The main objective of the current work is to assess the hybridization of a solar power towerâ€™s with wind turbines and the potential of this integration to compensate the energy losses caused by aerosols attenuation of the reflected irradiance of the solar field. The combined solar power tower and wind turbines configurations are assessed over the range of 60-100 MW. A maximum reduction of 6.8 % in the annual energy generation is found in the standalone solar power tower when the aerosols are adopted. The integration of wind turbines has a limited effect in the compensation of the energy loss due to the aerosols effect on the solar field, however, it has a major role in the decrease of the LCOE.
This study applies a distributed energy resources scheme on industrial microgrids and provides a case study that is based on the Component-oriented Modeling and Optimization for Nonlinear Design and Operation (COMANDO). The model comprises distributed energy resources commonly used by industrial enterprises (i.e., solar power, combined-heat-and-power, heat pumps), energy storage systems, and management strategies (peer-to-peer trading, bulk purchasing). To demonstrate the model, a case study is conducted for a real-world industrial area in Germany. We find that the economic impact of the various strategies is highly dependent on the specific demand curves. However, combining the DER and the stated management strategies is always profitable and leads to reductions of a global warming index used as an ecological indicator.
In this contribution, monetary benefits that result from demand side management (DSM) integration in biogas digesters are analyzed. A model-based study to describe the influence of an electricity price-adjusted agitation (EPAA) control on biogas production is presented. Three price limits were calculated, which decide on the operation of four different predefined EPAA intervals. Results show that especially at very high and very low electricity prices, DSM strategies in biogas plants can lead to an increase in profit of the plant.
Decarbonising the steel industry is among the ultimate climate challenges. This study analyzes how the cost optimal mix of technologies to meet the demand of a green-steel manufacturing process using direct reduction with hydrogen and electric arc furnace (HDR-EAF) changes under progressively lower CO2 emission limits. The modellization was done thanks to the PyPSA framework and includes renewable generators, different hydrogen production techniques and burners technologies, as well as a carbon capture system. It is shown that a big reduction in CO2 emission is possible with a little increase in CO2 price and a totally green production of steel can be achieved.
Evaluating convective heat transfer performance in packed beds arising in various engineering problems is a complex issue due to the different parameters involved in the media, such as the bed materials, heat transfer mechanisms, packing structure and heat source. Apart from a number of assumptions made in various correlations developed for determining heat transfer performance in packed beds, literature survey also reveals that more experimental researches were conducted with fluid flowing in and out of the medium than heated fluid confined in an enclosed medium. Noted also is that most of the experimental research found in literature were conducted under forced convection compared with the investigation in the present study conducted under natural convection. In a quest to investigate the particle-to-fluid heat transfer characteristics expected in the proposed new fuel design, a basic unit cell (BUC) model is being developed for the theoretical analysis and applied to determine the heat transfer coefficient, h, of the medium. The model adopted a concept in which a single unit of the packed bed was analyzed and taken as representative of the entire bed; it related the convective heat transfer effect of the flowing fluid with the conduction and radiative effect at the finite contact spot between adjacent unit cell particles. As a result, the model could account for the thermophysical properties of sphere particles and the heated gas, the interstitial gas effect, gas temperature, contact interface between particles, particle size and particle temperature distribution in the investigated medium. Although the heat transfer phenomenon experienced in the experimental set-up was a reverse case of the proposed fuel design, the study with the achievement in the validation with the Gunn correlation aided in developing the appropriate theoretical relations required for evaluating the heat transfer characteristics in the proposed nuclear fuel design.
Obtaining an accurate mapping relationship between lithium-ion battery open-circuit voltage (OCV) with the state of charge (SOC) at different ambient temperatures is the basis for its accurate SOC estimation in the whole ambient temperature range. However, the experimental test of the OCV-SOC correspondence takes a lot of time; and it is obviously impossible to perform the test at all temperatures. To achieve accurate SOC estimation at different ambient temperatures with a lower experimental cost, a model-based SOC estimation method is proposed in this paper. First, based on generalized regression neural network (GRNN), an OCV-SOC mapping model for the whole ambient temperature range is established. Second, a new diagonalization of matrix adaptive cubature Kalman filter (DMACKF) is proposed, which enhances the filtering stability and realizes the adaptive update of noises in the recursive process. Finally, combined with the forgetting factor recursive least squares (FFRLS) algorithm, the proposed SOC estimation method is verified under the DST conditions at three temperatures. The root mean square errors (RMSEs) of SOC estimation results are within 0.4% at each temperature.
Minimizing the lifecycle environmental impact of buildings is urgently needed to achieve carbon neutrality in the coming decades. Low carbon buildings can only be achieved by optimizing the performance of buildings throughout all lifecycle phases. Currently, conventional methods are mostly used to reduce the operational impacts of buildings whereas they may limit the likelihood of enhancing the embodied performance. To improve whole lifecycle performance, enhanced methods such as the life cycle assessment (LCA) and life cycle costing (LCC) need to be coupled to allow for building performance analyses across different stages. Considering the complexities of these assessments, they are often not sufficiently integrated into whole building modelling processes. To account for both embodied and operational impacts of buildings, this study proposes a robust parametric BIM-based lifecycle optimization method to achieve building designs with least environmental and economic costs. LCA and LCC are optimized with a non-dominated sorting genetic algorithm II (NSGA-II) and applied to a case study building. The results show that the optimal design of the case building can reduce the CED, GWP and cost by 35%, 42% and 26% respectively. This integrated approach provides a robust and effective solution to optimize the whole lifecycle performance of buildings towards carbon neutrality.
In this paper, the supercritical (SC) fuel combustion is investigated on its performance in a diesel engine cylinder to improve output power and reduce emissions. The computational fluid dynamics (CFD) model is developed to comparatively study the spray combustion and the SC fuel combustion in a cylinder during constant volume combustion period. Results indicate that the engine in-cylinder pressure and output power can be increased by 6.8% and no less than 2.5% respectively. Moreover, the fuel concentration and temperature field of the SC combustion are more evenly distributed, which enables more sufficient combustion and indicates the potential to reduce pollutants such as NOx and soot.
Biomass char conversion is substantially influenced by metals contained in the material. One of the main catalytically active metals in biomass is Fe which occurs in various mineral forms. For an implementation of catalytic effects into char conversion models, investigations on mineral type and loading are required. In this work, the catalytic effect of an Fe loading series on the oxidation of an inherently mineral-free char was analysed. Characterisations focused on the Fe phase present in the char identifying its transition from FeSO4 to Î³-Fe2O3 during doping, and further to Îµ-Fe2O3 and Î±-Fe2O3 upon char oxidation. A very high loading-dependent activity of Îµ-Fe2O3 was found.
In order to improve CO2 flux of ceramic membrane in membrane absorption, the micro Al2O3 ceramic membrane is modified from hydrophilic to hydrophobic, and its surface morphology, wettability and water flux are characterized. The CO2 capture performance of CM before and after modification is studied experimentally. The surface morphology of ceramic membrane has no obvious difference before and after modification. After modification, contact angle increased from 47Â° to 130.9Â° with critical breakthrough pressure 1 bar, and its pure water flux decreased significantly. The mass transfer rate of CO2 at interface of ceramic membrane increases with increase of both absorbent flow rate and flue gas flow rate, and the mass transfer rate of modified ceramic membrane is always higher than that of unmodified ceramic membrane, which means a better CO2 capture performance. We believe this study will provide technical references for industrial CO2 capture applications.