Energy Technologies

Ice-based thermal energy storage systems are widely utilized for cooling and managing peak electrical demand globally, offering daily or weekly storage capabilities for both individual homes and larger office buildings. However, scaling these systems for district-level cooling or integrating them with renewable energy sources presents challenges, especially in accommodating larger volumes and addressing seasonal storage requirements in densely populated urban areas. This paper proposes a novel solution by evaluating subsurface ice-based thermal energy storage, in which the underground is subjected to seasonal freeze/thaw cycles. However, these cycles may influence ground behavior, affecting pore pressure and inducing ground movement. To systematically investigate these challenges, we enhance the TOUGH-FLAC simulator by integrating water/ice phase change capabilities and updating the effective stress–strain constitutive relation. Both modifications are validated against analytical solutions or experimental data. Through numerical simulations spanning a decade with ten seasonal freeze/thaw cycles, we evaluate the performance and long-term stability of a generic subsurface ice-based thermal energy storage system, considering factors such as ground permeability, freezing pipe spacing, freeze/thaw damage, and glycol solution temperature. The simulations indicate that ice formation induces pore pressure variations that drive seasonal surface heave and settlement, controlled by ground permeability, pipe spacing, and glycol solution temperature, along with tensile and localized shear deformation around freeze pipes. This highlights the need for accurate ground property characterization and geomechanical analysis for subsurface ice-based thermal energy storage.

Rapid growth of electric demand combined with increased penetration of intermittent power sources increases the value of flexible electric heating, but the techno-economic role of central hot-water storage under realistic tariffs remains unclear. This paper introduces HeatShift-HP, an open-source Python/Modelica co-simulation workflow. The framework couples a generic air-to-water heat pump and a stratified storage tank Functional Mock-up Unit with a supervisory rule-based controller and time series of weather, heating load, and end-user electricity prices. The framework targets plant-side flexibility and varies specific tank volume, nominal supply and return temperatures, heat-pump sizing, controller settings, and tariff variants, including a price-responsive boosting strategy constrained by a cost-neutral coefficient of performance approximation. The workflow is applied to a single-family reference building in Berlin and Chicago using 2025 weather and hourly retail prices with and without dynamic grid-fee components. A theoretical pre-analysis shows that typical hot-water buffers provide only limited stand-alone autonomy compared with building thermal mass. Annual simulations show that nominal system temperature is the dominant driver of both seasonal performance and paid heat costs. Lowering the nominal supply temperature from 40 ∘C to 35 ∘C reduces the median paid specific heat cost by about 3.7 % in Berlin and 4.4 % in Chicago for the standard controller, with similarly strong reductions under price-based control. By contrast, additional storage mainly improves operational flexibility by reducing cycling and increasing low-price heat generation. The pooled effect of price-based control remains below 1 % in both cities, but matched Berlin dynamic-grid cases show savings of up to 4.0 %. Dynamic grid fees in Berlin reduce median paid heat costs by about 7.0 %  to  7.6 % relative to the fixed-grid tariff. The results show that low-temperature system design minimises absolute heat costs, whereas the incremental value of price-based control depends primarily on sufficient storage, moderate control actions, and retail tariffs with sufficiently strong temporal price variation.

Buildings are becoming ever more complex to manage during operations, driven by increasing dynamics in energy demand, intermittent on-site power generation, and distributed energy resources, compounded by variable grid pricing signals and the rising threat of extreme weather events causing large-scale power outages. To achieve optimal performance in energy efficiency, demand flexibility, energy resilience, and occupant comfort, new methods and tools are essential for transforming design and operations across a building’s entire life cycle. Digital Twins (DTs), enabled by recent advances in ubiquitous sensing, big data, cloud computing, and AI, offer a transformative approach to creating a real-time, bi-directional digital counterpart of a physical building. This paper presents ten critical questions highlighting the most foundational issues underpinning the successful deployment of digital twins for buildings. The questions are holistically structured around three core themes to provoke significant research and accelerate adoption. The paper first examines the Users and Business aspects, including use cases, stakeholder alignment, business models, and governance structures. It then dives into the Technology foundation, addressing key components, layered software architectures, data integration, semantic interoperability, and the critical issues of cybersecurity and privacy. Finally, it explores Application aspects, such as the role of AI, performance metrics and standards, and the current landscape of software tools. DT technology is still in the early stages of adoption in buildings despite successful pilot applications in preventive maintenance, FDD, and advanced controls to optimize building performance. Several barriers need to be addressed to scale up DT technology in buildings, including high development and maintenance costs, the lack of data-rich infrastructure in buildings, and a shortage of skilled workforce.

Exposure to low indoor air temperature is a major contributor to temperature-related mortality during extreme cold events, especially when power outages disrupt operation of space heating systems. This study explores the impact of high-performance windows on the thermal resilience of residential buildings during extreme cold weather and grid power outages, as well as their long-term benefits through energy efficiency and reduced risk of property damage. Building performance simulations were conducted for reference residential buildings in three construction vintages and two major U.S. cities located in cold climate zones, considering two types of extreme cold events: short and severe, and long and milder. Our research found that even houses compliant with current energy codes struggle to maintain safe indoor temperatures for more than a few hours during power outages, necessitating rapid evacuations. High-performance windows can extend the thermal survivability time by up to 3.8 days within a 7-day cold snap and significantly reduce risk of bursting frozen water pipes, depending on the building’s insulation and infiltration level, cold event severity, and occupant vulnerability. This extended thermal safety time is crucial in scenarios where reduced mobility complicates emergency responses in senior housing. In addition to boosting thermal resilience, upgrading older homes with high-performance windows can reduce heating energy consumption by over 18 % and cooling energy by 15 %. Our findings highlight the need to incorporate thermal resilience assessments into new designs or major retrofits, including the use of typical and extreme weather scenarios and advanced technologies like high-performance windows.

Current Indoor Air Quality (IAQ) regulations rely on concentration thresholds for individual pollutants, which limits their ability to capture cumulative health impacts and hinders the development of performance-based ventilation standards. Building on prior work, including Annexe D of ASHRAE 62.2, this study introduces a harm-based framework that shifts the reference from indoor concentrations to a standardised emission rate scenario. The proposed Harm Metric aggregates multiple contaminant exposures into a single health outcome expressed in DALYs·(105 pers)−1·year−1, enabling an outcome-based evaluation of ventilation performance. Applied to a representative sample of the French dwelling stock, the metric shows a median total harm of 994 DALYs·(105 pers)−1·year−1. The main contributors are particulate matter, formaldehyde, nitrogen dioxide (NO2), and radon. A ventilation sensitivity analysis demonstrates the metric’s responsiveness, with total harm decreasing to around 800 DALYs·(105 pers)−1·year−1 when airflow exceeds 350 m³·h−1 in a 100 m2 dwelling. By linking emissions, ventilation, and health outcomes, this framework supports integrating chronic exposure impacts into building standards and codes. It offers policymakers a quantitative basis for transitioning towards performance-based regulation and highlights the need for improved emission scenarios using large-scale real-world data.

The central molecular zone (CMZ) resides in the inner few hundred parsecs of our Galaxy, and despite being the largest reservoir of dense molecular gas in the Milky Way, it has a relatively low present-day star formation rate (SFR) of ∼0.08 M⊙ yr−1. Continuum and spectral line observations from the Atacama Large Millimeter/submillimeter Array (ALMA) CMZ Exploration Survey (ACES) provide the first full-coverage, high-resolution map of the inner 200 pc of the CMZ at 3 mm. In this paper we present the ACES catalog of compact continuum sources, the most complete catalog of potential sites of star formation in the CMZ to date. Using an automated dendrogram-based source extraction procedure in combination with a by-eye morphological classification scheme, we produce a “full” catalog of 1735 detections in total. Additionally, we use spectral index measurements to generate a “filtered” catalog of 567 sources with minimal contamination from nonthermal filaments and extended free–free emission. We find that 359 (∼63%) of the filtered catalog sources are located at column densities <1023 cm−2, outside of the densest molecular cloud regions, 195 of which have not been identified in previous surveys. After cross-referencing with various catalogs generated from data at different wavelengths, we consider it likely that many of these newly discovered detections are produced by pre-/protostellar sources or compact H ii regions.

The urban microclimate is essential for accurate simulation-based urban building energy modelling (UBEM). However, a high spatial-resolution microclimate can increase the computational resources demands of UBEM. Surrogate modelling is one of the promising approaches for fast UBEM. This study proposes a bidirectional Long Short-Term Memory (LSTM)-based approach for simulation-based UBEM surrogate modelling. The estimations are aggregated into census tracts using total building floor area. A case study using UBEM to estimate annual hourly building energy use and anthropogenic heat from all existing buildings in Los Angeles County found that most of the surrogate models can complete the annual hourly simulation within 90 minutes with a normalized mean absolute error lower than 10%, and that the bidirectional LSTM outperforms the standard LSTM in accuracy. This study demonstrates the advantages of bidirectional RNN architecture in building energy surrogate modelling and is expected to promote long-term and high-resolution UBEM with detailed microclimates.

Brightening the Earth’s surface can increase the outflux of sunlight to space. This diminishes the net (downward minus upward) solar flux into the Earth’s radiative system, providing negative solar radiative forcing that can cool the atmosphere. We are quantifying the extent to which surface-brightening projects, such as installing solar-reflective roofs and pavements, can raise bottom-of-atmosphere (BOA) albedo, increase top-of-atmosphere (TOA) solar outflux, and reduce regional and global atmospheric temperatures. We compared four approaches to assessing BOA albedo: roof- or pavement-product documentation; local measurements with an albedometer (back-to-back pyranometers); extrapolation from extended-color aerial images (blue, green, red, and near-infrared); and multispectral satellite images sharpened by convolution with aerial images, selecting the last. We developed a technique to calculate the atmosphere’s upward solar transmittance anywhere in the Earth’s tropical and temperate zones based on analysis of BOA irradiances reported in the National Solar Radiation Database, then computed radiative forcing factors relating the increase in TOA solar outflux (negative solar radiative forcing) to the rise in BOA albedo (surface brightening). We identified atmospheric modeling experiments using both regional and global circulation models to quantify the atmospheric cooling potential of reflective surfaces at regional and global scales. These activities provide regional estimates of the atmospheric cooling benefits of reflective roofs and pavements.

Accurate and efficient evaluations of refrigerant thermophysical properties and their partial derivatives are essential for transient simulations of thermofluid systems, where several computations need to be executed at each integration time step. Since the utilization of an Equation of State for retrieving properties based on a pair of independent inputs typically involves numerical iterations in solution procedures, when the input variables differ from the refrigerant state variables employed in dynamic models, a variety of approaches including lookup table interpolation and curve fitting have been developed to explicitly approximate these properties based on the state variables, and consequently eliminate internal iterations. This paper presents an alternative method that exploits derivative-informed neural networks to model refrigerant properties explicitly from inputs of pressure and enthalpy, while ensuring consistent partial derivatives generated by differentiating the neural networks. Computational speed and accuracy of the proposed approach are demonstrated via transient simulations of a discretized heat exchanger model in Modelica, and comparisons against other property evaluation routines. Simulation results indicate that the proposed approach can realize a significant speedup with negligible discrepancies in predicted transients. The method is implemented in an open-source Modelica library.

Anthropogenic heat (AH) from buildings contributes to urban overheating, especially during heat waves, yet building retrofit studies usually evaluate energy savings without assessing impacts on AH. This study quantifies how common building retrofit measures affect both building energy use and AH emissions across the City of Los Angeles. Using a bottom-up urban building energy modeling framework coupled with high-resolution local weather from the Weather Research and Forecasting model with Building Effect Parameterization (WRF-BEP), we evaluate eleven retrofit measures and two multi-measure retrofit packages. HVAC and LED lighting retrofits provide the largest city-wide annual site energy savings, while roof coating is most effective for reducing AH. A package optimized for energy savings reduces summer site energy use by about 32% (2.3 TWh), while a package incorporating AH-focused measures reduces the total AH by over 50% (137 PJ) with minimal difference in energy savings. The AH-aware package produces substantially greater urban cooling, reducing mean near-surface air temperature by up to 0.62 ℃ and peak temperature by up to 3.79 ℃. These results show that retrofit strategies selected only for energy savings may overlook major opportunities for urban heat mitigation. The study provides a framework for integrating AH into building retrofit planning and urban heat resilience policy.