Search Results (371)

This paper evaluates the energy performance and lifecycle environmental impact of three retrofit pathways—conventional insulation, an extensive green roof, and rooftop photovoltaics—for two lamella-type residential buildings (three-story, five-story; over 40 years old) in Blocks 45 and 70, Novi Beograd, Serbia. The study builds on an architectural concept treating the flat roof as an active environmental surface. Energy demand was quantified via calibrated dynamic simulation for a baseline and three scenarios—insulation (S1), insulation with an extensive green roof (S2), and insulation with rooftop photovoltaics (S3)—with environmental performance assessed using cradle-to-use LCA (1 m² heated floor area, 50-year period) covering global warming potential, cumulative energy demand, and Environmental Footprint 3.1 indicators. Insulation and the green roof achieve nearly identical reductions in heating demand (27–31%) and lifecycle GWP/CED (10–13%), the green roof performing marginally better while also offering evapotranspirative cooling and urban co-benefits. Photovoltaics deliver the largest lifecycle GWP reduction (26% three-story, 15% five-story) and cut acidification by up to 36%, but increase mineral resource depletion, water and land use. Benefits of both roof-level measures scale with roof-to-floor-area ratio, favoring lower-rise buildings, supporting prioritization of green roof and photovoltaic retrofit across the district lamella stock. Full article

Decarbonizing the German building sector by 2045 requires a rapid transformation of the heating technology stock, in which decentralized heat pumps (HPs) compete with district heating (DH) and green-gas-based solutions. Yet, the existing literature has not sufficiently resolved which segments of the building stock are most appropriately served by which low-carbon technology. This paper addresses this gap through a bottom-up, dynamic analysis using the agent-based building stock model RENDER-Building, which combines building-specific environmental heat-source potentials with DH and gas distribution infrastructure availability across Germany. Three explorative techno-economic scenarios are evaluated, differing in their electricity network charge development, DH expansion, and gas infrastructure trajectories. The modeling results show that HPs are cost-competitive over their lifetime in most building stock segments, delivering unit heat at an average cost of 11–15 ct/kWh. Between 11 and 15 million units are projected to be heated by HPs by 2045, covering 25 to 30% of building heating demand. Settlement type and local heat-source availability are found to be the primary determinants of feasibility and adoption. The findings underline the importance of ensuring the availability of energy carriers and stable long-term policies for a cost-effective and climate-friendly transformation of heating in buildings. Full article

Seasonal energy storage effectively addresses seasonal electricity supply–demand imbalances. This study proposes a cross-seasonal compressed air energy storage system using a depleted gas reservoir as the storage reservoir. A six-stage compression–expansion system with intercooling and reheating is designed. Also, the heat of compression is recovered for district heating, and the expansion of cold energy is for cooling supply, thereby avoiding cross-seasonal heat storage costs. For the YD1 depleted gas reservoir case, the system achieves a round-trip efficiency of 57.85% and an exergy efficiency of 70.9%. The total energy utilization ratio, which represents the combined utilization of the electricity, heating, and cooling outputs relative to the corresponding energy input, reaches 166.6%. Incorporating revenue from the heating and cooling sales, the dynamic payback period is 2.38 years, and the investment recovery ratio reaches 4.40. Parametric analysis indicates that thermodynamic performance improves with the increase in discharge pressure and decrease in discharge power. Economic performance improves with longer daily operating hours and plant lifetime. This study demonstrates that depleted gas reservoirs combined with well-designed surface combined cooling, heating, and power systems offer a new research and development direction for large-scale, long-duration seasonal storage, facilitating renewable energy integration and grid stability. 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 European Union has set an ambitious goal of achieving net-zero emissions by 2050, and 90% reduction by 2040, through its Green Deal policy. A promising solution to this challenge lies in the adoption of Fifth-Generation District Heating Networks (5GDHNs) that operate at low temperatures, collecting and distributing heat from diverse sources (energy geostructures, asphalt pavement solar thermal collectors, industrial waste heat and waste heat from buildings’ cooling plants). The system’s design allows for heat storage underground, primarily during summer months, with distribution occurring via pipelines during winter. As part of the REHEAT project, a study has been conducted focusing on a simulation model developed using TRNSYS software. This model incorporates solar thermal collectors installed beneath the parking area asphalt pavements as a thermal energy source, coupled with a borehole thermal energy storage system. The setup is designed to meet the heating and cooling demands of a multiplex cinema situated in Northern Italy. The study presents the optimization of the system, reporting the monthly and annual data on energy balances and system efficiency. The findings demonstrate significant energy savings when compared to traditional heating and cooling systems (50.6% non-renewable primary energy reduction) and even greater CO₂ emission reduction (63.2%). Also, the economic analysis reveals positive results both from the point of view of operating costs and taking into account investment costs, highlighting the potential of 5GDHN as a sustainable solution for urban energy needs for a real case as the main novelty of this study. Full article

Heating and cooling account for roughly half of EU final energy demand, yet the decarbonisation of community-led district heating remains underrepresented in the finance literature. This paper examines the financial viability of heat cooperative business models through a three-model typology (administrative, leasing, asset) developed within the HeatCOOP project, applied to a financial feasibility analysis of two Slovenian biomass district heating projects. Both adopt the asset model via a public–private concession framework; capital expenditures range from €617,000 to €1.87 million and annual heat delivery from 1100 to 3300 MWh. Sensitivity analysis across grant rates of 0–65% shows that the subsidy rate is the dominant financial viability determinant: all four variants achieve positive net present value and internal rate of return of 7.3–7.8% at approximately 45% subsidy, while none are viable without public co-financing. The levelised cost of heat ranges from 85–97 €/MWh on a private basis to 114–134 €/MWh on an unsubsidised full-cost basis. Comparison with fourteen European benchmark cases shows that public subsidy functions not as a market-failure correction but as a structural substitute for cooperative member capital in environments where the cooperative tradition is nascent—with direct implications for subsidy programme design targeting cooperative district heating. Full article

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

Urban Building Energy Modeling (UBEM) offers a scalable, physics-based method to simulate energy demand at the district level, enabling data-driven district energy demand planning and optimization. However, translating UBEM into a reliable, openly replicable workflow remains a significant methodological gap. In particular, limited attention has been devoted to the development of transparent and transferable UBEM workflows capable of systematically quantifying the impact of modeling assumptions on district-scale thermal demand accuracy. This paper presents and validates a five-step UBEM pipeline integrating freely available geospatial data from OpenStreetMap (OSM), archetype-based building characterization, multi-scenario EnergyPlus simulation via the Urban Modeling Interface (UMI) within a structured validation framework. To improve interpretability and reproducibility, a dedicated three-scenario simulation protocol was developed to isolate and quantify the influence of geometry simplifications, archetype assumptions, and weather data fidelity on model accuracy. The workflow is demonstrated through application to a real district heating system (DHS) in northern Italy, encompassing UBEM results validated against monitored consumption data at different temporal resolutions. The refined model achieves a district-scale annual magnitude error of 1.30% between real and simulated data. Persistent limitations in domestic hot water representation and peak load estimation are identified as priorities for future development. Full article

This paper investigates the use of a hybrid renewable heat-supply system based on photovoltaic–thermal collectors, an industrial heat pump, thermal energy storage, and electrical energy storage for a small- to medium-scale district-heating network. A dynamic lumped-parameter model was developed and applied to hourly data from an existing network. The photovoltaic–thermal collector model accounts for low-temperature operation, wind effects, precipitation, and condensation-related heat gains, while the heat pump is represented using compressor performance characteristics under variable source and sink temperatures. The analysis focuses on whether the proposed configuration can meet summer heat demand and reduce reliance on a conventional peak or backup source during shoulder periods. The results show that, during an extended non-heating season, the system can supply approximately 90–100% of the district-heating demand while maintaining a daily mean coefficient of performance in the range of approximately 2.0–3.1. The photovoltaic–thermal field and electrical energy storage do not provide full electrical self-sufficiency, but they reduce grid electricity import; in July and August, the electricity autarky coefficient is approximately 48–49%. The results indicate that the proposed system can serve as a seasonal renewable heat source for district heating. Further refinement of the configuration, operating setpoints, and control strategy could improve its shoulder-season performance. Full article

The energy transition entails numerous challenges related to the generation and distribution of various forms of energy and heat. In the case of electricity, these include, among others, challenges associated with transmission; however, these are usually less severe than those related to heat transmission, particularly due to limited access to district heating transmission networks. At the same time, heat storage is considerably less expensive than electricity storage, which means that heat management and balancing are also less costly than balancing electricity generation and demand. These two factors provided the rationale for selecting mobile heat transport from locations where surplus heat is generated to potential end-users as the subject of analysis. In this context, analytical methods were applied to determine the relationship between the economic efficiency of such a solution and selected technical and economic parameters, including CAPEX, OPEX, heat transport costs, and heat prices. The analysis demonstrated that, under current price conditions as of 2025, the economic efficiency of this solution, expressed using the net present value measure, may exceed zero only under specific conditions. These include relatively short transport distances, a high frequency of transport cycles, and favourable heat prices. Nevertheless, in the future, due to potential increases in heat prices or the possibility of subsidising this type of heat transport, particularly with respect to CAPEX, the solution may become economically attractive. Full article

The growing integration of variable renewable energy sources (VRES) challenges power system flexibility and may cause curtailment due to excess capacity, grid constraints, or operational and market factors. Power-to-Heat (PtH) technology can mitigate these issues by coupling electricity and district heating sectors, providing additional flexibility and supporting decarbonisation. This study develops a long-term generation capacity expansion model that integrates PtH and district heating system (DHS) operation to achieve balanced VRES penetration. The model includes DHS heat demand balances and links electricity and heat via thermal power plants, combined heat and power (CHP) plants, and PtH units. The methodology is applied to Ukraine’s Integrated Power System and district heating demand through 2040, employing typical daily load profiles discretised into six four-hour segments. Results demonstrate the feasibility of deploying PtH electric boilers during the non-heating season, when high RES and base load nuclear generation create surplus electricity. These boilers convert excess wind and solar power into thermal energy for district heating, displacing natural gas-fired technologies and simultaneously decarbonising electricity and heat supply. Full article

High indoor temperatures during the heating season increase the indoor–outdoor temperature difference and lead to additional heating demand in residential buildings in severe cold regions. However, the quantitative relationship between measured overheating and the potential for optimizing heating temperatures remains unclear. This study investigated case study dwellings in Ulanhot, Xilinhot, and Hailar using field measurements and EnergyPlus simulations to characterize indoor thermal conditions during the heating season and estimated the reduction in heating demand under comfort-oriented temperature adjustments. The results show that all primary occupied spaces exhibited elevated temperatures to varying degrees. The mean bedroom temperature in Xilinhot was 28.03 °C, and the temperature exceeded 28 °C for 63.89% of the monitored time. In Hailar, the mean temperatures in the living room and bedroom were 27.35 °C and 27.78 °C, respectively, and the corresponding time fractions above 26 °C were 90.21% and 95.57%. Under standardized prototype dwelling conditions, heating demand increased continuously with the setpoint temperature. Increasing the setpoint from 18 °C to 26 °C raised seasonal area-normalized heating demand from 64.14–98.18 to 103.39–143.50 kWh m⁻² across the three cities. Reducing an overheating setpoint of 26 °C to 20 °C lowered seasonal heating demand by 23.94–28.94%; adjusting the measured temperature reference scenarios to 20 °C produced reductions of 24.49–33.60%. The district heating emission factor scenarios further showed that, when 20 °C temperature optimization was combined with the low-coal emission parameters, seasonal area-normalized operational carbon emissions decreased by 57.22–60.03% relative to the 2020 26 °C baseline. These findings indicate that comfort-oriented heating temperature adjustment can effectively reduce residential heating demand in severe cold regions and provide quantitative support for temperature optimization, refined control, and low-carbon operation in district-heated dwellings. Full article

Building materials and their spatial distribution play a significant role in determining the outcomes of human-caused and natural disasters in urban and peri-urban areas. However, building-level data on building and roofing materials are scarce. Here, we explore the feasibility and performance of a Convolutional Neural Network (CNN) model using spectrally transformed high-resolution multispectral imagery to map roofprints (i.e., classifying and delineating roofing materials at the building footprint-level) in Washington, District of Columbia (D.C.) and Denver, CO, United States. To generate consistent training data, we integrate geospatial vector data of individual building footprints with real estate industry-derived building-level roofing material data to create labeled image data from Planet SuperDove imagery. We compare the CNN classifier to a pixel-based machine learning (ML) model to demonstrate the capability of our roofprints mapping approach. With F1-scores ranging from 0.56 to 0.95 for the most common roof material classes, the CNN model outperformed the pixel-based ML classifier by 15% and 17% in Washington, D.C., and Denver, respectively. Results demonstrate within-domain robustness for the studied metropolitan areas, which are characterized by differing building densities, roof morphologies, and material patterns. While cross-region transferability was not evaluated, our findings provide a controlled comparison of pixel-based and context-aware approaches for rooftop material mapping and highlight the importance of hierarchical representations that integrate spectral information with roof texture, edge characteristics, spatial arrangement, and neighborhood context for improving classification performance. Accurately mapping building materials has the potential to advance urban planning and environmental policies, including assessments of heat exposure, energy demand, as well as hazard risk and community resilience. Full article

This study presents a BIM-integrated life cycle analysis framework (screening-level) for climate-responsive urban energy performance assessment at district scale. The methodology addresses the need for consistent evaluation of operational energy demand under both design interventions and future climate conditions. A mixed-use district in Milan is used as a case study, where parametric BIM massing models (LOD 200–300) are coupled with building energy simulation to analyze three scenarios: a baseline configuration (S0), an envelope optimization scenario (S1), and a future climate scenario based on CMIP6 morphed weather data (S2). The framework enables comparative assessment of energy performance across consistent geometric, operational, and climatic assumptions. Results indicate that envelope optimization reduces energy use intensity by approximately 15–22% across building typologies. Under future climate conditions, cooling demand increases significantly, while reduced heating requirements result in a total district energy use intensity of 33.6 kWh/m²·year (1.60 GWh/year). An indicative carbon assessment based on simulated energy use highlights cooling-driven electricity as the dominant contributor to operational emissions under future conditions. The findings demonstrate that climate change primarily redistributes energy demand between heating and cooling rather than uniformly increasing total consumption, and confirm the value of BIM-integrated, scenario-based workflows for supporting climate-responsive urban design decisions. Full article

This study addresses the technical, environmental, economic, and systemic role of multi-apartment residential buildings as hydrogen consumption nodes within urban energy systems. A representative five-story building comprising 30 apartments and 2400–2800 m² of heated floor area, located in a cold European climate, was modelled with an annual heat demand of approximately 185,000 kWh. Four heating configurations were assessed: a conventional natural gas/biomethane boiler (baseline), a hydrogen boiler, a hydrogen-fuel-cell combined heat and power (CHP) system, and a hybrid heat-pump–hydrogen solution. Dynamic simulations indicate that all hydrogen-based systems can fully satisfy space heating and domestic hot water demand without modifications to the internal hydronic distribution network. The fuel cell CHP achieved an overall efficiency of 93%. It generated approximately 54,000 kWh/year of on-site electricity, while the hybrid configuration reached a seasonal efficiency of 108% and the highest primary energy reduction (46%). Operational CO₂ emissions decreased from 37,800 kg/year (gas baseline) to 1900 kg/year (green hydrogen boiler), 1200 kg/year (fuel cell CHP), and 900 kg/year (hybrid system), corresponding to reductions of up to 98%. Peak-load analysis demonstrated improved operational stability in CHP and hybrid systems, characterised by reduced cycling frequency and enhanced thermal resilience through hydrogen storage integration. Capital expenditure (CAPEX) ranged from 41,000 EUR (gas baseline) to 101,000 EUR (fuel cell CHP), reflecting additional storage, safety, and control requirements. Over a 20-year lifecycle (5% discount rate), the hybrid system achieved the lowest levelized cost of heat (0.076 EUR/kWh), followed by fuel cell CHP (0.081 EUR/kWh), compared to 0.087 EUR/kWh for gas. Payback periods ranged between 9 and 13 years, depending on configuration and hydrogen pricing assumptions. Sensitivity analysis identified a break-even hydrogen price of approximately 0.085 EUR/kWh, while carbon pricing above 100 EUR/t CO₂ significantly improves economic competitiveness. District-scale aggregation modelling suggests that hydrogen-equipped multi-apartment buildings can reduce grid electricity imports by 30–40% through on-site generation and seasonal storage. The findings confirm that multi-apartment buildings offer structural and economic advantages for early hydrogen deployment compared to dispersed housing typologies. By combining high demand density, centralised infrastructure, and compatibility with sector-coupling strategies, such buildings can function as distributed energy hubs within decarbonized urban systems. Full article