Search Results (1,663)

Enhancing the real-world performance of sustainably designed and certified green buildings remains a significant challenge, particularly in hot climates where efforts to improve thermal comfort often conflict with energy efficiency goals. In the United Arab Emirates (UAE), even newly constructed facilities with green building certifications present opportunities for retrofitting and performance optimization. This study investigates the energy and thermal comfort performance of a LEED Gold-certified, mixed-use university campus in Dubai through a calibrated digital twin developed using IES thermal modelling software. The analysis evaluated existing sustainable design strategies alongside three retrofit energy conservation measures (ECMs): (1) improved building envelope U-values, (2) installation of additional daylight sensors, and (3) optimization of fan coil unit efficiency. Simulation results demonstrated that the three ECMs collectively achieved a total reduction of 15% in annual energy consumption. Thermal comfort was assessed using operative temperature distributions, Predicted Mean Vote (PMV), and Predicted Percentage of Dissatisfaction (PPD) metrics. While fan coil optimization yielded the highest energy savings, it led to less favorable comfort outcomes. In contrast, enhancing envelope U-values maintained indoor conditions consistently within ASHRAE-recommended comfort zones. To further support energy reduction and progress toward Net Zero targets, the study also evaluated the integration of a 228.87 kW rooftop solar photovoltaic (PV) system, which offset 8.09% of the campus’s annual energy demand. By applying data-driven thermal modelling to assess retrofit impacts on both energy performance and occupant comfort in a certified green building, this study addresses a critical gap in the literature and offers a replicable framework for advancing building performance in hot climate regions. Full article

Photovoltaic–thermal (PVT) technology has attracted considerable attention for its ability to significantly improve solar energy conversion efficiency by simultaneously providing electricity and heat during the day. PVT technology serves a purpose in condensers and subcoolers for passive cooling in refrigeration systems at night. In our previous work, we proposed a switchable film-insulated photovoltaic–thermal–passive cooling (PVT-PC) module to address the structural incompatibility between diurnal and nocturnal modes. However, the performance of the proposed module strongly depends on two key design parameters: the structural height and the vacuum level of the air cushion. In this study, a numerical model of the proposed module is developed to examine the impact of design and meteorological parameters on its all-day performance. The results show that diurnal performance remains stable across different structural heights, while nocturnal passive cooling power shows strong dependence on vacuum level and structural height, achieving up to 103.73 W/m² at 10 mm height and 1500 Pa vacuum, which is comparable to unglazed PVT modules. Convective heat transfer enhancement, induced by changes in air cushion shape, is identified as the primary contributor to improved nocturnal cooling performance. Wind speed has minimal impact on electrical output but significantly enhances thermal efficiency and nocturnal convective cooling power, with a passive cooling power increase of up to 31.61%. In contrast, higher sky temperatures degrade nocturnal cooling performance due to diminished radiative exchange, despite improving diurnal thermal efficiency. These findings provide fundamental insights for optimizing the structural design and operational strategies of PVT-PC systems under varying environmental conditions. Full article

Outdoor air pollution poses a major global health risk, yet monitoring remains insufficient, especially in regions with limited infrastructure. Solar-powered monitors could allow for increased coverage in regions lacking robust connectivity. However, reliable sample collection can be challenging with these systems due to extreme temperatures and insufficient solar energy. Proper planning can help overcome these challenges. Air Sampler Solar and Thermal Optimization for Reliable Monitoring (Air-STORM) is an open-source tool that uses meteorological and solar radiation data to identify temperature and solar charging risks for air pollution monitors based on the target deployment area. The model was validated experimentally, and its utility was demonstrated through illustrative case studies. Air-STORM simulations can be customized for specific locations, seasons, and monitor configurations. This capability enables the early detection of potential sampling risks and provides opportunities to optimize monitor design, proactively mitigate temperature and power failures, and increase the likelihood of successful sample collection. Ultimately, improving sampling success will help increase the availability of high-quality outdoor air pollution data necessary to reduce global air pollution exposure. Full article

Dynamic and Adaptive solar systems demonstrate a greater potential to enhance the satisfaction of occupants, in terms of indoor environment quality and the energy efficiency of the buildings, than conventional shading solutions. This study has evaluated Dynamic and Adaptive Photovoltaic Shading Systems (DAPVSSs) through a comprehensive analysis of six shading designs in which their energy production and the comfort of occupants were considered. Energy generation, thermal comfort, daylight, and glare control have been assessed in this study, considering multiple orientations throughout the seasons, and a variety of tools, such as Rhino 6.0, Grasshopper, ClimateStudio 2.1, and Ladybug, have been exploited for these purposes. The results showed that the prototypes that were geometrically more complex, designs 5 and 6 in particular, had approximately 485 kWh higher energy production and energy savings for cooling and 48% better glare control than the other simplified configurations while maintaining the minimum daylight as the threshold (min DF: 2%) due to adaptive and control methodologies. Design 6 demonstrated optimal balanced performance for all the aforementioned criteria, achieving 587 kWh/year energy production while maintaining the daylight factor within the 2.1–2.9% optimal range and ensuring visual comfort compliance during 94% of occupied hours. This research has established a framework that can be used to make well-informed design decisions that could balance energy production, occupants’ wellbeing, and architectural integration, while advancing sustainable building envelope technologies. Full article

Although concentrated solar power (CSP) coupled with calcium looping (CaL) offers a promising avenue for efficient thermal chemical energy storage, calcium-based sorbents suffer from accelerated structural degradation and decreased CO₂ capture capacity during multiple cycles. This study used Density Functional Theory (DFT) calculations to investigate the mechanism by which Mg and Ni doping improves the adsorption/desorption performance of CaO. The DFT results indicate that Mg and Ni doping can effectively reduce the formation energy of oxygen vacancies on the CaO surface. Mg–Ni co-doping exhibits a significant synergistic effect, with the formation energy of oxygen vacancies reduced to 5.072 eV. Meanwhile, the O²⁻ diffusion energy barrier in the co-doped system was reduced to 2.692 eV, significantly improving the ion transport efficiency. In terms of CO₂ adsorption, Mg and Ni co-doping enhances the interaction between surface O atoms and CO₂, increasing the adsorption energy to −1.703 eV and forming a more stable CO₃²⁻ structure. For the desorption process, Mg and Ni co-doping restructured the CaCO₃ surface structure, reducing the CO₂ desorption energy barrier to 3.922 eV and significantly promoting carbonate decomposition. This work reveals, at the molecular level, how Mg and Ni doping optimizes adsorption–desorption in calcium-based materials, providing theoretical guidance for designing high-performance sorbents. Full article

According to the United Nations Environment Programme reports, buildings are responsible for nearly 40% of energy-related emissions; therefore, energy-optimized building design is crucial to reduce the reliance on non-renewable energy sources as well as greenhouse gas emissions. The OECD reports indicate the use of Building Information Modelling (BIM) as one of the effective strategies for decarbonization of buildings, since a 3D digital representation of both physical and functional characteristics of a building can help to design a more efficient infrastructure. An efficient integration of solar energy in building design can be vital for the enhancement of energy performance in terms of heating, cooling, and lighting demand. This paper presents results of an analysis of how factors related to the use of daylight, such as automatic control of artificial lighting, external shading, or the visual absorptance of internal surfaces, influence the energy efficiency within an example room in two different climatic zones. The simulation was conducted using Design Builder software, with predefined occupancy schedules and internal heat gains, and standard EPW weather files for Warsaw and Genua climate zones. The study indicates that for the examined room, when no automatic sunshades or a lighting control system is utilized, most of the final energy demand is for cooling purposes (45–54%), followed by lighting (42–43%), with only 3–12% for heating purposes. The introduction of sunshades and/or the use of daylight allowed for a reduction of the total demand by up to half. Moreover, it was pointed out that often neglected factors, like the colour of the internal surfaces, can have a significant effect on the final energy consumption. In variants with light interior, the total energy consumption was lower by about 3–4% of the baseline demand, compared to their corresponding ones with dark surfaces. These results are consistent with previous studies on daylighting strategies and highlight the importance of considering both visual and thermal impacts when evaluating energy performance. Similarly, possible side effects of certain actions were highlighted, such as an increase in heat demand resulting from a reduced need for artificial lighting. The results of the analysis highlight the potential of a simulation-based design approach in optimizing daylight use, contributing to the broader goals of building decarbonization. Full article

Nanofluids are an innovative working medium in solar hot water installations (DHWs), thanks to their increased thermal conductivity and heat transfer coefficient. The aim of this work was to assess the effect of Al₂O₃ nanofluids in a water–ethylene glycol base (40:60%) and with the addition of Tween 80 surfactant (0.2 wt%) on thermal efficiency (ε) and exergy (η_ex) in a plate heat exchanger at DHW flows of 3 and 12 L/min. The numerical NTU–ε model was used with dynamic updating of thermophysical properties of nanofluids and the solution of the ODE system using the ode45 method, and the validation was carried out against the literature data. The results showed that the nanofluids achieved ε ≈ 0.85 (vs. ε ≈ 0.87 for the base fluid) and η_ex ≈ 0.72 (vs. η_ex ≈ 0.74), with higher entropy generation. The addition of Tween 80 reduced the viscosity by about 10–15%, resulting in a slight increase of Re and h-factor; however, the impact on ε and η_ex was marginal. The environmental analysis with an annual demand of Q = 3000 kWh/year and an emission factor of 0.2 kg CO₂/kWh showed that for ε < 0.87 the nanofluids increased the emissions by ≈16 kg CO₂/year, while at ε ≈ 0.92, a reduction of ≈5% was possible. This paper highlights the need to optimize nanofluid viscosity and exchanger geometry to maximize energy and environmental benefits. Nowadays, due to the growing problems of global warming, the analysis of energy efficiency and carbon footprint related to the functioning of a building seems to be crucial. Full article

Internal fins are commonly utilized as a passive technique to enhance natural convection, but their efficiency depends on complex interplay between fin design, material properties, and convective strength. This study presents an extensive numerical analysis of buoyancy-driven flow in square cavities containing a single horizontal fin on the hot wall. Over 9000 simulations were conducted, methodically varying the Rayleigh number (Ra = 10 to 10⁵), Prandtl number (Pr = 0.1 to 10), and fin characteristics, such as length, vertical position, thickness, and the thermal conductivity ratio (up to 1000), to assess their overall impact on thermal efficiency. Thermal enhancements compared to scenarios without fins are quantified using local and average Nusselt numbers, as well as a Nusselt number ratio (NNR). The results reveal that, contrary to conventional beliefs, long fins positioned centrally can actually decrease heat transfer by up to 11.8% at high Ra and Pr due to the disruption of thermal plumes and diminished circulation. Conversely, shorter fins located near the cavity’s top and bottom wall edges can enhance the Nusselt numbers for the hot wall by up to 8.4%, thereby positively affecting the development of thermal boundary layers. A U-shaped Nusselt number distribution related to fin placement appears at Ra ≥ 10³, where edge-aligned fins consistently outperform those positioned mid-height. The benefits of high-conductivity fins become increasingly nonlinear at larger Ra, with advantages limited to designs that minimally disrupt core convective patterns. These findings challenge established notions regarding passive thermal enhancement and provide a predictive thermogeometric framework for designing enclosures. The results can be directly applied to passive cooling systems in electronics, battery packs, solar thermal collectors, and energy-efficient buildings, where optimizing heat transfer is vital without employing active control methods. Full article

Using solid particulates as a heat transfer medium for concentrated solar power (CSP) systems has many advantages, positioning them as a superior option compared with conventional heat transfer media such as steam, oil, air, and molten salt. However, a critical imperative lies in the comprehensive evaluation of the properties of potential solid particulates intended for utilization under such extreme thermal conditions. This paper undertakes an exhaustive examination of both ambient and high-temperature thermophysical properties of four naturally occurring particulate materials, Riyadh white sand, Riyadh red sand, Saudi olivine sand, and US olivine sand, and one well-known engineered particulate material. The parameters under scrutiny encompass loose bulk density, tapped bulk density, real density, sintering temperature, and thermal conductivity. The results reveal that the theoretical density decreases with the increase in temperature. The bulk density of solid particulates depends strongly on the particulate size distribution, as well as on the compaction. The tapped bulk density was found to be larger than the loose density for all particulates, as expected. The sintering test proved that Riyadh white sand is sintered at the highest temperature and pressure, 1300 °C and 50 MPa, respectively. US olivine sand was solidified at 800 °C and melted at higher temperatures. This proves that US olivine sand is not suitable to be used as a thermal energy storage and heat transfer medium in high-temperature particle-based CSP systems. The experimental results of thermal diffusivity/conductivity reveal that, for all particulates, both properties decrease with the increase in temperature, and results up to 475.5 °C are reported. Full article

Evaporative cooling (EC) offers an energy-efficient alternative to direct expansion (DX) cooling but suffers from high water consumption. This limitation can be mitigated by pre-cooling incoming fresh air using cooler exhaust air via energy recovery. This study presents and optimizes a solar-driven EC system integrated with underfloor air distribution (UFAD) to enhance thermal comfort and minimize water use in a temporary office in Riyadh’s arid climate. A 3D CFD model was developed and validated against published data to simulate indoor airflow, providing data for thermal comfort evaluation using the predicted mean vote model in cases with and without energy recovery. A year-round hourly energy analysis revealed that the solar-driven EC-UFAD system reduces grid power consumption by 93.5% compared to DX-based UFAD under identical conditions. Energy recovery further cuts annual EC water usage by up to 31.3%. Operational costs decreased by 84% without recovery and 87% with recovery versus DX-UFAD. Full article

Access to clean cooking remains a major challenge in rural and off-grid areas where traditional fuels are costly, harmful, or scarce. Solar cooking offers a sustainable solution, but many existing systems suffer from fixed positioning and low efficiency. This study presents a low-cost, dual-axis solar tracking parabolic dish cooker designed for such regions, featuring adjustable pot holder height and portability for ease of use. The system uses an Arduino UNO, LDR sensors, and a DC gear motor to automate sun tracking, ensuring optimal alignment throughout the day. A 0.61 m parabolic dish with ≥97% reflective silver-coated mirrors concentrates sunlight to temperatures exceeding 300 °C. Performance tests in April, June, and November showed boiling times as low as 3.37 min in high-irradiance conditions (7.66 kWh/m²/day) and 6.63 min under lower-irradiance conditions (3.86 kWh/m²/day). Compared to fixed or single-axis systems, this design achieved higher thermal efficiency and reliability, even under partially cloudy skies. Built with locally available materials, the system offers an affordable, clean, and effective cooking solution that supports energy access, health, and sustainability in underserved communities. Full article

This research proposes an integrated lighting and solar shading strategy to improve energy efficiency and user comfort in a retrofit project in a temperate-humid climate. The study examines a future library addition to an existing faculty building in Bursa, featuring highly glazed façades (77% southwest, 81% northeast window-to-wall ratio), an open-plan layout, and situated within an unobstructed low-rise campus environment. Trade-offs between daylight availability, heating, cooling, lighting energy use, and visual and thermal comfort are evaluated through integrated lighting (DIALux Evo), climate-based daylight (CBDM), and energy simulations (DesignBuilder, EnergyPlus, Radiance). Fifteen solar shading configurations—including brise soleil, overhangs, side fins, egg crates, and louvres—are evaluated alongside a daylight-responsive LED lighting system that meets BS EN 12464-1:2021. Compared to the reference case’s unshaded glazing, optimal design significantly improves building performance: a brise soleil with 0.4 m slats at 30° reduces annual primary energy use by 28.3% and operational carbon emissions by 29.1% and maintains thermal comfort per ASHRAE 55:2023 Category II (±0.7 PMV; PPD < 15%). Daylight performance achieves 91.5% UDI and 2.1% aSE, with integrated photovoltaics offsetting 129.7 kWh/m² of grid energy. This integrated strategy elevates the building’s energy class under national benchmarks while addressing glare and overheating in the original design. Full article

The large-scale integration of new energy is an inevitable trend to achieve the low-carbon transformation of power systems. However, the strong randomness of wind power, photovoltaic power, and loads poses severe challenges to the safe and stable operation of systems. Existing studies demonstrate insufficient integration and handling of source-load bilateral uncertainties in wind–solar–fossil fuel storage complementary systems, resulting in difficulties in balancing economy and low-carbon performance in their energy storage configuration. To address this insufficiency, this study proposes an optimal energy storage configuration method considering source-load uncertainties. Firstly, a deterministic bi-level model is constructed: the upper level aims to minimize the comprehensive cost of the system to determine the energy storage capacity and power, and the lower level aims to minimize the system operation cost to solve the optimal scheduling scheme. Then, wind and solar output, as well as loads, are treated as fuzzy variables based on fuzzy chance constraints, and uncertainty constraints are transformed using clear equivalence class processing to establish a bi-level optimization model that considers uncertainties. A differential evolution algorithm and CPLEX are used for solving the upper and lower levels, respectively. Simulation verification in a certain region shows that the proposed method reduces comprehensive cost by 8.9%, operation cost by 10.3%, the curtailment rate of wind and solar energy by 8.92%, and carbon emissions by 3.51%, which significantly improves the economy and low-carbon performance of the system and provides a reference for the future planning and operation of energy systems. Full article

Under the dual-carbon goals, with the rapid increase in the proportion of fluctuating power sources such as wind and solar energy, the regulatory capacity of traditional thermal power generation can no longer meet the demand for intra-day fluctuations. There is an urgent need to tap into the potential of flexible load-side regulatory resources. To this end, this paper proposes a low-carbon economic optimal dispatching strategy for virtual power plants (VPPs), considering the aggregation of diverse flexible and adjustable resources with the integration of wind and solar power. Firstly, the method establishes mathematical models by analyzing the dynamic response characteristics and flexibility regulation boundaries of adjustable resources such as photovoltaic (PV) systems, wind power, energy storage, charging piles, interruptible loads, and air conditioners. Subsequently, considering the aforementioned diverse adjustable resources and aggregating them into a VPP, a low-carbon economic optimal dispatching model for the VPP is constructed with the objective of minimizing the total system operating costs and carbon costs. To address the issue of slow convergence rates in solving high-dimensional state variable optimization problems with the traditional plant growth simulation algorithm, this paper proposes an improved plant growth simulation algorithm through elite selection strategies for growth points and multi-base point parallel optimization strategies. The improved algorithm is then utilized to solve the proposed low-carbon economic optimal dispatching model for the VPP, aggregating diverse adjustable resources. Simulations conducted on an actual VPP platform demonstrate that the proposed method can effectively coordinate diverse load-side adjustable resources and achieve economically low-carbon dispatching, providing theoretical support for the optimal aggregation of diverse flexible resources in new power systems. Full article

This paper calculates the expected cost of uncertainty in solar and wind energy using the uncertainty cost function (UCF), with a primary focus on computational processing time. The comparison of processing time for the uncertainty cost quantification (UCQ) is conducted through three methods: the Monte Carlo simulation method (MC), numerical integration method, and analytical method. The MC simulation relies on random simulations, while numerical integration employs established numerical formulations. These methods are commonly used for solving cost optimization problems in power systems. However, the analytical method is a less conventional approach. The analytical method for calculating uncertainty costs is closely related to the UCF, as it relies on a mathematical representation of the impact of uncertainty on costs, which is modeled through the UCF. A multi-objective approach was employed for scheduling an energy management system, that is to say, thermal–wind–solar energy systems, proposing a simplified method for modeling controllable renewable generation through UCF with an analytical method, instead of the complex probability distributions typically used in traditional methods. This simplification reduces complexity and computational processing time in optimization problems, offering greater accuracy in approximating real distributions and adaptability to various scenarios. The simulations performed yielded positive results in improving cost estimation and computational efficiency, making it a promising tool for enhancing economic distribution and grid operability. Full article