This contribution firstly proposed the concept of annual average power generation hours and analyzed per capita energy consumption, carbon emission, and the human development index from a macro perspective. On this basis, we compared the average household electrical energy consumption of urban and rural residents based on the data from CGSS-2015 from a micro perspective. The results show the positive correlation between carbon emissions per capita and the human development index and China’s regional imbalance characteristics between household electricity consumption and renewable energy distribution. Therefore, the distributed energy supply system is proposed as an effective complement to centralized power generation systems and is the key to synergizing human development and carbon emissions in China. Moreover, we analyzed the characteristics of distributed energy supply systems in the context of existing energy supply systems, pointing out the need to fully use solar energy and natural gas. Finally, two types of typical distributed energy supply systems are proposed for satisfying the household energy requirements in remote or rural areas of western and the eastern or coastal areas of China, respectively. Two typical distributed energy systems integrate high-efficiency energy conversion, storage, and transfer devices such as electric heat pumps, photovoltaic thermal, heat and electricity storage, and fuel cells.

As the heat flux of electronic components is increasing rapidly, the traditional air-cooling technique is gradually not meeting the requirements of thermal management. The immersion liquid-cooling technique shows great potential, and has attracted increasing attention due to its excellent performance in recent years. The finned heat sink is common and essential for cooling electric components. To analyze the influences of its structural parameters on heat dissipation and improve its efficiency while using a dielectric coolant, this study used the orthogonal analysis method to obtain the optimal structure via the numerical simulation method. The maximum temperature of the heat sink was selected as the evaluation criteria. The results showed that the parameters that affect the maximum temperature, in order of importance, are fin thickness, the number of fins, the height of the fins, and substrate thickness. Finally, taking the maximum temperature and mass as indexes obtained the optimal structure of the heat sink. The mass was reduced by 19%, while the temperature only increased by 4.5% when considering the mass index.

Packed bed thermal energy storage (PBTES) is an essential means to solve the temporal difference and continuity between energy supply and utilization in the fields of concentrating solar power, compressed‐air energy storage, and waste heat recovery. In this paper, to solve the imperfection and inaccuracy of the current energy model, a precise transient two‐dimensional multiequation model is developed. The model comprehensively considers thermal dispersion, wall effects, thermophysical property changes, and other factors. Experiments are performed to compare and validate the simulation results, and the simulation error is −4.5% to 1.9%, achieving excellent simulation accuracy. Also, the wall effect on the PBTES is observed, and the gas‐solid temperature fields exhibit an evident nonuniform oscillation distribution in the radial direction. The effects of storage tank length, thermophysical properties, particle diameter, and gas‐solid initial temperature on the heat release rate are discussed in detail. Results indicated that decreasing the tank length, increasing the gas‐solid temperature difference, and particle materials with high thermal diffusivities can increase the heat release rate, and a clear linear change was found. Particularly because a larger range of particle diameter variation is considered, the heat release rate is shown to vary nonlinearly with particle diameter, and the selection of the optimal diameter must comprehensively consider the effects of the heat capacity and the heat transfer rate. This study provides a theoretical basis for the design of thermal energy storage systems.

Quantum thermodynamic relationships in emerging nanodevices are significant but often complex to deal with. The application of machine learning in quantum thermodynamics has provided a new perspective. This study employs reinforcement learning to output the optimal cycle of a quantum heat engine. Specifically, the soft actor-critic algorithm is adopted to optimize the cycle of a three-level coherent quantum heat engine with the aim of maximal average power. The results show that the optimal average output power of the coherent three-level heat engine is 1.28 times greater than the original cycle (steady limit). Meanwhile, the efficiency of the optimal cycle is greater than the Curzon-Ahlborn efficiency as well as efficiencies reported by other researchers. Notably, this optimal cycle can be fitted as an Otto-like cycle, which illustrates the effectiveness of the method.