Results 21 - 30 of 1916
Results 21 - 30 of 1916. Search took: 0.025 seconds
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[en] Highlights: • Novel solution for passive thermal management of BICPV with microfins, PCM, n-PCM. • Average temperature reduction with PCM, 9.6 °C (13.9%); 11.2 °C (16.2%) with n-PCM. • With microfins, PCM reduced temperature by 10.7 °C (15.9%); n-PCM by 12.5 °C (18.5%). • Individual effectiveness of n-PCM was the highest followed by PCM and microfins. • PV electricity output predicted to theoretically increase by 15.15 GW worldwide. - Abstract: Building-Integrated Concentrated Photovoltaics (BICPV) is based on Photovoltaic (PV) technology which experience a loss in their electrical efficiency with an increase in temperature that may also lead to their permanent degradation over time. With a global PV installed capacity of 303 GW, a nominal 10 °C decrease in their average temperature could theoretically lead to 15 GW increase in electricity production worldwide. Currently, there is a gap in the research knowledge concerning the effectiveness of the available passive thermal regulation techniques for BICPV, both individually and working in tandem. This paper presents a novel combined passive cooling solution for BICPV incorporating micro-fins, Phase Change Material (PCM) and Nanomaterial Enhanced PCM (n-PCM). This work was undertaken with the aim to assess the unreported to date benefits of introducing these solutions into BICPV systems and to quantify their individual as well as combined effectiveness. The thermal performance of an un-finned metallic plate was first compared to a micro-finned plate under naturally convective conditions and then compared with applied PCM and n-PCM. A designed and fabricated, scaled-down thermal system was attached to the electrical heaters to mimic the temperature profile of the BICPV. The results showed that the average temperature in the centre of the system was reduced by 10.7 °C using micro-fins with PCM and 12.5 °C using micro-fins with n-PCM as compared to using the micro-fins only. Similarly, the effect of using PCM and n-PCM with the un-finned surface demonstrated a temperature reduction of 9.6 °C and 11.2 °C respectively as compared to the case of natural convection. Further, the innovative 3-D printed PCM containment, with no joined or screwed parts, showed significant improvements in leakage control. The important thermophysical properties of the PCM and the n-PCM were analysed and compared using a Differential Scanning Calorimeter. This research can contribute to bridging the existing gaps in research and development of thermal regulation of BICPV and it is envisaged that the realised incremental improvement can be a potential solution to (a) their performance improvement and (b) longer life, thereby contributing to the environmental benefits.
[en] Highlights: • Three different technology learning approaches are introduced in the Global TIMES model. • Technology diffusion can effectively reduce global mitigation costs and welfare losses. • Developed countries should take the lead in low-carbon technologies’ deployment. • Technology cooperation will improve mitigation capability of developing countries. - Abstract: Low-carbon power generation technologies such as wind, solar and carbon capture and storage are expected to play major roles in a decarbonized world. However, currently high cost may weaken the competitiveness of these technologies. One important cost reduction mechanism is the “learning by doing”, through which cumulative deployment results in technology costs decline. In this paper, a 14-region global energy system model (Global TIMES model) is applied to assess the impacts of technology diffusion on power generation portfolio and CO2 emission paths out to the year 2050. This analysis introduces three different technology learning approaches, namely standard endogenous learning, multiregional learning and multi-cluster learning. Four types of low-carbon power generation technologies (wind, solar, coal-fired and gas-fired CCS) undergo endogenous technology learning. The modelling results show that: (1) technology diffusion can effectively reduce the long-term abatement costs and the welfare losses caused by carbon emission mitigation; (2) from the perspective of global optimization, developed countries should take the lead in low-carbon technologies’ deployment; and (3) the establishment of an effective mechanism for technology diffusion across boundaries can enhance the capability and willingness of developing countries to cut down their CO2 emission.
[en] Highlights: • The overheating rate reduction using PCM solutions. • Thermal dynamic simulation of a department building: comfort and energy analyses. • Building features optimization, aiming the energy reduction during the building use. • Economic analysis to estimate the payback period for the PCM constructive solutions. - Abstract: Sustainable energy and thermal retrofit design of buildings or districts has a strong global impact in the viewpoint of economies and energy-efficiency perspectives. Several aspects such as architectonic design, building materials, construction technology, mechanical systems and outdoor climate determines the thermal behaviour of buildings and their ability to provide indoor thermal comfort to occupants. The use of geothermal energy and phase change materials (PCMs) in the construction systems are an opportunity that may attenuate indoor air temperature fluctuation as well as overheating risk. This paper presents the results of a study on indoor thermal comfort and energy efficiency regarding the PCM’s positive role when applied to new constructive solutions, inside a building with a geothermal system linked to the air conditioning system. The PCM study was based on real and simulated investigations in two rooms of a new university department at the Aveiro campus. Higrothermal monitoring (indoor air temperature) of two rooms in which one of them has PCM panels incorporated into gypsum board partition wall and into a suspended ceiling. The scope was driven to investigate the potential of these solutions for overheating mitigation. The numerical study was conducted by using an evolutionary algorithm coupled with the software EnergyPlus® used in simulations. In the scope of this optimization process, constructive solutions with the incorporation of different types of PCM with different melting temperatures and enthalpy, and different flow rates of natural ventilation were combined to investigate the potential and the payback time of these novel solutions. The results for the room measurements show that the indoor thermal comfort of the rooms, present long periods of discomfort namely in overheating. However, it was proved that the PCM application in one of the rooms lead to an overheating reduction of 7.23% representing a PCM efficiency of 35.49%. After the optimization process an overheating reduction of about 34% was attained by the use of PCM in one of the rooms. Regarding the economic analysis of the use of the PCM for cooling demand reduction, a payback time of 18 years was attained.
[en] Highlights: • This study reports a new concept, ‘random walk’ occupants’ presence. • According to building types, the characteristics of occupants’ presences can differ. • Different occupant models can cause significant performance gaps in energy prediction. - Abstract: Occupant behavior is regarded as one of the major factors contributing to the discrepancy between simulation prediction and real energy use. Over the past several decades, occupants have been represented as fixed profiles of occupant presence in building energy simulation tools. Recently, stochastic models have been introduced to account for dynamic occupant presence. This stochastic approach is based on the premise that occupant presence can be described by empirical and probabilistic transition rules, e.g. Markov Chain. This paper presents evidence that occupant presence in some rooms and buildings follows a “random walk” pattern. In other words, occupant presence in certain types of buildings cannot be predicted stochastically. In this study, occupants’ presence in two laboratories and three reading rooms at two universities was monitored. The hypothesis of the random walk pattern was tested using the Normalized Cumulative Periodogram (NCP) method. Based on a series of six experiments, it is shown that each occupant’s presence in the five locations follows a random walk pattern. Three different occupant models (fixed ASHRAE model, Markov Chain model, and Random Walk model) were applied in EnergyPlus simulation runs. The adjusted R2 for three experiments between the fixed AHSRAE model and the random walk model, and between the Markov chain model and the random walk model are 0.54, 0.02, 0.01 and 0.86, 0.19, 0.41, respectively. This does not negate the need for the fixed ASHRAE model or the MC model. Rather, this signifies that, for a certain type of building, another occupant presence model should be introduced, e.g. the RW Model.
[en] Highlights: • A novel ensemble forecasting framework for PV integrated bioclimatic buildings. • Five different predictors along with their wavelet transformed are combined. • Bayesian model averaging technique is used to aggregate the multiple predictors. • Forecast framework is analyzed for multiple forecast horizons and buildings. • Significant error reduction in different test case studies using the framework. - Abstract: Buildings are one of the major sources of electricity and greenhouse gas emission (GHG) in urban areas all around the world. Since a large integration of solar energy is observed in the form of rooftop photovoltaic (PV) units, electricity use of buildings is highly uncertain due to intermittent nature of solar output power. This leads to poor energy management for both network operators and building owners. In addition, uncertain metrological conditions, diversity and complexity of buildings are big hurdles to accurate prediction of the demand. To improve accuracy of load demand forecast of PV integrated smart building, a hybrid ensemble framework is proposed in this paper. This is based on a combination of five different predictors named as backpropagation neural network (BPNN), Elman neural network (EN), Autoregressive Integrated Moving Average (ARIMA), feed forward neural network (FNN), radial basis function (RBF) and their wavelet transform (WT) models. WT is applied to historical load data to remove the spikes and fluctuations. FNN and RBF network were trained with particle swarm optimization (PSO) for higher forecast accuracy. The output of each predictor in the ensemble network is combined using Bayesian model averaging (BMA). The proposed framework is tested using real data of two practical PV integrated smart buildings in a big university environment. The results indicate that the proposed framework show improvement in average forecast normalized root mean square error (nRMSE) around 17% and 20% in seasonal daily and seasonal weekly case studies, respectively. In addition, proposed framework also produces lowest of nRMSE about 3.88% in seasonal monthly forecast of smart buildings with rooftop PV as compared to benchmark model. The proposed forecast framework provides consistent forecast results for global change institute (GCI) and advance engineering building (AEB) during seasonal daily and weekly comparison.
[en] Highlights: • A novel approach for the efficient management of wastewater pumps is presented. • This approach couples fuzzy logic and data-mining to reduce the pump energy consumption. • This approach enables to monitor the pumps and provide case-specific suggestions. • Short-term and long-term phenomena were identified and separately monitored. • Flow-related issues and early-stage failures were detected. - Abstract: Studies and publications from the past ten years demonstrate that generally the energy efficiency of Waste Water Treatment Plants (WWTPs) is unsatisfactory. In this domain, efficient pump energy management can generate economic and environmental benefits. Although the availability of on-line sensors can provide high-frequency information about pump systems, at best, energy assessment is carried out a few times a year using aggregated data. Consequently, pump inefficiencies are normally detected late and the comprehension of pump system dynamics is often not satisfactory. In this paper, a data-driven methodology to support the daily energy decision-making is presented. This innovative approach, based on fuzzy logic, supports plant managers with detailed information about pump performance, and provides case-based suggestions to reduce the pump system energy consumption and extend pump life spans. A case study, performed on a WWTP in Germany, shows that it is possible to identify energy inefficiencies and case-based solutions to reduce the pump energy consumption by 18.5%.
[en] Highlights: • A blockchain-enabled system is proposed for emissions trading application. • The objective is to improve management and increase abatement investment. • Financial incentive is used to motivate industry participants. • Multi-criteria analysis emphasizes the benefit of the system against established ETS. - Abstract: Emission Trading Scheme (ETS) has dual aims to reduce emission production and stimulate adoption of long-term abatement technology. Whilst it has generally achieved its first aim, its issues are hindering the accomplishment of the second. Several solutions have been proposed to improve ETS’s efficacy, yet none of them have considered the advancement of Industry 4.0. This paper proposes a novel ETS model customised for Industry 4.0 integration. It incorporates blockchain technology to address ETS’s management and fraud issues whilst it utilizes a reputation system in a new approach to improve ETS efficacy. Specific design of how the blockchain technology and reputation system are used to achieve these objectives is showed within this paper. The case study demonstrates the inner working of reputation-based trading system—in which reputation signifies participants performance and commitment toward emission reduction effort. Multi-criteria analysis is used to evaluate the proposed scheme against conventional ETS model. The result shows that the proposed model is a feasible scheme and that the benefits of its implementation will outweigh its drawback.
[en] Highlights: • Modelling of hydrodynamics, gas–liquid mass transfer and biological reactions in a continuously stirred tank reactor. • General model framework can be utilized for different reactor designs and biocatalysts. • High gas–liquid mass transfer rate is the most critical parameter for high output gas quality. • Scale-up study predicts stirring power to be 0.7–1.1% of the electrolyser power in order to reach over 98% CH4 gas output. • Dynamic simulations show fast response to inflow transients. - Abstract: Power-to-gas technology can facilitate the transition toward a renewables-based energy system by converting excess electricity to hydrogen and then into methane via methanation. Unlike traditional chemical methanation, biological methanation uses an aqueous solution of biomass (archaea), which consumes H2 and CO2 to produce CH4. The process is limited primarily by the gas–liquid mass transfer step. In addition to experimental research, modeling is often used to guide and expedite the development and scale-up of bioreactors from the laboratory to the pilot and commercial scales. Modeling has been used to optimize and test various operation conditions outside the range of experimentation. Estimations of gas–liquid mass transfer and the related stirring power are important for optimization and feasibility studies in the application of biological methanation to power-to-gas systems. Related published literature, however, is limited. In this study, a dynamic model for a continuously stirred biomethanation reactor was developed with novel approach that combines semi-fundamental modeling of gas–liquid mass transfer, hydrodynamics, and biological reactions. The model was validated against existing experimental data and used in a sensitivity analysis of critical parameters, a scale-up study of a biomethanation reactor, and process dynamics studies. In each of the varying operational conditions, the model reproduced the trends observed in the experimental studies. The sensitivity analysis showed that biological parameters have a minimal effect on methane production. Conversely, the model is very sensitive to the gas–liquid mass transfer properties, such as the geometry of the impeller and reactor. The scaled-up study of biomethanation reactors with a CH4 production capacity of 56–508 Nm3/h revealed that the required stirring power is 0.7–1.1% from the electrolyzer power and decreases as the size of the reactor increases. High output quality (∼98%) of the methane could be reached in each of the studied cases, and the overall efficiency of the power-to-methane process was roughly 50%. Dynamic simulations showed that the modeled process is tolerant to large gradients in the input parameters. After correctly setting the reactor- and process-specific parameters, the model can be used to perform scaled-up and dynamic studies of various reactor designs and different biomass solutions.
[en] Highlights: • A gasification model was developed to predict the production of syngas and biochar. • Economic value of syngas and biochar production was evaluated based on the model. • The heat and mass transfer in the reactor was modelled by a three-region approach. • The effects of various factors on syngas and biochar production were studied. - Abstract: Syngas and biochar are two main products from biomass gasification. To facilitate the optimization of the energy efficiency and economic viability of gasification systems, a comprehensive fixed-bed gasification model has been developed to predict the product rate and quality of both biochar and syngas. A coupled transient representative particle and fix-bed model was developed to describe the entire fixed-bed in the flow direction of primary air. A three-region approach has been incorporated into the model, which divided the reactor into three regions in terms of different fluid velocity profiles, i.e. natural convection region, mixed convection region, and forced convection region, respectively. The model could provide accurate predictions against experimental data with a deviation generally smaller than 10%. The model is applicable for efficient analysis of fixed-bed biomass gasification under variable operating conditions, such as equivalence ratio, moisture content of feedstock, and air inlet location. The optimal equivalence ratio was found to be 0.25 for maximizing the economic benefits of the gasification process.
[en] Highlights: • We used a choice experiment to analyze the effect of PCT on BEV adopting decision. • We compared the effect of PCT with BEV performances and other policy incentives. • Except for government subsidy, the PCT was more powerful than other policies. • The PCT was less effective than the improvement of some BEV performances. - Abstract: The implementation of personal carbon trading (PCT) to influence transport choices has recently been suggested as a method to reduce private carbon emissions. In this study, we conducted a choice experiment in Jiangsu, China, to evaluate if PCT influences individual decisions to adopt battery electric vehicles (BEVs). The results showed that PCT can effectively change the decision to adopt and encourage the adoption of BEVs. PCT was shown to be more effective than free parking as well as eliminating road tolls, vehicle and vessel tax, and purchase tax, but less effective than government subsidies. In addition, we found that improving some BEV performance attributes was preferred to policy incentives, including PCT. These results improve our understanding of the effectiveness of PCT and the individual decision to adopt BEVs. Our findings could facilitate the practical implementation of PCT and provide suitable guidelines for developing BEV promotion strategies.