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Residential energy consumption in China increased dramatically over the period of 2002-2010. In this paper, we undertake a decomposition analysis of changes in energy use by Chinese households for five energy-using activities: space heating/cooling, cooking, lighting and electric appliances. We investigate to what extent changes in energy use are due to changes from appliances and to change in floor space, population and energy mix. Our decomposition analysis is based on the logarithmic mean Divisia index technique using data from the China statistical yearbook and China energy statistical yearbook in the period of 2002-2010. According to our results, the increase in energy-using appliances is the biggest contributor to the increase of residential energy consumption during 2002-2010 but the effect declines over time, due to energy efficiency improvements in those appliances. The second most important contributor is floor space per capita, which increased with 28%. Of the four factors, population is the most stable factor and energy mix is the least important factor. We predicted electricity use, with the help of regression-based predictions for ownership of appliances and the energy efficiency of appliances. We found that electricity use will continue to rise despite a gradual saturation of demand

Electrolysis occupies a dominant position in the long-term application of hydrogen energy, as it can use the power surplus directly from renewable energies to produce hydrogen. Alkaline water electrolysis (AWE) is a mature and reliable technology standing out from other types of electrolysis because of its simplicity and low cost. Several multiphysics processes inside the AWE cell, such as the electrochemical, thermal, and fluidic processes. Developing the multiphysics model to quantify the relationship between these physics fields is essential for cell design. This paper establishes a three-dimensional numerical model to consider the quantitative relationship between the electrochemical process and fluidic process inside the cell of industrial AWE. The model considers the structural design of industrial AWE equipment, revealing that the shunting current effect introduced by the structure design cannot be ignored in the model. The simulation results present that the multiphysics model considering the bubble effect can estimate the current–voltage (I-V) characteristic curve more accurately with a relative error smaller than 5%, especially at a current density higher than 2500 A/m2. The model established is supposed to advance the development of water electrolysis models and guide the electrolyzer design of industrial AWE cell.

Abstract Finding cost effective retrofits for heat exchanger networks remains a challenge. Whilst it is often straightforward to find retrofit changes to an existing network that can improve energy performance, in practice such changes are most often uneconomic. This paper will present an approach to heat exchanger network retrofit around a fixed network structure. Network energy performance is improved through the selective use of heat transfer enhancement. A sensitivity analysis is used to find the most effective heat exchangers to enhance in order to improve the performance of the overall network. The sensitivity analysis used is an extension of a previous sensitivity analysis that was introduced to study network flexibility. The proposed method is applicable for heat exchanger networks involving streams with linear or non-linear physical properties. The enhancement of the most sensitive heat exchangers and avoiding new equipment, together with piping and civil engineering costs, allow much more cost-effective heat exchanger network retrofit.

A new cyclic model of a class of chemical engines is set up, in which not only finite-rate mass transfer and mass leakage but also the internal irreversibility resulting from friction, eddy currents and other irreversible effects inside the cyclic working fluid are taken into account. The influences of these irreversibilities on the performance of the cycle are revealed. The optimal relation between the power output and the efficiency of the cycle is derived. On the basis of the optimal relation, some optimal performances and important performance bounds of the cycle are determined and evaluated. For example, the maximum power-output and the corresponding efficiency, the maximum efficiency and the corresponding power output, the optimal mass-transfer time, the minimum rate of energy loss and so on are calculated and analyzed. The results obtained here cannot only enrich the application of thermodynamic theory but also provide some theoretical guidance for the effective application of energy resources and for the optimal design and development of a class of chemical engines. Moreover, some important conclusions relative to the isothermal endoreversible chemical engines, which have been investigated previously, can be directly deduced from the results in this paper.

Global warming, water scarcity and limited land resources are the most challenging problems facing agriculture to ensure food security for the expected 9 billion people in 2050. To solve these problems, the classical optimal planting pattern, based on crop suitability evaluation method, is often adopted to reallocate water and land resources. However, whether or not the classical optimal planting pattern, which only considers environmental conditions in crop suitability evaluation, is beneficial to the regional carbon neutrality goal and saves water and energy resources has rarely been explored. Here, China's major arid food production area, the middle reaches of Heihe River Basin, is chosen as the demonstration to explore this issue. The classical optimal planting pattern obtained by crop suitability evaluation is compared with current planting in terms of planting distribution, carbon sequestration, energy consumption and water productivity from 2002 to 2016. Interestingly, the results indicate that optimal planting would reduce the regional net carbon sequestration capacity by up to 13.09% and increase regional carbon emissions by up to 22%, which is harmful to reach the commitment of carbon neutrality goal in China. Contrary, optimal crop planting pattern can increase regional water productivity by 1.74–32.59% and economic benefits by 1.52–30.55% while having little impact on energy consumption and water consumption. Considering the contradictions effects of classical optimal planting pattern on the food-energy-water-carbon nexus, we strongly recommend redefining the “optimal” in crop planting management by taking impacts on carbon into consideration to alleviate the crisis of global warming.

This paper proposes a technique for the probabilistic wind power forecasting (WPF) of a newly built wind farm (NWF) using a limited amount of historical data. First, the state-of-the-art Transformer network is employed to capture the power generation pattern of different wind farms (WFs) based on abundant historical training samples. Then, the Bayesian averaging regression method is applied to transfer the learned power generation pattern to the NWF by assigning proper weights to the WPF results of different WFs. This enables the proposed method to yield accurate NWF power predictions utilizing a limited amount of historical data. The Bayesian characteristics further enable the quantification of multiple uncertainties in forecasting results that may be essential for the NWF operator when the input is uncertain. Comprehensive tests were also performed by employing other deterministic and probabilistic WPF methods using field data. By comparing the results, the proposed method is demonstrated to produce accurate forecasting results with sparse historical data. Moreover, the uncertainties of outcomes are quantified, and acceptable performance is achieved.

Due to the intermittency and uncertainty natures of wind power, electrical energy storages (EESs) are often equipped in the power systems to reduce the side-effect of wind power fluctuations, and adiabatic compressed air energy storage (A-CAES) is one of EES technologies to smooth the power fluctuation of wind farms (WFs). This paper proposes a coordinated control framework of WF and A-CAES station to achieve frequency response, and discusses the active power distribution scheme among wind turbines (WTs) and A-CAES units during frequency regulation. Firstly, the models of WT and A-CAES used in frequency regulation are presented. Then, considering that the power distribution might go through a long iteration process when the number of WTs in WF is quite large, these WTs are clustered into several groups using a comprehensive multi-view grouping indicator. On the basis of the WTs grouping result and with a defined generalized energy increment (GEI), this paper proposes a discrete consensus based tri-level coordinated frequency control method, which divides the control into three levels, i.e., group level, wind farm level and coordinated level. Through the three levels’ control, the method can reasonably and rapidly distribute the frequency regulation powers among WTs and A-CAES units without being limited by the scale of WF, and the coordination of WF and A-CAES station during frequency regulation is achieved. To demonstrate the effectiveness of the proposed method, a modified WF in Inner Mongolia of China is utilized for case study. Simulation results show that the proposed method is valid in various frequency events and can reach consensus within 4 s in the studied cases, and it is well-performed with different capacities of wind powers and A-CAESs in the power systems. The common communication failures have few influences on the methods, and the frequency nadirs fluctuate lower than 0.1 Hz with time delays in the communications. Compared with centralized and multi-machine equivalent methods, the proposed distributed method can balance the computational speed and the solution accuracy, and thus is beneficial to improve the system frequency nadirs when frequency drops.

The acceptability, energy consumption, and environmental benefits of electric vehicles are highly dependent on travel patterns. With increasing ride-hailing popularity in mega-cities, urban mobility patterns are greatly changing; therefore, an investigation of the extent to which electric vehicles would satisfy the needs of ride-hailing drivers becomes important to support sustainable urban growth. A first step in this direction is reported here. GPS-trajectories of 144,867 drivers over 104 million km in Beijing were used to quantify the potential acceptability, energy consumption, and costs of ride-hailing electric vehicle fleets. Average daily travel distance and travel time for ride-hailing drivers was determined to be 129.4 km and 5.7 h; these values are substantially larger than those for household drivers (40.0 km and 1.5 h). Assuming slow level-1 (1.8 KW) or moderate level-2 (7.2 KW) charging is available at all home parking locations, battery electric vehicles with 200 km all electric range (BEV200) could be used by up to 47% or 78% of ride-hailing drivers and electrify up to 20% or 55% of total distance driven by the ride-hailing fleet. With level-2 charging available at home, work, and public parking, the acceptance ceiling increases to up to 91% of drivers and 80% of distance. Our study suggests that long range BEVs and widespread level-2 charging infrastructure are needed for large-scale electrification of ride-hailing mobility in Beijing. The marginal benefits of increased all electric range, effects on charging infrastructure distribution, and payback times are also presented and discussed. Given the observed heterogeneity of ride-hailing vehicle travel, our study outlines the importance of individual-level analysis to understand the electrification potential and future benefits of electric vehicles in the era of shared smart transportation.