The cold accumulation problem can lead to performance degradation of heat pumps. This paper presents the design and optimization of a solar-assisted storage system to solve this issue. A ground source heat pump (GSHP) project was established using the transient system simulation program (TRNSYS) based on a ground heat exchange theoretical model, which was validated by a previously established experiment in Beijing. The Beijing, Harbin, and Zhengzhou regions were used in numerical simulations to represent three typical cities where buildings require space heating (a cold region, a severe cold region, and a hot summer and cold winter region, respectively). System performance was simulated over periods of ten years. The simulation results showed that the imbalance efficiencies in the Beijing, Harbin, and Zhengzhou regions are 55%, 79%, and 38%, respectively. The annual average soil temperature decreases 7.3°C, 11.0°C, and 5.3°C during ten years of conventional GSHP operation in the Beijing, Harbin, and Zhengzhou regions, respectively. Because of the soil temperature decrease, the minimum heating coefficient of performance (COP) values decrease by 23%, 46%, and 11% over the ten years for GSHP operation in these three regions, respectively. Moreover, the simulation data show that the soil temperature would still be decreasing if based on the previous solar energy area calculation method. Design parameters such as the solar collector size are optimized for the building load and average soil temperature in various cold regions. Long-term operation will test the matching rate of the compensation system with the conventional GSHP system. After the system is optimized, the solar collector area increases of 20% in the Beijing region, 25% in the Harbin region, and 15% in the Zhengzhou region could help to maintain the annual average soil temperature balance. The optimized system could maintain a higher annual average COP because of the steady soil temperature. It provides a method for the design of a solar collector area which needs to be determined in the seasonal heat storage solar ground source heat pump system.

Buildings are significant contributors to global energy consumption and greenhouse gas emissions, with air conditioning systems representing a large share of this demand. Multi-building cooperative energy management is a promising solution for improving energy efficiency, but traditional control methods often struggle with dynamic environments and complex interactions. This study proposes an enhanced Soft Actor–Critic (SAC) algorithm, termed ORAR-SAC, to address these challenges in building cluster energy management. The ORAR-SAC integrates an Ordered Reward-based Experience Replay mechanism to prioritize high-value samples, improving data utilization and accelerating policy convergence. Additionally, an adaptive temperature parameter regularization strategy is implemented to balance exploration and exploitation dynamically, enhancing training stability and policy robustness. Using the CityLearn simulation platform, the proposed method is evaluated on a cluster of three commercial buildings in Beijing under time-of-use electricity pricing. Results demonstrate that ORAR-SAC outperforms conventional rule-based and standard SAC strategies, achieving reductions of up to 11% in electricity costs, 7% in peak demand, and 3.5% in carbon emissions while smoothing load profiles and improving grid compatibility. These findings highlight the potential of ORAR-SAC to support intelligent, low-carbon building energy systems and advance sustainable urban energy management.

As an efficient energy conversion technology, the organic Rankine cycle (ORC) has been widely applied in medium- and low-grade heat sources. In order to explore the experimental performance of ORC and reveal the effects of operation parameters, an experimental setup was built and R123 was selected as the working fluid. In the experiments, the heat source temperature as well as the mass flow rates of the working fluid and cooling water were controlled. Under the design conditions, the net work and cycle efficiency can, respectively, reach up to 0.55 kW and 8.7%. As for the influences of key parameters, with the increase in heat source temperature from 130 °C to 160 °C, the involved heat has a small increase, while the net work increases from 0.44 kW to 0.55 kW, and the cycle efficiency greatly increases from 6.71% to 8.72% at a mass flow rate of working fluid 25 g/s. As for the mass flow rate of cooling water, it has a similar impact on the cycle performances. The difference is that the effect of the cooling water rate is relatively smaller. At the mass flow rate 25 g/s, when the cooling water rate increases from 0.68 kg/s to 0.83 kg/s, the net work varies from 0.46 kW to 0.55 kW, the cycle efficiency increases in the range 7.41~9.4%. Furthermore, except cycle efficiency, all performances are proportional to the mass flow rate of working fluid. In the test range, the difference of cycle efficiency among different mass flow rates is less than 0.7%.

Efficient management of building energy systems, particularly those integrating photovoltaic (PV) generation and energy storage systems (ESSs), is challenging due to the fluctuating nature of energy demand, supply, and electricity prices. Traditional optimization methods, such as day‐ahead or intraday scheduling, often fail to fully meet the operational needs of energy systems, leading to wasted renewable energy, unmet load demand, and suboptimal cost‐efficiency. This study addresses these gaps by proposing an integrated optimization strategy that combines day‐ahead predictive scheduling with intraday adjustments, leveraging a second‐order equivalent resistance–capacitance (RC) model to manage ESS charging and discharging. The strategy optimizes energy storage utilization and minimizes reliance on grid power by dynamically adjusting charging and discharging based on intraday conditions and price fluctuations. Tested on an office building, the proposed strategy reduced operating costs by 29.66% compared to day‐ahead scheduling alone and by 15.48% compared to intraday optimization while eliminating unmet load demand and minimizing wasted PV generation. Grid reliance was also reduced by 33.33%. These findings underscore the critical role of coordinated charging and discharging strategies in improving energy management efficiency and reducing costs in PV–ESS integrated systems.