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On the basis of the endoreversible model of absorption refrigeration cycles, a new cycle model, which includes the heat leak from the heat sink to the cooled space and irreversibilities due to the internal dissipation of the working fluid besides the finite-rate heat transfer between the working fluid and the external heat reservoirs, is established and used to calculate the maximum coefficient of performance and cooling rate of the absorption refrigeration system for a given total heat-transfer area of the heat exchangers. The temperatures of the working fluid and the distribution of the heat-transfer areas of the heat exchangers are optimized for the two states of maximum coefficient of performance and maximum cooling rate. The behavior of the dimensionless specific cooling rate as a function of the coefficient of performance is presented, which may be used to describe the general performance characteristics of an irreversible absorption refrigerator. The practical operating regions of the cycle system are determined and the new bounds of the primary performance parameters are given.

Abstract Compressed air energy storage (CAES) systems are promising for the application of a standalone hybrid system. This study adopts a scenario-based bi-level programming method to design a standalone hybrid system that mainly contains wind turbines, diesel generators (DGs), and a CAES system. The demand response is considered as a deferrable load. The uncertainties on the wind power outputs and load demand are modeled using scenario generation and reduction techniques. The generated scenarios are used in a bi-level programming model for designing the hybrid energy system (HES). The model is composed of an outer planning layer and an inner operation layer. The outer layer optimizes the size of each component in the HES using a quantum particle swarm optimization (QPSO) method with the objective of minimizing daily total costs including daily investment costs and daily operating costs. On the other hand, the inner layer optimizes the operational strategies of the HES using a sequential quadratic programming method with the objective of minimizing the total operating costs, including the generation and emission costs of the DGs and the degradation cost of the CAES system. The well-established HES tool HOMER is used to validate the results obtained by the developed and adopted models. The results indicate that 1) the QPSO method performs better than the particle swarm optimization and genetic algorithm methods. 2) The results obtained by the scenario-based bi-level programming method have an average similarity of approximately 97%, which is very high compared to that of the results obtained by HOMER.

Differences in ash behavior during hydrothermal treatment were identified based on multivariate data analysis of literature information on 29 different feedstock. In addition, the solubility of individual elements was evaluated based on a smaller data set. As a result two different groups were distinguished based on char ash content and ash yield. Virgin terrestrial and aquatic biomass, such as different types of wood and algae, in addition to herbaceous and agricultural biomass, bark, brewer's spent grain, compost and faecal waste showed lower char ash content than municipal solid wastes, anaerobic digestion residues and municipal and industrial sludge. Lower char ash content also correlated with lower ash yield indicating differences in chemical composition and ash solubility. Further evaluation of available data showed that ash in industrial sludge mainly contained anthropogenic Al, Fe and P or Ca and Si with low solubility during hydrothermal treatment. Char from corn stover, miscanthus, switch grass, rice hulls, olive, artichoke and orange wastes and empty fruit bunch had generally higher contents of K, Mg, S and Si than industrial sludge although differences existed within the group. In the future information on ash behavior should be used for enhancing the fuel properties of char based on feedstock type and hydrothermal treatment conditions.

Abstract Solar radiation is a primary driver for many physical, chemical and biological processes on the earth’s surface, and complete and accurate solar radiation data at a specific region are quite indispensable to the solar energy related researches. This study, with Nanchang station, China, as a case study, aimed to calibrate existing models and develop new models for estimating missing global solar radiation data using commonly measured meteorological data and to propose a strategy for selecting the optimal models under different situations of available meteorological data. Using daily global radiation, sunshine hours, temperature, total precipitation and dew point data covering the years from 1994 to 2005, we calibrated or developed and evaluated seven existing models and two new models. Validation criteria included intercept, slope, coefficient of determination, mean bias error and root mean square error. The best result ( R 2 = 0.93) was derived from Chen model 2, which uses sunshine hours and temperature as predictors. The Bahel model, which only uses sunshine hours, was almost as good, explaining 92% of the solar radiation variance. Temperature based models (Bristow and Campbell, Allen, Hargreaves and Chen 1 models) provided less accurate results, of which the best one ( R 2 = 0.69) is the Bristow and Campbell model. The temperature based models were improved by adding other variables (daily mean total precipitation and mean dew point). Two such models could explain 77% (Wu model 1) and 80% (Wu model 2) of the solar radiation variance. We, thus, propose a strategy for selecting an optimal method for calculating missing daily values of global solar radiation: (1) when sunshine hour and temperature data are available, use Chen model 2; (2) when only sunshine hour data are available, use Bahel model; (3) when temperature, total precipitation and dew point data are available but not sunshine hours, use Wu model 2; (4) when only temperature and total precipitation are available, use Wu model 1; and (5) when only temperature data are available, use Bristow and Campbell model.

Abstract Promoting cleaner heating is one of the key pathways towards future energy transition. One promising solution is to implement large-scale heat pumps into the existing heat supply system. This paper aims to investigate whether the integration of large-scale heat pumps can be the first feasible step for cities towards clean heating transition. As opposed to the traditional way of conducting independent energy planning within a single city/region, this paper presents a novel approach of employing integrated planning for the multiple neighboring regions to explore the advantages of interregional energy collaboration. The Beijing-Tianjin-Hebei (BTH) urban agglomeration is taken as a case study, which is the most polluted region in China caused by the coal-based heating system. By implementing a series of simulations for the heating and power systems in the EnergyPLAN tool for six predesigned future scenarios which consider different planning strategies and analysis focuses, this paper analyzes to what extent heat pumps can help to achieve energy and environmental improvement for the whole BTH region by 2030 while ensuring economic feasibility. This is done by creating three independent models for respective Beijing, Tianjin, and Hebei, and one integrated model for the whole BTH region. The results suggest that when guaranteeing economic feasibility, the integration of large-scale heat pumps can potentially result in at least 9.5% energy saving and 9.28% reduced CO2 emission compared to the baseline. The integrated planning strategy can furthermore be more efficient in the urban agglomeration, which reduces 1.92% and 2.27% more energy and emissions in 2030 compared to the independent planning results. Based on cost analysis, the maximum economic feasible potential of heat pump penetration is identified as well. This paper can provide references for policymakers in the BTH region and the rest of Northern China, as well as present a principle of energy planning for cities on a general level.