Within the next five years, renewable energy is expected to account for approximately 80% of the new global power generation capacity, with solar power contributing to more than half of this growth. However, the intermittent nature of solar energy remains a significant challenge to fully realizing its potential. Thus, efficient energy storage is crucial for optimizing the effectiveness and dependability of renewable energy. Phase-change materials (PCMs) can play an important role in solar energy storage due to their low cost and high volumetric energy storage density. The low thermal conductivity of PCMs restricts their use for energy storage, despite their immense potential. Hence, the primary goal of this study is to experimentally investigate the energy storage capacity of two blended phase-change materials (paraffin and barium hydroxide octahydrate) through integration with a medium-temperature solar heat collection system. The experimental findings reveal that the blended PCMs possess the highest cumulative charge fraction (0.59), energy capacity, and low energy loss compared to each PCM alone. Furthermore, the phase change storage tank achieves higher heat storage (27%) and exergy storage efficiency (18%) compared to the stored tank water without any PCMs. The blended PCMs enhanced their performance, exhibiting improved interaction and excellent thermal storage properties across a range of temperatures, offering an opportunity for the design of an energy-efficient, low-cost storage system.

Refrigeration is vital in daily life and industries, traditionally relying on single-system cooling. The two predominant kinds of single-system cooling are vapor compression refrigeration (VCR) and thermoelectric cooling (TEC). Each of these two single systems has its own disadvantages, such as higher input energy requirements and lower efficiency. However, the effect of the integration of VCR and TEC for achieving higher cooling performance with lower energy input has not been well studied and reported in the existing literature. Therefore, the aim of this study is to conduct a thorough investigation into an integrated refrigeration system that combines VCR and TEC. This integration allows switching between systems based on specific requirements, leveraging the high coefficient of performance (COP) of VCR and the benefits of TEC. Three configurations have been studied, and each of them has three operating conditions: VCR alone, TEC alone, and TEC hybrid with VCR. Configuration I corresponds to the results from the individual refrigeration test. In Configuration II, the hot end of the thermoelectric cooling module is installed at the insulation layer between the TEC layer and the VCR compartment. In Configuration III, the cold end of the thermoelectric cooling module is positioned at the insulation layer between the TEC layer and the VCR compartment. Configuration III of the integrated system demonstrated good performance by reducing the time required to reach the target temperature. It took 40 min for TEC alone to reach a temperature of 11.1 °C, 13 min for VCR alone, and only 9.6 min for a hybrid system. The hybrid system shows increased versatility and potential for future applications, providing valuable insight into optimizing advanced cooling technologies. Furthermore, from an economic and sustainability standpoint, the proposed hybrid refrigeration system is advantageous and ambitious as it offers superior cooling capacity and greater efficiency than current refrigeration systems.

The strong uncertainty of renewable energy poses significant reliability and safety challenges for the coordinated operation of multi-integrated energy systems (MIES). To address this, a data-driven two-stage distributed robust collaborative optimization scheduling model for MIES is proposed, based on a spatiotemporal fusion conditional diffusion model (STF-CDM). First, to more accurately capture the uncertainty in renewable energy output, the model utilizes a scenario set generated by the STF-CDM model and reduced via the K-means clustering algorithm as the initial renewable energy scenarios for the distributed robust optimization set. The STF-CDM model employs a Temporal module component (TMC) unit composed of Transformer time-series modules and a Spatial module component (SMC) unit composed of CNN neural networks for feature extraction and fusion of time-series and spatial-series data. Second, a benefit allocation method based on multi-energy trading contribution rates is proposed to achieve equitable distribution of cooperative gains. Finally, to protect participant privacy and enhance computational efficiency, an alternating direction multiplier method (ADMM) coupled with parallelizable column and constraint generation (C&CG) is employed to solve the energy trading problem. The case analysis results demonstrate that the STF-CDM model proposed in this study exhibits superior performance in addressing the uncertainty of renewable energy output. Concurrently, the asymmetric Nash game mechanism and the ADMM-C&CG solution algorithm proposed in this study achieve a fair and reasonable distribution of benefits among all participants when handling energy transactions and cooperative gains. This is accomplished while ensuring system robustness, economic efficiency, and privacy.