Hasil untuk "Energy conservation"

L. Aditya, T. Mahlia, Behzad Rismanchi et al.

R. Thauer, K. Jungermann, K. Decker

Hao Wu, Yan Zhang, Xinrui Lyu et al.

Understanding the influence of water adsorption on gas transport properties is critical for optimizing coalbed methane (CBM) extraction. This study investigates the influence of adsorbed water on methane diffusion and apparent permeability in three coals representing sub-bituminous, bituminous, and anthracite ranks. The samples were selected as geologically representative end-members from major coalbed methane basins in China. Adsorbed water was introduced via humidity equilibration at 97% relative humidity, simulating residual moisture conditions typical of partially dewatered reservoirs. Pressure decay experiments show that under dry conditions, apparent permeability exhibits a non-monotonic variation with coal rank, with the medium-rank coal displaying the highest value among the three tested samples. Upon exposure to adsorbed water, apparent permeability decreases by 39% to 70% across all ranks, with greater suppression observed in low-rank and high-rank coals compared to the medium-rank sample. This rank-dependent response is attributed to differences in pore structure and water distribution associated with coalification history. While the findings are based on single samples per rank and reflect a simplified moisture condition, they provide mechanistic insight into how coal maturity modulates the sensitivity of gas transport to adsorbed water, offering implications for permeability modeling during the dewatering phase of coalbed methane recovery.

Qinglin Yang, Yang He, Lukai Zhou et al.

Driven by the combined effects of global warming and the urban heat island (UHI) effect, building energy consumption has been rising steadily in recent years. The photovoltaic-cool roof (PVCR) system has emerged as an effective solution for urban energy conservation and carbon reduction. However, existing research on the energy-saving benefits of PVCR remains relatively limited, and none of these studies have considered the interaction between photovoltaic modules and high-reflectivity roofs (also called cool roof, CR). Therefore, field experiments were conducted to compare the thermal performance of the PVCR system against that of three conventional roof configurations, including photovoltaic roof (PVR), asphalt roof (AR), and CR. The results demonstrate that the PVCR system achieves a remarkable daytime cooling effect, with a maximum temperature reduction of 29 °C compared to the AR system, and maintains lower temperature fluctuations throughout the entire day. In addition, the findings reveal that the photovoltaic modules exhibit a lower average temperature when installed on the cool roof, with a temperature decrease of 0.15 °C relative to the asphalt roof. A numerical model incorporating the photothermal interaction between a high-reflectivity surface and PV modules was developed and validated with experimental data. The numerical model considers the interactions between the photovoltaic (PV) modules and the high reflectivity surface, including shortwave radiation reflection, longwave radiative exchange, and convective heat transfer. The sensitivity analysis indicates that a change in the spacing and height of the PV arrays from 0.3 m to 0.5 m increases the relative energy-saving efficiency of the system. The conclusions drawn in this paper can provide a reference for the application of the PVCR system in hot-summer and cold-winter areas.

Mark A. Andor, K. Fels

Research from economics and psychology suggests that behavioral interventions can be a powerful climate policy instrument. This paper provides a systematic review of the existing empirical evidence on non-price interventions targeting energy conservation behavior of private households. Specifically, we analyze four nudge-like interventions referred to as social comparison, commitment devices, goal setting, and labeling in 44 international studies comprising 105 treatments. This paper differs from previous systematic reviews by solely focusing on studies that permit the identification of causal effects. We find that all four interventions have the potential to significantly reduce energy consumption of private households, yet effect sizes vary immensely. We conclude by emphasizing the importance of impact evaluations before rolling out behavioral policy interventions at scale.

Yanbo Zhan, Zhigang Lu, Liangce He et al.

In recent years, large-scale power outages due to climate change have occurred frequently worldwide, causing significant economic losses and severe social impacts. Against the backdrop of extremely cold weather in northeastern China. This paper presents a new operating model using asymmetric Nash cooperative game theory and resource bankruptcy theory to minimize power system operating costs under extreme weather conditions. Firstly, a fair power allocation model based on the principle of minimum variance is used to determine the boundary conditions of power resource allocation. Secondly, an asymmetric Nash cooperative game model based on bankruptcy theory is constructed for two load response modes. Then, a comprehensive bargaining weight calculation method is proposed for the negotiation of power resource allocation among various types of users by taking into account the actual demands of various users and economic factors. According to the load response game, a cost minimization power system scheduling model is derived by combining thermal power, wind power, photovoltaic power, and pumped storage. Four scenarios are investigated in the case study. The results show that the proposed model can improve fairness and satisfaction while reducing load response costs.

Hussein A. Kazem, Miqdam T. Chaichan, Ali H.A. Al-Waeli et al.

Integrating the photovoltaic/thermal (PV/T) system in green hydrogen production is an improvement in sustainable energy technologies. In PV/T systems, solar energy is converted into electricity and thermal energy simultaneously using hot water or air together with electricity. This dual use saves a significant amount of energy and officially fights greenhouse gases. Different cooling techniques have been proposed in the literature for improving the overall performance of the PV/T systems; employing different types of agents including nanofluids and phase change materials. Hydrogen is the lightest and most abundant element in the universe and has later turned into a flexible energy carrier for transportation and other industrial applications. Issues, including the processes of Hydrogen manufacturing, preservation as well as some risks act as barriers. This paper provides an analysis of several recent publications on the efficiency of using PV/T technology in the process of green hydrogen production and indicates the potential for its increased efficiency as compared to conventional systems that rely on fossil fuels. Due to the effective integration of solar energy, the PV/T system can play an important role in the reduction of the levelized cost of hydrogen (LCOH) and hence play an important part in reducing the economic calculations of the decarbonized energy system.

Phillip Ormeño Vásquez, Naysha Rojas Villa, Cristian Rojas Villa

Tourism and hospitality education in Peru is evolving to incorporate a stronger focus on environmental conservation. In this context, this study aims to assess environmental conservation attitudes of students enrolled in tourism and hospitality programs offered by Peruvian universities. A descriptive, cross-sectional study was carried out among students pursuing a degree in Tourism and Hospitality Management at the National University of Cañete, in Lima, Peru. The research focused on the period from July to December 2018. The final report was completed in 2020. A sample of 45 students was taken from a total population of 281, based on specific selection criteria. The demographic variables considered for this study were: age group, gender, and class shift, alongside the following dimensions: cognitive, affective and reactive. The majority of participants were female (73.3%), within the 20- to 22-year-old group (80.0%), and attended morning classes (64.4%). The highest level of agreement among students was observed for the statement that local hotels should implement appropriate waste management systems (item 2). Furthermore, 86.7% of respondents strongly agreed that plants and animals have the same right to life as human beings (item 20). Similarly, 60.0% of them indicated they would be willing to mobilize others in support of public space conservation (item 26). Overall, the findings suggest that students possess sound cognitive and affective attitudes towards environmental conservation. However, a lower reactive performance requires strategies to foster a deep emotional connection.

Aashish K Moses, Srinath Ranjan Tripathy, Saroj Sundar Baral

Abstract The existing energy-wastewater nexus may be resolved using metal oxide semiconductor photocatalysts in photocatalytic hydrogen production and pollutant degradation, which is a clean and sustainable process. SnO2 is one such well-researched and proven photocatalyst that is now in use, although it only works with ultraviolet light, which only makes up 4% of the total solar energy received. The present research aims to use iron as a dopant to make SnO2 active under visible light, enhancing reactions like water splitting and dye degradation. The sol-gel method was used to synthesize the photocatalysts. XRD, BET, UV diffuse reflectance spectra, PL spectra, XPS, and SEM micrographs were used to characterize the synthesized photocatalysts. For 7.5 wt% Fe-doped SnO2, a remarkable hydrogen generation rate of 18.81 µmol/hr under sunlight was achieved, nearly three times that of pure SnO2 (5.71 µmol/h). The nanocomposites display excellent photoreactivity towards RhB dye degradation with an optimal concentration of 7.5 wt% Fe-doped SnO2. This optimal composite photocatalyst removes 93% of RhB dye on 0.1 g/L photocatalysts in only 60 min under sunlight. Pristine SnO2 removes 36% of the dye under similar reaction conditions. The photoluminescence spectra of Fe-doped SnO2 had lower peak locations than the pristine SnO2, indicating a decreased rate of charge recombination and increased life duration of the active species. As a result, hydrogen generation rates and dye degradation efficiencies have increased significantly. The photocatalyst’s recyclability study revealed that the photocatalysts can be used efficiently for four cycles without significant reduction in the yield.

Mark A. Andor, Andreas Gerster, J. Peters et al.

The seminal studies by Allcott and Mullainathan (2010), Allcott (2011), and Allcott and Rogers (2014) show that social comparison-based home energy reports (HER) are a cost-effective climate policy intervention in the US. Our paper demonstrates the context-dependency of this result. In most industrialized countries, average electricity consumption and carbon intensity are well below US levels. Consequently, HER interventions can only become cost-effective if treatment effect sizes are substantially higher. For Germany, we provide evidence from a large-scale randomized controlled trial that effect sizes are in fact considerably lower than in the US. We conclude by illustrating that targeting highly responsive subgroups is crucial to reach cost-effectiveness and by identifying the few countries in which HER are promising policy instruments.

Damien R. Farine, Julia Penndorf, Sofia Bolcato et al.

Abstract Animal tracking has opened the door to address many fundamental questions in ecology and conservation. Whilst historically animals have been tracked as a means to understand their large‐scale movements, such as migration, there is now a greater focus on using tracking to study movements over smaller scales, individual variation in movement or how movements shape social network structure. With this shift in focus also comes different tracking needs, including the need to track larger numbers of individuals. Tracking studies all face some technological limitations. For example, GPS and other active tracking solutions can collect fine‐scale movement data, but have a high cost per tag, limiting the number of individuals that can be followed. They also have high low‐energy costs of data acquisition and download, limiting time periods over which data can be collected. Low‐energy passive (e.g. PIT) or active (e.g. reverse GPS) tags can overcome these limitations, but instead require animals to remain within a bounded study area or to come into close proximity to detectors. Here we describe one solution that can overcome many current limitations by employing the massive global network of personal mobile phones as gateways for tracking animals using Bluetooth low‐energy (BLE) beacons. In areas with medium to high density of people, these simple‐to‐make beacons can provide regular updates of position over long time periods (battery life 1–3 years). We describe how to use off‐the‐shelf components to produce BLE beacons that weigh c. 5–6 g and cost <$7USD. Using field‐testing, we then show that beacons are capable of producing high‐frequency tracking data that can be used to build home ranges or to detect spatiotemporal co‐occurrences among individuals. BLE beacons are a low cost, low‐energy solution for studying organisms (e.g. birds, mammals and reptiles) living and moving in urban landscapes. Their low weight and small size makes them particularly well‐suited for tracking smaller species. When combined with fixed gateways, their use can also be extended to non‐urban habitats. Their high accessibility is likely to make them an attractive solution for many research projects.

Juan Mena-Fernández, Dark Energy Survey Collaboration

Here, we present the angular diameter distance measurement obtained from the measurement of the Baryonic Acoustic Oscillation (BAO) feature using the completed Dark Energy Survey (DES) data, summarizing the main results of [Phys. Rev. D 110, 063514] and [Phys. Rev. D 110, 063515]. We use a galaxy sample optimized for BAO science in the redshift range 0.6 < z < 1.2, with an effective redshift of $z_{\rm eff}$ = 0.85. Our consensus measurement constrains the ratio of the angular distance to the sound horizon scale to $D_M(z_{\rm eff})/r_d$ = 19.51 $\pm$ 0.41. This measurement is found to be 2.13$σ$ below the angular BAO scale predicted by Planck. To date, it represents the most precise measurement from purely photometric data, and the most precise from any Stage-III experiment at such high redshift. The analysis was performed blinded to the BAO position and is shown to be robust against analysis choices, data removal, redshift calibrations and observational systematics.

Rajat Saxena, D. Rakshit, S. Kaushik

Abstract Sun is a perennial source of energy and utilization of this energy for building energy conservation can be a turning point in the energy saving potential of buildings. With Indian population crossing 1.3 billion, there is a need to utilize solar energy to deal with space and resource constraints. The buildings in urban areas are therefore exploring and integrating different passive techniques to conserve energy. One method of improving the energy efficiency aspects of a building would be to increase the thermal mass of the building elements by incorporating phase change materials (PCMs) within the building envelope thereby, reducing the temperature fluctuation and peak temperature within. This study discusses the competence of using PCMs for building energy conservation through their large heat storage capacities in the climatic conditions of Delhi. The aim is to experimentally study the impact of PCM incorporated bricks, assessing the change in temperature difference and heat flow across the same. Analytically for the climatic conditions of Delhi, thermally mapped Eicosane and OM35 are found suitable. These two PCMs are then experimentally characterised, to determine their melting-solidification characteristics, using differential scanning calorimeter (DSC). The results also reiterated the suitability of Eicosane and OM35. Further, the PCM incorporated bricks are tested under real conditions during peak summer (third week of May-2018). A temperature reduction of 5–6 ℃ across the bricks with respect to conventional brick was observed. Energy calculations showed reduction in heat flow by 8% and 12% for Eicosane and OM35 respectively.

Zainab Jan, Anupriya M. Geethakumari, Kabir H. Biswas et al.

Background: SARS-CoV-2 variants continue to spread throughout the world and cause waves of COVID-19 infections. It is important to find effective antiviral drugs to combat SARS-CoV-2 and its variants. The main protease (Mpro) of SARS-CoV-2 is a promising therapeutic target due to its crucial role in viral replication and its conservation in all the variants. Therefore, the aim of this work was to identify an effective inhibitor of Mpro. Methods: We studied around 200 antimicrobial peptides using in silico methods including molecular docking and allergenicity and toxicity prediction. One selected antiviral peptide was studied experimentally using a Bioluminescence Resonance Energy Transfer (BRET)-based Mpro biosensor, which reports Mpro activity through a decrease in energy transfer. Results: Molecular docking identified one natural antimicrobial peptide, Protegrin-2, with high binding affinity and stable interactions with Mpro allosteric residues. Furthermore, free energy calculations and molecular dynamics simulation illustrated a high affinity interaction between the two. We also determined the impact of the binding of Protegrin-2 to Mpro using a BRET-based assay, showing that it inhibits the proteolytic cleavage activity of Mpro. Conclusions: Our in silico and experimental studies identified Protegrin-2 as a potent inhibitor of Mpro that could be pursued further towards drug development against COVID-19 infection.

Jia-Yu Chen, Mai-Ying Duan, Fu-Hu Liu et al.

We study the transverse momentum ($p_T$) spectra of neutral pions and identified charged hadrons produced in proton--proton ($pp$), deuteron--gold ($d$--Au), and gold--gold (Au--Au) collisions at the center of mass energy $\sqrt{s_{NN}}=200$ GeV. The study is made in the framework of a multi-source thermal model used in the partonic level. It is assumed that the contribution to the $p_T$-value of any hadron comes from two or three partons with an isotropic distribution of the azimuthal angle. The contribution of each parton to the $p_T$-value of a given hadron is assumed to obey any one of the standard (Maxwell-Boltzmann, Fermi-Dirac, and Bose-Einstein) distributions with the kinetic freeze-out temperature and average transverse flow velocity. The $p_T$-spectra of the final-state hadrons can be fitted by the superposition of two or three components. The results obtained from our Monte Carlo method are used to fit the experimental results of the PHENIX and STAR Collaborations. The results of present work serve as a suitable reference baseline for other experiments and simulation studies.

Yijian Wang, Yang Cui, Yang Li et al.

Achieving the economical and stable operation of Multi-microgrids (MMG) systems is vital. However, there are still some challenging problems to be solved. Firstly, from the perspective of stable operation, it is necessary to minimize the energy fluctuation of the main grid. Secondly, the characteristics of energy conversion equipment need to be considered. Finally, privacy protection while reducing the operating cost of an MMG system is crucial. To address these challenges, a Data-driven strategy for MMG systems with Shared Energy Storage (SES) is proposed. The Mixed-Attention is applied to fit the conditions of the equipment, additionally, Multi-Agent Soft Actor-Critic(MA-SAC) and (Multi-Agent Win or Learn Fast Policy Hill-Climbing)MA-WoLF-PHC are proposed to solve the partially observable dynamic stochastic game problem. By testing the operation data of the MMG system in Northwest China, following conclusions are drawn: the R-Square (R2) values of results reach 0.999, indicating the neural network effectively models the nonlinear conditions. The proposed MMG system framework can reduce energy fluctuations in the main grid by 1746.5kW in 24 hours and achieve a cost reduction of 16.21% in the test. Finally, the superiority of the proposed algorithms is verified through their fast convergence speed and excellent optimization performance.

Xiaoling Chen, Cory Miller, Mithun Goutham et al.

In this paper, a real time implementable load coordination strategy is developed for the optimization of electric demands in a smart home. The strategy minimizes the electricity cost to the home owner, while limiting the disruptions associated with the deferring of flexible power loads. A multi-objective nonlinear mixed integer programming is formulated as a sequential model predictive control, which is then solved using genetic algorithm. The load shifting benefits obtained by deploying an advanced coordination strategy are compared against a baseline controller for various home characteristics, such as location, size and equipment. The simulation study shows that the deployment of the smart home energy management strategy achieves approximately 5% reduction in grid cost compared to a baseline strategy. This is achieved by deferring approximately 50\% of the flexible loads, which is possible due to the use of the stationary energy storage.

Lily Xu, Esther Rolf, Sara Beery et al.

In this white paper, we synthesize key points made during presentations and discussions from the AI-Assisted Decision Making for Conservation workshop, hosted by the Center for Research on Computation and Society at Harvard University on October 20-21, 2022. We identify key open research questions in resource allocation, planning, and interventions for biodiversity conservation, highlighting conservation challenges that not only require AI solutions, but also require novel methodological advances. In addition to providing a summary of the workshop talks and discussions, we hope this document serves as a call-to-action to orient the expansion of algorithmic decision-making approaches to prioritize real-world conservation challenges, through collaborative efforts of ecologists, conservation decision-makers, and AI researchers.

Karine Lacroix, R. Gifford

Verena Tiefenbeck, Anselma Wörner, Samuel Schöb et al.