The solar energy supply system has played an increasingly substantial role in realizing nearly zero-carbon buildings. In order to overcome the impact of solar randomness on the energy supply of a distributed solar system, this paper proposes a solar tri-generation supply system which integrates a photovoltaic/thermal collector (PV/T), a heat pump (HP), and an absorption chiller (AC). The PV/T-HP integration system is adopted to provide stable heating for a building and AC. The system model is established in TRNSYS software, and its performance is evaluated based on energy, exergy, and economic aspects. The results demonstrate that the system effectively meets the load demand, with an energy efficiency of 32.98% and an exergy efficiency of 17.62%. The payback period (PP) is 7.77 years. Compared with the systems proposed in the other literature, the performance of the proposed system has a certain extent of advantage. Furthermore, the equipment and system exergy performance decline with an increase in the intensity of solar radiation. Increasing the PV/T area effectively improves the system’s profitability within the actual roof area limitation of the building. Moreover, increasing the capacity of the low-temperature heat pump after 68 kW improves the system efficiency and reduces the payback period. In summary, this paper proposes an efficient distributed solar energy system that is suitable for urban building energy supply.

In this paper, a low-concentration photovoltaic/thermal (LCPV/T) coupled with air-source heat pump (AHP) system is proposed which fully utilizes the heat generated by LCPV/T and improves the performance of the AHP. The system is built and investigated in the Transient System Simulation Program (TRNSYS) and an experimental room model is established to verify the feasibility of the system. The performance of the system is researched from the perspective of energy and exergy, and the system performance with LCPV/T and without LCPV/T is compared. Finally, the influence of the variation of key parameters of the system is studied. The results indicated that on the coldest day, the electrical efficiency of LCPV/T reached 10% which was equal to the electrical exergy efficiency. The maximum thermal efficiency was 31.88% while thermal exergy efficiency was 2.7%. The maximum coefficient of performance (COP) of AHP was 3.3, and the thermal exergy efficiency was 47%. The indoor temperature was maintained at about 20 °C in the heating season. When LCPV/T was adopted, the COP and thermal exergy efficiency of the AHP was generally higher than those without LCPV/T. In conclusion, the utilization of LCPV/T has a positive impact on the performance of the AHP.

Highly integrated energy systems are on the rise due to increasing global demand. To capture the underlying physics of such interdisciplinary systems, we need a modern framework that unifies all forms of energy. Here, we apply modified Lagrangian mechanics to the description of multi-energy systems. Based on the minimum entropy production principle, we revisit fluid mechanics in the presence of both mechanical and thermal dissipations and propose using exergy flow as the unifying Lagrangian across different forms of energy. We illustrate our theoretical framework by modeling a one-dimensional system with coupled electricity and heat. We map the exergy loss rate in real space and obtain the total exergy changes. Under steady-state conditions, our theory agrees with the traditional formula but incorporates more physical considerations such as viscous dissipation. The integral form of our theory also allows us to go beyond steady-state calculations and visualize the local, time-dependent exergy flow density everywhere in the system. Expandable to a wide range of applications, our theoretical framework provides the basis for developing versatile models in integrated energy systems.

With 2 × 330MW subcritical coal-fired boiler as the research object, the influence of two kinds of primary air flue gas recirculation schemes on the combustion of pulverized coal and the formation of NO_x were studied by numerical simulation software. The variation law of SCR inlet fume temperature was studied with boiler thermodynamic calculation. The calculation results show that the reasonable smoke mixing method can effectively improve the SCR inlet flue gas temperature and reduce the furnace outlet NO_x concentration. The optimal proportion was 20% with the primary air mixing smoke while maintaining constant air mass. The results of numerical simulation and thermodynamic calculation show that SCR inlet flue temperature can be increased by 15°C, the concentration of NO_x emission can reduce by 18.29mg/Nm³.

Accurate wind power forecasting is essential for the stability of modern power systems. However, current probabilistic forecasting frameworks often encounter a fundamental conflict between the computational efficiency required for high-dimensional meteorological pattern matching and the physical consistency of the resulting probability distributions. Existing methods frequently fail to maintain the logical monotonicity of quantiles or overlook the fine-grained temporal correlations in massive historical datasets. To address these critical gaps, this research develops a comprehensive framework that synergizes a hierarchical similarity filtering mechanism with a structurally constrained non-crossing quantile regression model. First, the target sample is partitioned into several weather segments, and a new two-stage high-similarity weather pattern matching method is developed to screen multiple sets of historical samples that are highly similar to the target weather pattern. Second, a deep learning model for probabilistic wind power quantile forecasting is proposed, which incorporates historical data augmentation. The model utilizes an attention mechanism to extract the correlation between the target and historical segments, while an improved non-crossing quantile regression model is adopted to ensure the validity of the output quantiles. Finally, the effectiveness of the proposed method is validated through case studies using real-world data from an actual wind farm.