improve energy efficiency and reduce operational demand in steel production, supporting more sustainable industrial processes. Full article

Aiming at frequency fluctuations caused by high-penetration renewable energy, a coordinated secondary frequency regulation strategy is proposed for hybrid energy storage stations in regional power grid. Hybrid energy storage stations contain electrochemical energy storage stations (EESSs) and compressed air energy storage stations (CAESSs). Complete mathematical models of concerned energy storage stations are constructed, and a multi-objective exponential distribution optimization (MOEDO) algorithm is proposed to optimize regulation cost, automatic generating control (AGC) command tracking and constrain the state of charge (SoC) of energy storage equipment. The proposed strategy rationally distributes secondary regulation instructions for hybrid energy storage stations and thermal power plants. In the end, simulation results on a two-area interconnected power grid are given to verify the effectiveness of proposed strategy. Results reveal that the presented method can effectively suppress frequency deviation and tie-line power oscillation and maintain SoC within a safe operating range. Full article

This paper presents an advanced load frequency regulation strategy for interconnected thermal power systems integrated with renewable energy sources and energy storage systems. A two-area non-reheat thermal power system is investigated, where photovoltaic generation is incorporated in Area 1 and wind turbine generation in Area 2 to assess the impact of renewable penetration on system dynamics and frequency stability. To improve the dynamic response under varying operating conditions, a novel multi-stage TDn(1+PIDn) controller is proposed. The TDn stage enhances transient shaping, while the PIDn stage provides superior damping and steady-state accuracy. The controller parameters are optimally tuned using the pied kingfisher optimizer (PKO) and compared with particle swarm and grey wolf-based optimizers. Furthermore, vanadium redox flow batteries, superconducting magnetic energy storage, and hydrogen–air fuel cells are integrated into the hybrid system to mitigate frequency oscillations caused by renewable intermittency. Offline simulations and real-time validation using the OPAL-RT OP4512 simulator are conducted under different dynamic scenarios. The obtained results demonstrate that the proposed PKO-TDn(1+PIDn) method achieves the best transient performance, for example, reducing the $∆F_1$ overshoot, undershoot and settling time by 28%, 15% and 7.5%, respectively, relative to its closest-performing counterpart while attaining the minimum ITAE value of 0.037309. Consistent improvements are observed across the key performance metrics, confirming the robustness and effectiveness of the scheme for modern hybrid power systems. Full article

Heat pumps are core equipment for efficient low-grade thermal energy utilization and low-carbon transformation of the energy structure, offering significant energy-saving potential in building heating and industrial waste heat recovery. This paper reviews the research progress and technical challenges of compression, absorption, and adsorption heat pumps as well as nanofluid-enhanced heat transfer technology and elastocaloric heat pump systems. Air source heat pumps can delay frosting through variable frequency, heat storage, and waste heat recovery. However, accurate prediction models for performance degradation under extreme cold conditions are lacking. Although ground source and water source heat pumps exhibit significant energy efficiency advantages, ground source systems may suffer from performance degradation due to underground thermal imbalance. The application of water source systems is strictly constrained by water resource conditions. Driven by low-grade waste heat, absorption heat pumps employing traditional working pairs suffer from crystallization, corrosion, or high rectification energy consumption. The COP of a single-effect cycle under 80~100 °C waste heat is only 1.2~1.9, while hybrid cycles can reach approximately 3.2 at 120~150 °C. Although adsorption heat pumps achieve significantly improved performance under continuous heat recovery cycles, the full-scale power density of novel adsorbents such as metal–organic frameworks is inferior to the power density of traditional silica gel. Moreover, under off-design conditions, the performance drops by 23~48% compared to theoretical values. Nanofluids can enhance heat transfer, but the long-term effects of particle agglomeration at high temperatures on pump power consumption and system compatibility remain to be systematically evaluated. Elastocaloric heat pump systems can achieve refrigerant-free cooling, but current prototypes still cannot compete with traditional vapor compression systems in long-cycle fatigue reliability and power density. Current heat pump technologies generally face challenges such as insufficient adaptability to extreme conditions, bottlenecks in working fluids and materials, and a lack of long-term validation. Future research must construct a multi-source coupling optimization system, address common problems in working fluids and materials, promote long-term validation and kilowatt-level prototype demonstrations, and drive the large-scale deployment and engineering application of heat pump technology toward high efficiency, intelligence, and high reliability. Full article

The accelerating transition toward renewable electricity generation has elevated grid-scale electrochemical energy storage from an ancillary grid service to a foundational infrastructure requirement. This review provides a comprehensive account of rechargeable battery technologies for stationary grid applications, spanning advanced lithium-ion systems, sodium-ion and post-lithium multivalent chemistries, vanadium and organic flow batteries, solid-state architectures, and high-energy-density future systems such as lithium-sulfur and metal-air cells. The techno-economic context of grid-scale storage is systematically examined, including performance metrics, market drivers, and regulatory frameworks. Each battery chemistry is analyzed with respect to electrochemical mechanism, cycle life, energy density, safety profile, material availability, and commercial readiness. Non-electrochemical storage technologies are discussed as system-level alternatives. Battery safety engineering, thermal management system design, thermal runaway mechanisms and prevention, and failure containment strategies are examined in depth, followed by analysis of critical material supply-chain vulnerabilities, life-cycle assessment, and recycling pathways. The expanding role of artificial intelligence, machine learning, and digital twin frameworks in optimizing performance and enabling predictive maintenance is reviewed. Key challenges, including material bottlenecks, manufacturing scalability, long-duration storage gaps, and the absence of harmonized performance standards, are identified, and the review concludes with a techno-economic roadmap toward cost-competitive, resilient, and low-carbon grid storage. Full article