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Abstract The compressed air storage connects charging and discharging process and plays a significant role on performance of Adiabatic Compressed Air Energy Storage (A-CAES) system. In this paper, a thermodynamic model of A-CAES system was developed in Matlab Simulink software, and a dynamic compressed air storage model was applied in the simulation, revealing the influence of time-varying temperature and pressure of air on performance indicators, e.g., roundtrip efficiency and energy density. The analysis results can be used as an explanation of the contradicting conclusions on system efficiency from other articles, as well as a reference in the design and operation of an A-CAES plant. There exists an optimal after-throttle-valve pressure when applying energy density as objective function with constant expander inlet pressure. A relatively higher heat transfer coefficient between atmosphere and air in storage tank results in more stored air in charging process and more released air in discharging process, which are of great benefit for A-CAES system in terms of energy density. The dynamic performance characteristic of compressed air storage can affect design capacity of first heat exchanger of expansion train and moreover, reduce roundtrip efficiency and energy density of A-CAES system.

Abstract A two-dimensional (2D) model is developed to analyze the performance and efficiency of H 2 O/CO 2 co-electrolysis in tubular SOEC (solid oxide electrolysis cell). The model fully considers the fluid flow, heat/mass transfer and electrochemical/chemical reactions in the SOEC. The results show that RWGSR (reversed water-gas shift reaction) significantly promotes CO 2 conversion ratio. The effect of important operating parameters was comprehensively studied and optimal operating condition was determined. When the inlet gas flows in parallel flow mode with the velocity of 1 m s −1 , TSOEC with the H 2 O/CO 2 molar ratio of 1 at 700 °C at 1.4 V achieves the highest efficiency of 59.4% and the syngas conversion ratio of 43.8%. Lowering gas flow velocity decreases the syngas yield but promotes H 2 O/CO 2 convert ratio and efficiency. Finally, calculation found that counter flow is superior to parallel flow.

Abstract The thermochemical conversion of algae biomass into energy via direct combustion is an important and effective way but emits pollutants. To address the gas pollutant emissions and ash characteristics in this process, three species of algae biomass, namely, Enteromorpha (En), Sargassum (Sa) and Chlorella (Ch), were used to investigate the process behavior of real-time SO2/NOx emissions and ash formation at varied combustion temperatures. It was found that SO2/NOx emission peaks and concentrations highly depended on the combustion temperature in addition to algae species. The SO2 emission amount and conversion ratio generally increased with increasing sulfur content in the algae. The NOx emissions were not causally related to the nitrogen content in the algae biomass. The conversion ratio from N to NOx for each algae species was similar at 700–900 °C. In particular, it was relatively low for the algae En and Ch, which have relatively high N contents, implying that a large amount of N exists in the form of reductive intermediates. Moreover, the morphological and physicochemical properties of the ash were also found to be associated with the combustion temperature and algae species. The results may provide a positive reference for pollution assessment and control from algae biomass combustion.

Abstract Biofuel production is of particular interest to Taiwan in the face of energy insecurity and climate change. Since climate-induced impacts such as changes in regional temperature and precipitation will influence crop yields, this study accommodates the estimated yield changes and subsequently develops a two-stage stochastic programming with recourse model to investigate the economic and environmental effects of biofuel production. The results indicate that the utilization of sweet potato, along with its by-products, can result in the maximum amount of ethanol production. At higher levels of gasoline and greenhouse gas (GHG) prices, 441.2 million liters of ethanol can be produced, of which 70 million liters come from utilization of by-products. In general, economic factors such as gasoline and GHG prices have larger impacts on biofuel production than yield changes. However, although biofuel production is relative stable in the face of crop yield change, a shift in cultivars and land-use pattern is likely to occur. We show that the net emission reduction is relatively low compared to Taiwan's total emission and the aggregate value of emission reduction merits more investigation. These concerns, as well as resource reallocation and policy reformulation are also discussed in detail.

Abstract Using a panel data analysis of a newly developed sample of monthly data by state for January 2005 to December 2019, we estimate a series of error correction models for US residential electricity demand postulated to move with electricity price, natural gas price, income, and weather. Our key findings are as follows. First, the short-run own-price elasticity estimate is not statistically different from zero (p-value > 0.8). Second, the long-run own- and cross-price elasticity estimates are −0.054 (p-value = 0.000) and 0.019 (p-value = 0.000) under the double-log specification, smaller in size than the long-run own- and cross-price elasticity estimates of −0.120 (p-value = 0.000) and 0.069 (p-value = 0.000) under the linear demand specification. Third, price elasticity estimates have been shrinking in size over time. Fourth, erroneously ignoring the panel data's cross-sectional dependence tends to more than double the long-run price elasticity estimates. Fifth, mismatching the timing of price information's availability and consumption decision leads to anomalous price elasticity estimates. Finally, our new empirics' key takeaway of low price-responsiveness supports continuation of energy efficiency standards and demand-side management programs.

The intermittent property of a photovoltaic (PV) system requires supplementary energy such as the utility grid or batteries to meet load demand. However, when large scale PV systems are connected to the utility grid, they might affect the grid stability if the overall system is not properly designed. Hence, an accurate model for forecasting the PV system output would be useful in enhancing the system stability and reliability. The dynamic modelling of PV systems is thus crucial to the rapidly developing technologies and integrated sources in the smart grid application. This paper presents different approaches to model PV systems and identifies their pros and cons in modelling. The paper then explains the importance of a dynamic model, followed by the methodology in building up such a dynamic model. A three-vertex representation of a nearby building casting a shadow onto the PV array is also proposed as a novel approach in shadow analysis. The implementation of the dynamic model for PV systems was demonstrated in a case study in Hong Kong.