Integrated Intelligent Energy

Research progress on modification methods for calcium looping thermochemical heat storage

GENG Bochen, SHI Xin, XIONG Yaxuan, JIANG Zeling

2025, 47(12): 1-13. doi:10.3969/j.issn.2097-0706.2025.12.001

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Recent advances in calcium looping thermochemical heat storage technologies are summarized， with a focus on the current research status of physicochemical properties， modification strategies， reactor design， and system integration of calcium-based materials. The main types of calcium-based materials are introduced， including naturally occurring， chemically synthesized， doped， and composite calcium-based materials， followed by a detailed discussion of how their physical characteristics and chemical properties （particle size， specific surface area， pore structure， thermal conductivity， and mechanical strength） influence heat storage performance. The physicochemical properties of these materials are thoroughly discussed， indicating their significant influence on heat storage efficiency and cyclic stability. The modification methods for calcium-based materials are introduced in detail， including physical modification such as particle size control and support addition， and chemical modification like metal oxide doping and surface coating. Through these methods， the cyclic stability and sintering resistance of calcium-based materials can be significantly enhanced. The design and optimization of calcium looping heat storage reactors and systems are discussed， including fixed-bed， fluidized-bed， and other novel reactor types. These reactors exhibit respective advantages and challenges in terms of heat and mass transfer efficiency， particle attrition balance， and system integration flexibility. The synergistic effects between reactor types and system configurations are analyzed， and the advantages of calcium looping heat storage in terms of heat storage density and mechanical strength are further explored， as well as challenges such as material deactivation， low system efficiency， and economic feasibility. Future research directions are outlined， highlighting the need to further develop highly stable materials， optimize reactor design， and conduct full life-cycle techno-economic analysis to promote the commercial application of calcium looping thermochemical heat storage technology.

Research progress in molten nitrate salts for thermal energy storage

JIANG Zeling, XIONG Yaxuan, BAI Yinlei, GENG Bochen

2025, 47(12): 14-24. doi:10.3969/j.issn.2097-0706.2025.12.002

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With the advancement of the "dual carbon" goals， the importance of thermal energy storage technology has become increasingly prominent. As an ideal thermal storage material， molten salts have gained significant attention in the medium- and high-temperature heat transfer and storage fields. Molten nitrate salts， in particular， exhibit unique advantages that lead to widespread applications across various fields. Recent research on the issues of insufficient thermal storage performance and strong metal corrosiveness in molten nitrate salt systems is reviewed， with a detailed overview of methods to improve the thermal storage performance of molten nitrate salts from two aspects： the development of multi-component molten nitrate salt systems and the doping of nanomaterials. Additionally， the impact of impurity ions on molten nitrate salts is discussed from the perspectives of thermal properties and corrosion. Research indicates that multi-component molten nitrate salt systems and doping with nanomaterial can substantially enhance thermal storage performance of molten salts. However， the presence of impurity ions negatively affects their performance. Future research could focus on the development of novel molten nitrate salt systems， the synergistic enhancement mechanism of nanoparticles， the combined effects of impurity ions on corrosion behavior， and the development of online detection technologies to further improve molten salt thermal storage technology.

Prediction of remaining useful life of lithium batteries based on grey wolf optimization and combined kernel function GPR model

HU Linjing, LI Zaiwei

2025, 47(12): 25-33. doi:10.3969/j.issn.2097-0706.2025.12.003

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Accurately predicting the remaining useful life （RUL） of lithium-ion batteries can help users formulate reasonable maintenance strategies. As a data-driven method， Gaussian process regression （GPR） algorithm can effectively capture the nonlinear relationship between features and variables， and thus is widely used in the RUL estimation of lithium batteries. To address the deficiency of traditional single-kernel GPR in feature capture， a combined kernel GPR model based on the grey wolf optimization （GWO） algorithm was proposed. By combining kernel functions， the model's ability to capture nonlinear features was enhanced， and the GWO algorithm was utilized to overcome the difficulty in optimizing the hyperparameters of the combined kernel function. The NASA lithium battery cycle aging dataset was adopted to verify this model， and the single-kernel GPR model based on particle swarm（PSO） optimization was selected for comparison. The experimental results showed that the GWO-optimized combined kernel GPR model achieved a 37.89% reduction in root mean square error （RMSE） and a 70.42% reduction in mean absolute error （MAE） compared with the PSO-optimized single-kernel GPR model， demonstrating a stronger ability to capture capacity degradation. The results indicate that compared with the traditional GPR model， the GWO-optimized combined kernel GPR model has higher accuracy for the RUL prediction of lithium batteries.

Economic optimal scheduling of electricity-hydrogen coordinated energy storage system considering spatiotemporal correlation of wind and photovoltaic power outputs

WANG Qianrui, RUAN Jingxin, WANG Yueshe

2025, 47(12): 34-45. doi:10.3969/j.issn.2097-0706.2025.12.004

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In China， there is a spatiotemporal mismatch between the areas rich in wind and solar energy resources and the load centers. By leveraging the correlation and complementarity between wind and solar energy within the same region， an electricity-hydrogen coordinated energy storage model was proposed， which was an effective technical approach to mitigate the adverse impact of renewable energy output on the power grid. The probability distribution patterns of wind and photovoltaic power output data were fitted using the nonparametric kernel density estimation method， and time-series scenarios considering the spatiotemporal correlation of wind and photovoltaic power were generated using Copula theory. By considering the correlation between wind and photovoltaic power generation， an economic day-ahead optimal scheduling model for the integrated electricity-hydrogen coordinated energy system was further established， and solved using the adaptive simulated annealing particle swarm optimization （ASA-PSO） algorithm. The simulation results showed that compared to the basic PSO algorithm， the ASA-PSO algorithm demonstrated superior solving speed and accuracy. The economic day-ahead optimal scheduling scheme for the electricity-hydrogen coordinated energy storage system reduced daily operating costs by approximately 19%， avoided large-scale electricity purchases during peak price periods， and enabled local consumption of fluctuating renewable energy. It provides a flexible electricity-hydrogen matching approach for constructing a grid-friendly and large-scale power system.

Multi-objective optimization of multi-energy supply for residential buildings in cold regions based on energy storage

YAN Jing, LI Meng, GUAN Baoliang, MENG Siyu, FAN Yanbo, WANG Fenglong, YANG Shangfeng, YANG Zhongyang, XIONG Yaxuan

2025, 47(12): 46-56. doi:10.3969/j.issn.2097-0706.2025.12.005

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Aiming at the current situation of high heating demand， large energy consumption， and heavy carbon emission pressure for residential buildings in cold regions during winter， and considering the coupling effect of electrical and thermal energy storage， a multi-objective optimization model of an integrated energy system incorporating photovoltaic power generation， air-source heat pumps， gas boilers， energy storage batteries， and high-temperature thermal storage devices was constructed. The performance of three optimization algorithms was compared across five cities with heterogeneous climates. Based on meteorological data from Beijing， Zhengzhou， Yinchuan， Lhasa， and Kashgar， along with a building thermal model established in EnergyPlus， the baseline energy demand was determined. Utilizing the Matlab platform， the non-dominated sorting genetic algorithm Ⅱ（NSGA-Ⅱ）， particle swarm optimization（PSO）， and simulated annealing（SA） were employed to conduct multi-objective optimization for minimizing carbon emissions and economic cost. The results showed that the NSGA-Ⅱ achieved the best trade-off between cost and carbon emissions in most scenarios， demonstrating the optimal comprehensive performance. The PSO showed significant effectiveness in optimizing operating costs in regions with long heating periods. The SA was suitable for scenarios aiming for lower operating costs， but it was typically accompanied by higher initial investment. This optimization model can balance the economic and low-carbon performance of residential buildings in cold regions， significantly improve the utilization rate of renewable energy， and provide a reference for the planning and operation of building integrated energy systems under cold climate conditions.

Research on coordinated optimized operation of microgrids in industrial parks considering energy storage operators

LI Menglu, Degejirifu

2025, 47(12): 57-65. doi:10.3969/j.issn.2097-0706.2025.12.006

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Energy storage systems co-located with renewable energy stations in industrial parks generally lack effective market-based profitability models， resulting in low utilization rates and poor economic performance. In contrast， independent energy storage operators can flexibly participate in electricity market trading. Energy storage operators that meet technical and safety standards can enhance the overall economic efficiency of multiple sub-microgrid centers within industrial parks. A coordinated optimized operation model for multi-microgrids in industrial parks， considering the participation of unified energy storage operators， was proposed. Based on factors such as power output characteristics and topological structure， the system operation architecture was constructed， and the operational modes were analyzed. K-means clustering was employed to address the uncertainties in wind and solar power output. A coordinated optimized operation model for microgrids within industrial parks was established， and the branch and bound method was applied to solve the model. Comparisons with particle swarm optimization and genetic algorithms demonstrated its advantages in solution efficiency and accuracy. The case study results demonstrated that microgrids within industrial parks， considering unified energy storage operators， could effectively increase economic benefits for both individual entities and the overall system. This approach significantly increased the revenues of energy storage operators， effectively addressed the issues of renewable energy consumption， and reduced dependence on the distribution network， providing a quantifiable decision-making basis for microgrid operation and scheduling under unified energy storage operator management.

Thermodynamic analysis and performance enhancement of high-temperature heat pump coupled energy storage system

MA Xudong, DU Yanjun, LI Bingqi, CUI Yin, ZHANG Cancan, WU Yuting

2025, 47(12): 66-72. doi:10.3969/j.issn.2097-0706.2025.12.007

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Under the "dual carbon" goals， it is urgent to reduce CO₂ emissions from fossil fuel combustion in the industrial steam production process. High-temperature heat pumps， as highly promising low-carbon energy conversion systems， can not only efficiently produce high-temperature steam but also significantly reduce energy consumption and carbon emissions. To address the challenge that single-stage high-temperature heat pumps cannot achieve large temperature lifts， an efficient energy solution integrating high-temperature heat pumps with energy storage systems was proposed. This coupled system could leverage the characteristics of the energy storage system to reduce the compressor pressure ratio， thereby enabling efficient steam production under extreme operating conditions. Additionally， an integrated regulation model for variable operating conditions including energy， exergy， economic， and environmental benefits was established. Through comparative analysis with conventional high-temperature heat pumps capable of large temperature lifts， the application potential of the high-temperature heat pump coupled with the energy storage system was evaluated. Furthermore， an optimal strategy model for the coupled system was established. The results showed that under operating conditions where single-stage high-temperature heat pumps failed to operate effectively， the high-temperature heat pump coupled with an energy storage system could still maintain stable industrial steam output， with its coefficient of performance and steam production improved by at least 134.3% and 461.5%， respectively. The energy storage system had an optimal operating strategy， and only through rational configuration under variable operating conditions could the coupled system achieve synchronous improvements in performance and economic efficiency.

Load prediction of primary heating networks based on tree models and neural networks

YAN Jing, JIANG Zeling, GUAN Baoliang, MENG Siyu, WANG Fenglong, YANG Shangfeng, YANG Zhongyang, XIONG Yaxuan

2025, 47(12): 73-80. doi:10.3969/j.issn.2097-0706.2025.12.008

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Accurate prediction of building heating load is crucial for optimizing the operation of building energy systems， reducing energy consumption， and achieving building energy-saving goals. Using machine learning algorithms for prediction can effectively overcome the limitations of traditional prediction methods， significantly reducing the computational cost of conventional simulation analyses and enhancing system energy efficiency. Five regression machine learning models — random forest regression （RFR）， extremely randomized trees regression （ETR）， gradient boosting regression （GBR）， extreme gradient boosting regression （XGBR）， and multilayer perceptron （MLP） — were employed to predict building heating load. Four indicators were used to evaluate their prediction accuracy. The results showed that the ETR and XGBR models demonstrated the optimal predictive performance among all models. The ETR model achieved a root mean square error （RMSE） as low as 97.189 4 kW and an R² value of 0.766 0. The XGBR model achieved a mean absolute error （MAE） and a mean absolute percentage error （MAPE） as low as 69.967 1 kW and 4.086 0%， respectively. These two models achieve high predictive accuracy， providing valuable references for subsequent research on building heating load prediction.

Impact of expander automatic control on operational stability during AA-CAES startup process

WEN Xiankui, LI Yaqin, ZHANG Shihai, FAN Qiang, YE Huayang, XIE Yiying, LI Xinzhuo

2025, 47(12): 81-88. doi:10.3969/j.issn.2097-0706.2025.12.009

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To investigate the operational regulation strategy， complexity， and stability of the expander during the startup process of an advanced adiabatic compressed air energy storage （AA-CAES） system， proportional-integral-derivative （PID） control was implemented on the temperature and pressure parameters at the expander inlet based on the established expander energy release model. The effects of the pressure gain coefficient K and temperature proportional coefficient K_p on the startup characteristics of the expander were systematically analyzed， including the dynamic responses of output power， outlet temperature， and outlet pressure， as well as the stability during the startup process， thereby enhancing the system's anti-interference capability to address sudden failures of key equipment. The results showed that when K=0.5， compared with K=1.5， the output power peak was 4.2% higher， the pressure decline was more gradual， and the curve slope was smaller， exhibiting better response smoothness. When K_p=0.1， the peak expansion ratio was about 0.54% higher than that when K_p=0.5， while the peak isentropic efficiency at different K_p values remained consistent， both reaching 0.88. When K=1.0， the peak outlet temperature was the smallest and the stabilization time was the shortest. When K_p=0.3， the isentropic efficiency could rapidly stabilize. The findings indicate that reasonable regulation of PID parameters can significantly improve the expander startup performance， thereby providing control strategies for solving the problem of unit stability degradation caused by key equipment failures.

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