• Energy Research
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Abstract This paper presents a novel multi-time-stage input–output-based modeling framework for simulating the dynamics of bioenergy supply chains. One of the key assumptions used in the model is that the production level at the next time-stage of each segment of the energy supply chain adjusts to the output surplus or deficit relative to targets at the current time period. Furthermore, unlike conventional input–output models, the technology matrix in this approach need not be square, and thus can include coefficients denoting flows of environmental goods, such as natural resources or pollutants. Introducing a feedback control term enables the system to regulate the dynamics, thus extending the model further. This is an important feature since the uncontrolled dynamic model exhibits oscillatory or unstable behavior under some conditions; in principle, the control term allows such undesirable characteristics to be suppressed. Numerical simulations of a simple, two-sector case study are given to illustrate dynamic behavior under different scenarios. Although the case study uses only a hypothetical system, preliminary comparisons are made between the simulation results and some broad trends seen in real bioenergy systems. Finally, some of the main policy implications of the model are discussed based on the general dynamic characteristics seen in the case study. In particular, insights from control theory can be used to develop policy interventions to impart desirable dynamic characteristics to nascent or emerging biofuel supply chains. These interventions can be used to guide the growth of bioenergy supplies along final demand trajectories with minimal fluctuation and no instability.

Abstract In an effort to minimise electricity consumption and greenhouse gases emissions, the heating, ventilation and air-conditioning sector has focused its attention on developing alternative solutions to electrically-driven vapour-compression cooling. Liquid desiccant air-conditioning systems represent an energy-efficient and more environmentally friendly alternative technology for dehumidification and cooling, particularly in those cases with high latent loads to maintain indoor air quality and comfort conditions. This technology is considered particularly efficient in hot and humid climates. As a matter of fact, the choice of the desiccant solution influences the overall performance of the system. The current paper reviews the working principle of liquid desiccant systems, focusing on the thermodynamic properties of the desiccant solutions and describes an evaluation of the reference thermodynamic properties of different desiccant solutions to identify which thermodynamic, physical, transport property influences the liquid desiccant process and to what extent. The comparison of these thermodynamic properties for the commonly used desiccants is conducted to estimate which fluid could perform most favourably in the system. The economic factors and the effect of different applications and climatic conditions on the system performance are also described. The paper is intended to be the first step in the evaluation of alternative desiccant fluids able to overcome the problems related to the use of the common desiccant solutions, such as crystallization and corrosion to metals. Ionic liquids seem a promising alternative working fluid in liquid desiccant air-conditioning systems and their characteristics and cost are discussed.

Abstract Urban planners, local authorities, and energy policymakers often develop strategic sustainable energy plans for the urban building stock in order to minimize overall energy consumption and emissions. Planning at such scales could be informed by building stock modeling using existing building data and Geographic Information System-based mapping. However, implementing these processes involves several issues, namely, data availability, data inconsistency, data scalability, data integration, geocoding, and data privacy. This research addresses the aforementioned information challenges by proposing a generalized integrated methodology that implements bottom-up, data-driven, and spatial modeling approaches for multi-scale Geographic Information System mapping of building energy modeling. This study uses the Irish building stock to map building energy performance at multiple scales. The generalized data-driven methodology uses approximately 650,000 Irish Energy Performance Certificates buildings data to predict more than 2 million buildings’ energy performance. In this case, the approach delivers a prediction accuracy of 88% using deep learning algorithms. These prediction results are then used for spatial modeling at multiple scales from the individual building level to a national level. Furthermore, these maps are coupled with available spatial resources (social, economic, or environmental data) for energy planning, analysis, and support decision-making. The modeling results identify clusters of buildings that have a significant potential for energy savings within any specific region. Geographic Information System-based modeling aids stakeholders in identifying priority areas for implementing energy efficiency measures. Furthermore, the stakeholders could target local communities for retrofit campaigns, which would enhance the implementation of sustainable energy policy decisions.

Over the past three decades, Swedish energy policy has evolved in three major stages—oil reduction, phase-out of nuclear energy, renewable energy—each with a different focus. Since 1977, Swedish law has required municipalities to develop an energy plan that addresses the supply, distribution, and use of energy. Whether such plans have contributed to the development of local energy-systems has been a subject for debate. This paper is based on a study of 12 municipal energy-plans that attempted to control and develop local energy-systems in southern Sweden. The analysis examines how municipalities promote oil reduction, efficient energy use, and the use of renewable energy. The plans varied in planning processes, contents, and level of ambition. The results of the study show that the contents of the plans follow the national energy-policies with respect to reduction of oil use, improved energy efficiency, and increased use of renewable energy.

Efficient integration of solar energy into the electricity mix depends on a reliable anticipation of its intermittency. A promising approach to forecast the temporal variability of solar irradiance resulting from the cloud cover dynamics is based on the analysis of sequences of ground-taken sky images or satellite observations. Despite encouraging results, a recurrent limitation of existing deep learning approaches lies in the ubiquitous tendency of reacting to past observations rather than actively anticipating future events. This leads to a frequent temporal lag and limited ability to predict sudden events. To address this challenge, we introduce ECLIPSE, a spatio-temporal neural network architecture that models cloud motion from sky images to not only predict future irradiance levels and associated uncertainties, but also segmented images, which provide richer information on the local irradiance map. We show that ECLIPSE anticipates critical events and reduces temporal delay while generating visually realistic futures. The model characteristics and properties are investigated with an ablation study and a comparative study on the benefits and different ways to integrate auxiliary data into the modelling. The model predictions are also interpreted through an analysis of the principal spatio-temporal components learned during network training. Manuscript accepted for publication in Applied Energy

Abstract In the past, most studies considered solar as a drawback to road pavement, especially the asphalt pavement. Not only its radiation causes the asphalt aging, simultaneously with high temperature, asphalt pavement contributes to the urban heat island (UHI) effect. In recent, as the world becomes more supportive towards implementing sustainable energy as the alternatives to the scarcity of non-renewable resources, utilizing the abundant solar radiation as the main source of energy has gradually attracted attention from both industries and academia with its potentials, including solar application on roads. Nevertheless, more researches have focused on the potential energy collection with less consideration to simultaneously overcome the side effects of abundant solar radiation on its lifecycle. This study found the necessity to review a comprehensive correlation between solar radiation and asphalt pavement from both positive and negative effects. The negative effects included asphalt aging and its effects on surroundings i.e. UHI effect. Meanwhile, the positive effects included exhaust purification and solar energy conversion into other forms of energy that can be used by humans using pavement solar heat collection for winter snowmelt, thermoelectric technology and photoelectric technology to convert solar energy into electrical energy. The cases in various application scenarios were analysed and summarized and the existing problems in the current research were proposed in this paper. Also, the reflection on the transformation of road design concept from “avoiding harm” to “seeking profit” was included. Finally, a new concept of “solar-road harmonious symbiosis” for future road application was proposed based on a comprehensive review of previous published works. It is also the new suggestion to scholars on the treatment of solar diseases on roads to prolong its symbiosis functions.

Abstract Current conventional agricultural systems using intensive energy has to be re-vitalized by new integrated approaches relying on renewable energy resources, which can allow farmers to stop depending on fossil resources. The aim of the present study was to compare wheat production in dryland (low input) and irrigated (high input) systems in terms of energy ratio, energy efficiency, benefit/cost ratio and amount of renewable energy use. Data were collected from 50 irrigated and 50 dryland wheat growers by using a face-to-face questionnaire in 2009. The results showed that the total energy requirement under low input was 9354.2 MJ ha −1 , whereas under high input systems it was 45367.6 MJ ha −1 . Total energy input consumed in both dryland and irrigated systems could be classified as direct, indirect, renewable and non-renewable energies which average in two wheat production systems were 47%, 53%, 24% and 76%, respectively. Energy ratios of 3.38 in dryland and 1.44 in irrigated systems were achieved. The benefit–cost ratios were 2.56 in dryland and 1.97 in irrigated wheat production systems. Based on the results of the present study, dry-land farming can have a significant positive effect on energy-related factors especially in dry and semi-dry climates such as Iran.

Abstract The present work focuses on the numerical simulation of Moderate or Intense Low oxygen Dilution combustion condition, using the Partially-Stirred Reactor model for turbulence-chemistry interactions. The Partially-Stirred Reactor model assumes that reactions are confined in a specific region of the computational cell, whose mass fraction depends both on the mixing and the chemical time scales. Therefore, the appropriate choice of mixing and chemical time scales becomes crucial to ensure the accuracy of the numerical simulation prediction. Results show that the most appropriate choice for mixing time scale in Moderate or Intense Low oxygen Dilution combustion regime is to use a dynamic evaluation, in which the ratio between the variance of mixture fraction and its dissipation rate is adopted, rather than global estimations based on Kolmogorov or integral mixing scales. This is supported by the validation of the numerical results against experimental profiles of temperature and species mass fractions, available from measurements on the Adelaide Jet in Hot Co-flow burner. Different approaches for chemical time scale evaluation are also compared, using the species formation rates, the reaction rates and the eigenvalues of the formation rate Jacobian matrix. Different co-flow oxygen dilution levels and Reynolds numbers are considered in the validation work, to evaluate the applicability of Partially-Stirred Reactor approach over a wide range of operating conditions. Moreover, the influence of specifying uniform and non-uniform boundary conditions for the chemical scalars is assessed. The present work sheds light on the key mechanisms of turbulence-chemistry interactions in advanced combustion regimes. At the same time, it provides essential information to advance the predictive nature of computational tools used by scientists and engineers, to support the development of new technologies.