Reasonable planning and scheduling in low-carbon parks is conducive to coordinating and optimizing energy resources, saving total system costs, and improving equipment utilization efficiency. In this paper, the optimization study of a distributed photovoltaic energy storage system considers the synergistic effects of the planning and operation phases. On the basis of the variable operating characteristics of the unit equipment and the complementary synergistic characteristics of the energy storage equipment, a two-layer optimization model combining planning and operation is adopted, with the minimum total cost and the minimum carbon emission content in the whole life cycle of the system as the optimization objectives and the upper layer of the planning equipment capacity and the configured capacity of each equipment in the system as the optimization variables, which are solved by using the multi-objective no-dominated-sorting genetic algorithm. The lower layer is the optimized operation mode, and the time-by-time operating capacity of each item of equipment is the optimization variable, which is solved by the interior point method. The upper layer optimization results are used as the constraint boundary conditions for optimization of the lower layer, and the lower layer optimization results provide feedback correction to the upper layer optimization results, which ultimately determine the energy system optimization scheme. The optimization results reflect that photovoltaic green power should be arranged in large quantities as a priority, and the synergistic effect of power and cold storage equipment on the system’s economy and low-carbon performance is positive. At the same time, by setting up four control scenarios of only cold storage, only electricity storage, no energy storage, and no two-tier optimization, the impacts of cold storage and electricity storage on the economic and environmental aspects of the system and the positive effect of mutual synergy are investigated, which concretely proves the validity of the two-tier optimization strategy, taking into account the operating characteristics of the equipment.

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.

The hot-wall heat exchanger (HWHE) has been widely used in thermal engineering fields such as ceiling radiant heating/cooling, refrigerator condenser, solar heat collection, and high-temperature heat recovery. However, the numerical simulation normally used for heat transfer prediction in HWHE is usually not as convenient as the analytic solutions in engineering applications. In this paper, a new heat transfer mathematical model of HWHE-based on analytic solutions was developed, which could be much faster to obtain the heat transfer properties of HWHE. The proposed model was validated under four conditions with literature values, which showed that the deviations of heat flux are 2.53%, 0.99%, 2.12%, and 1.96%, indicating its accuracy is satisfied. The model was then used to analyze the thermal property of HWHE. The results show the thermal resistance caused by panel with heat convection and conduction accounts for 96.54% of HWHE thermal resistance, and the thermal resistance caused by heat convection on the surface of panel is 74.43%. The analyzation results also show that adding aluminum foil around pipes could decrease HWHE thermal resistance by 5.11%. Besides, the influence of pipe diameters, pipe distance, pipe heat conductivity, side wall heat conductivity, and convective heat transfer coefficient on the heat transfer performance of HWHE was analyzed. The research in this paper can be used for fast prediction and optimization of heat transfer in HWHE.

With rapidly developing new energy technologies, rational energy planning has become an important area of research. Ground source heat pumps (GSHPs) have shown themselves to be highly efficient. effective in reducing building or district energy consumption and operating costs. However, when optimizing integrated energy systems, most studies simplify the GSHP model by using the rated coefficient of performance (COP) of the GSHP unit, neglecting factors such as soil, buried piping, and actual operating conditions. This simplification leads to a deviation from the actual operation of GSHPs, creating a gap between the derived operational guidelines and real-world performance. Therefore, this paper examines a hotel equipped with photovoltaic panels, a GSHP, and a hybrid energy storage unit. By constructing models of the underground pipes, GSHP units, and pumps, this paper considers the thermal exchanger between the underground pipes and the soil, the thermal pump, and the operating status of the unit. The purpose is to optimize the running expenses using an enhanced mote swarm optimization (PSO) algorithm to calculate the optimal operating strategy of system equipment. Compared to the simplified energy system optimization model, the detailed GSHP unit model shows a 21.36% increase in energy consumption, a 13.64% decrease in the mean COP of the GSHP unit, and a 44.4% rise in system running expenses. The differences in the GSHP model affect the energy consumption results of the unit by changing the relationship between the power consumption of the PV system and the GSHP at different times, which in turn affects the operation of the energy storage unit. The final discussion highlights significant differences in the calculated system operating results derived from the two models, suggesting that these may profoundly affect the architectural and enhancement processes of more complex GSHP configurations.

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%.