• Energy Research
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We study the determinants of PV adoption in the region of Flanders (Belgium), where PV adoption reached high levels during 2006-2012, because of active government intervention. Based on a unique dataset at a very detailed spatial level, we estimate a Poisson model to explain the heterogeneity in adoption rates. We obtain the following findings. First, local policies have a robust and significant impact on PV adoption, providing indirect evidence that the larger regional incentives formed the basis for the strong development of PV adoption in the region. Second, there is a strong unconditional income effect, implying a Matthew effect in the subsidization of PVs. Our third finding is however that this income effect is largely driven by the fact that wealthier households are more likely to adopt because they tend to be larger (and hence higher users), are more frequent house owners (who capture more of the benefits), or own houses that are better suited for PV. We can thus identify the channels through which wealthier households are more likely to benefit from the PV support. Finally, we identify the importance of several housing characteristics: PV adoption tends to be more likely in larger and in more recently built houses. In several extensions, we consider the determinants of the average size of installed PVs, and the differential impact of certain variables over time.

The Flameless Combustion (FC) regime has been pointed out as a promising combustion technique to lower the emissions of nitrogen oxides (NOx) while maintaining low CO and soot emissions, as well as high efficiencies. However, its accurate modeling remains a challenge. The prediction of pollutant species, especially NOx, is affected by the usually low total values that require higher precision from computational tools, as well as the incorporation of relevant formation pathways within the overall reaction mechanism that are usually neglected. The present work explores a multiple step modeling approach to tackle these issues. Initially, a CFD solution with simplified chemistry is generated [both the Eddy Dissipation Model (EDM) as well as the Flamelet Generated Manifolds (FGM) approach are employed]. Subsequently, its computational cells are clustered to form ideal reactors by user-defined criteria, and the resulting Chemical Reactor Network (CRN) is subsequently solved with a detailed chemical reaction mechanism. The capabilities of the clustering and CRN solving computational tool (AGNES—Automatic Generation of Networks for Emission Simulation) are explored with a test case related to FC. The test case is non-premixed burner based on jet mixing and fueled with CH4 tested for various equivalence ratios. Results show that the prediction of CO emissions was improved significantly with respect to the CFD solution and are in good agreement with the experimental data. As for the NOx emissions, the CRN results were capable of reproducing the non-monotonic behavior with equivalence ratio, which the CFD simulations could not capture. However, the agreement between experimental values and those predicted by CRN for NOx is not fully satisfactory. The clustering criteria employed to generate the CRNs from the CFD solutions were shown to affect the results to a great extent, pointing to future opportunities in improving the multi-step procedure and its application.

This study is to our knowledge the first to describe the effect of a Gas-Gas Heater (GGH) of a coal fired power plant's has on (i) the H2SO4 concentration and (ii) the particle/aerosol number concentration and particle size distribution present in the flue gas. In the absence of a GGH, homogenous nucleation takes places inside the Wet Flue Gas Desulphurisation (WFGD) converting the gaseous H2SO4 into aerosol H2SO4. This leads to a high aerosol number concentration behind the WFGD with 80% of the aerosols being smaller than 0.02 μm. This implies that an amine based carbon capture (CC) installation treating this flue gas can suffer from amine mist formation due to the high amount of available nuclei (i.e., H2SO4 aerosols) resulting in high amine emissions. In contrast, in the presence of a GGH not only 70% of the H2SO4 is removed from the flue gas (measured at the Nijmegen powerplant), but also homogenous nucleation in the WFGD is prevented resulting in low particle number concentrations. The flue gas leaving the GGH will not create any mist formation issues in an amine based CC installation due to the low amount of nuclei present in the flue gas. It is not the reduction in H2SO4 concentration by 70% inside the GGH as such that prevents mist formation but absence of H2SO4 in its aerosol form. These results are most likely quite widely transformable to other power plants that burn low sulfur coal i.e., around 0.7 weight%. This information will serve future pilot and demo CC installation around the world; in particular when retrofitted on power plants that have a GGH.

Abstract This paper presents some research activities conducted at the Centre Spatial de Liege (CSL) in the field of space solar arrays and concentration. With the new generation of high efficiency solar cells, solar concentration brings new insights for future high power spacecrafts. A trade-off study is presented in this paper. Two different trough concentrators, and a linear Fresnel lens concentrator are compared to rigid arrays. Thermal and optical behaviors are included in the analysis. Several technical aspects are discussed: • Off-pointing with concentrators induces collection loss and illumination non uniformity, reducing the PV efficiency. • Concentrator deployment increases the mission risk. • Reflective trough concentrators are attractive and already proven. Coating is made of VDA (Aluminum). A comprehensive analysis of PV conversion increase with protected silver is presented. • Solar concentration increases the heat load on solar cells, while the conversion efficiency is significantly decreasing at warm temperatures. To conclude, this paper will point out the new trends and the key factors to be addressed for the next generation of solar generators.

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.

Abstract This paper investigates the impact of urbanization on energy consumption by applying the Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) in case of Malaysia. The study covers the time period of 1970Q1–2011Q4. The unit root test and the ARDL bounds testing approach have been applied to examine integrating properties and long run relationship in the presence of structural breaks. Our results validated the existence of cointegration and exposed that urbanization is a major contributor in energy consumption. Affluence raises energy demand. Capital stock boosts energy consumption. Trade openness leads to affluence and hence increases energy consumption. The causality analysis finds that urbanization Granger causes energy consumption. The feedback effect is found between energy consumption and affluence and, energy consumption and capital. The bidirectional causality exists between trade openness and energy consumption.