Table of Content

25 April 2026, Volume 48 Issue 4

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Integrated Energy System Analysis and Evaluation Optimized Configuration and Load Regulation

Integrated Energy System Analysis and Evaluation

Research progress on PVACs and their application in buildings

CAI Yang, ZHENG Shanle, HUANG Yingxi, LIU Ziquan, AN Rongbang

2026, 48(4):  1-11.  doi:10.3969/j.issn.2097-0706.2026.04.001

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Photovoltaic-driven air conditioning （PVAC） systems offer a new approach to reducing building energy consumption and advancing carbon neutrality by integrating renewable energy with building thermal management capabilities. The technical principles and classification characteristics of PVAC systems are systematically reviewed， and their driving mechanisms are categorized into three main types： AC-driven， DC-direct-driven， and hybrid-driven. The research progress on PVAC in terms of energy transfer optimization， dynamic control strategies， and multi-parameter collaborative design is presented. It is demonstrated that the optimized PVAC systems can achieve efficient matching of photovoltaic power generation and refrigeration demand in high-temperature regions in summer， with energy consumption coverage of air conditioning increasing to 29.5%. Furthermore， the system scheme integrating phase change materials and variable-speed compressors can significantly increase the real-time zero energy probability. The integration of PVAC systems with buildings （i.e.， building-integrated photovoltaics） can not only enhance building performance but also improve the energy utilization efficiency of PVAC systems. However， there is still a lack of a unified evaluation indicator system for building-integrated photovoltaic systems. Future research is expected to provide new insights into improving PVAC system performance and promoting their large-scale application in buildings.

Dynamic performance analysis of a combined cooling， heating， and power system based on PEMFC-ASHP integration

SHI Qindong, ZHANG Li, XU Qing, FU Changchang, ZHANG Xiangqin, CHEN Xi

2026, 48(4):  12-18.  doi:10.3969/j.issn.2097-0706.2026.04.002

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The proton exchange membrane fuel cell （PEMFC） is an electrochemical energy conversion device that converts the chemical energy of hydrogen fuel into electrical energy， characterized by advantages such as high energy efficiency， low emissions， and low noise. The air-source heat pump （ASHP） is a device driven by a small amount of electricity to upgrade thermal energy from low-grade to high-grade， capable of providing cooling and heating for buildings according to different seasons and meeting domestic hot water demand. The combined cooling， heating， and power system based on PEMFC-ASHP employed the fuel cell stack and ASHP as dual energy-driven units. Through heat pump technology， the waste heat generated during the power generation process of the fuel cell was further utilized to simultaneously provide electricity， heating， and cooling for building occupants， thereby achieving high-efficiency energy utilization in the combined cooling， heating， and power system. The results showed that the ASHP could enhance the operational performance of the PEMFC system. The system's energy efficiency reached 150%， the coefficient of performance of the ASHP subsystem ranged between 2 and 4， the net power generation efficiency of the PEMFC was approximately 40%， and the overall exergy efficiency of the PEMFC-ASHP integrated system reached 44.46%.

Evaluation method for electric heating-heat storage integrated systems based on machine learning

LI Kecheng, JIN Lu, CHENG Ling, ZHONG Ming, JIA Xinyi

2026, 48(4):  19-26.  doi:10.3969/j.issn.2097-0706.2026.04.003

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Under the dual carbon target， electric heating technology has achieved large-scale applications by virtue of its cleanliness and flexibility. Accurate detection and evaluation for the electric heating systems integrated heat storage device is the key to the efficient use of this technology. However， existing test methods are unsuitable for the detecting the systems due to instantaneous state instability and periodic fluctuations of water-supply temperature and electric power. An energy efficiency evaluation method suitable for electric heating-heat storage integrated systems is proposed， which is comprised of three processes： experimental preparation stage， pre-experimentation stage and experimental testing stage. Based on the monitoring data from the experimental testing stage， the energy efficiency evaluation executed on the electric heating device under whole-process and all-scenario steady states is realized by the machine learning model. The simulation results show that the proposed method can reflect the energy efficiency of the integrated system accurately， with an evaluation accuracy rate 5.74 percentage points higher than that obtained by the detection method specified in the national standard. And the proposed method can reduce the negative influence of transient instability and periodic fluctuations of water-supply temperature and electric power on the evaluation test for the integrated system， and can be applied to other electric heating devices for energy efficiency evaluation.

Real-time heat supply identification method and application for rural residences in North and Northeast China during heating season

CHEN Yue, TIAN Shen, ZHAN Binfei, SHI Xiaodong, XU Weichen, HU Kaiyong

2026, 48(4):  27-34.  doi:10.3969/j.issn.2097-0706.2026.04.004

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Heating systems in rural residences in cold and extremely cold regions face both economic and environmental challenges. A real-time heat supply identification model for rural residences based on the principle of conservation of energy was proposed. The model comprehensively considered factors such as the instantaneous heat gain and heat transfer of the building envelope， heat variation of indoor air， and air infiltration load. The model was applied to three rural residences in North China and Northeast China， with physical parameters calibrated using measured data from non-heating seasons. After calibration， the model achieved prediction accuracy for instantaneous indoor temperature with a root mean square error， mean bias error， and range normalized root mean square error of below 6.45%，3.31%，and 8.72%， respectively. The calibrated model was used for data analysis of rural residences during heating season. The results showed that the peak heat supply for rural residences in Northeast China differed significantly from that in North China， with a maximum difference of 72.2 W/m². Additionally， a reduction of 1 ℃ in indoor temperature decreased the heat supply by 12.45% for rural residences in Northeast China and by an average of 12.85% in North China. The findings indicate that predicting real-time heat supply under different indoor temperatures is of great significance for effective heating regulation in rural residences.

Optimized Configuration and Load Regulation

Optimal scheduling of integrated energy systems based on improved CNN-LSTM and multi-objective RIME algorithm

ZHU Lijuan, LIU Jiying, YU Mingzhi, YANG Kaimin, MAO Yudong

2026, 48(4):  35-46.  doi:10.3969/j.issn.2097-0706.2026.04.005

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To improve the forecast accuracy and operation economic efficiency of integrated energy systems under multi-energy coupling conditions， an electric-thermal load forecasting and optimal scheduling method was proposed based on an improved convolutional neural network and long short-term memory neural network（CNN-LSTM）. The method integrated CNN and LSTM， introduced an attention mechanism to enhance the capabilities of feature extraction and temporal response， and achieved the coordinated optimization of the forecasting model and operation strategy in combination with the multi-objective rime optimization algorithm（MORIME）. Simulation results based on typical summer and winter days of a park in Jinan showed that the improved CNN-LSTM model achieved a 32.6% reduction in the mean absolute percentage error of load forecasting compared to that of the traditional model. With MORIME， the total operation costs of the system in summer and winter reduced by 12.7% and 10.3% respectively， and the energy utilization efficiency of the system increased by 3.8% to 4.1%. The efficient coordination of the electric-thermal energy storage system and the time-shift utilization of energy significantly improve the operation economic efficiency and energy utilization efficiency of the system， thereby providing a new technical approach for the optimal scheduling of complex multi-energy systems.

Multi-time-scale robust coordinated optimal scheduling of DC transmission systems in new energy bases

WANG Qixi, HAN Zifen, LIU Kequan, DONG Haiying

2026, 48(4):  47-59.  doi:10.3969/j.issn.2097-0706.2026.04.006

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To address the scheduling issues caused by the instability of wind and photovoltaic outputs in DC transmission systems， a coordinated optimal scheduling method for wind-photovoltaic-thermal-storage DC systems integrating multi-time-scale optimization and adaptive robust optimization was proposed. A two-stage coordinated optimal scheduling model for wind-photovoltaic-thermal-storage DC systems was established with the objective of maximizing comprehensive benefits， including economic， environmental，system and flexibility benefits， subject to constraints such as power balance and energy storage state of charge （SOC）. In the day-ahead planning stage， the objective function and constraints were established， and the multi-objective problem was solved using the non-dominated sorting genetic algorithm Ⅱ. In the intraday rolling optimization stage， a deviation set describing the uncertainty of wind and photovoltaic outputs was constructed， and a min-max robust optimization method was employed. The model was solved using the column-and-constraint generation algorithm to ensure that transmission power met medium- and long-term DC transmission plans under extreme conditions. The simulation results demonstrated that the proposed strategy achieved the lowest comprehensive cost under scenario 4， which was approximately 7% lower than that under scenario 1. The fluctuation amplitude of medium- and long-term transmission power reduced by more than 30%. After intraday rolling optimization， the tracking error of the transmission power was controlled within 2%， and the robust optimization model still obtained a globally optimal solution satisfying hard constraints under the worst-case scenario. The proposed scheduling strategy can maximize comprehensive benefits in the DC transmission systems of new energy bases， ensuring that real-time transmission power closely tracks medium and long-term transmission plans. Furthermore， the robust optimization model can still obtain a globally optimal solution satisfying hard constraints even when considering uncertainty scenarios under the worst-case scenario.

Optimal scheduling of electric vehicles based on schedulable duration prediction

PENG Wenhe, CAI Ruitian, ZHANG Huaying, WANG Hualong, HUANG Huan, WU Yicong

2026, 48(4):  60-71.  doi:10.3969/j.issn.2097-0706.2026.04.007

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With the rapid growth of the number of electric vehicles （EVs）， large-scale uncoordinated charging has exacerbated the peak-valley load differences in distribution networks and degraded power quality， making coordinated charging control critically important. However， existing scheduling methods fail to fully consider the uncertainty in EV charging duration and lack targeted prediction mechanisms. Taking the actual charging data provided by ACM Laboratory in the United States as an example， a hierarchical and clustered coordinated charging control method for EVs based on schedulable duration prediction was proposed. The EV charging duration was predicted， serving as the basis for constructing an EV clustering model. Economic optimal scheduling was performed under power balance and energy storage constraints. Then， power flow verification was conducted in the distribution network， with limit violation information fed back to drive closed-loop re-optimization of the charging station. A power allocation algorithm was employed to achieve coordinated charging and discharging control， ensuring both the security and economic efficiency of system operation. Case simulation results demonstrated that the proposed optimal scheduling method effectively captured EV parking patterns and improved prediction accuracy. The load variance of the charging station decreased by 26.55%， effectively lowering EV charging and discharging costs and enhancing system economic performance.

Design and energy conservation optimization of cold and heat source systems for main control buildings of substations in cold regions

ZHU Weidong, HUANG Shuai, MIAO Wenjie, JIN Xu, Aruna , ZHANG Jiapeng, ZHANG Hao, SHA Shuai

2026, 48(4):  72-80.  doi:10.3969/j.issn.2097-0706.2026.04.008

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In the context of intensifying global climate change， promoting building energy conservation and carbon reduction and achieving zero-energy buildings have become critical measures to address climate change and resource depletion. Substations are indispensable structures in the process of urbanization. Currently， heating systems in substations located in severely cold regions typically rely on energy-intensive electric heating. Therefore， the existing cold and heat source systems were optimized by leveraging the surrounding environmental resources， and feasible schemes for building energy conservation and carbon reduction were proposed. During the research process， CFD methods were employed to demonstrate that the adoption of natural ventilation in the transitional season and summer could achieve a dynamic balance of operating loads. The annual power generation of the solar photovoltaic system could reach 18.75 MW·h. Air source heat pump heating saved 44.50% energy compared to electric heating. Furthermore， the photovoltaic-heat pump hybrid energy supply system achieved an annual CO₂ emission reduction of up to 4.90 t compared to traditional electric heating. The findings validate the engineering feasibility of the photovoltaic power generation and heat pump energy supply technology model for main control buildings of substations in severely cold regions.

Cycle optimization and environmental economic evaluation of transcritical CO₂ heat pump heating systems

HUANG Shuai, XIANG Xinyu, LI Ang, JIN Xu, Aruna , ZHANG Jiapeng

2026, 48(4):  81-89.  doi:10.3969/j.issn.2097-0706.2026.04.009

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As a representative of clean heating， air-source heat pumps can effectively reduce building energy consumption， but their application in cold regions faces technical bottlenecks， and traditional refrigerants have negative impacts on the environment. Therefore， taking the transcritical CO₂ heat pump heating system as the research object， the system performance was simulated and analyzed based on Dymola software. The heating performance of CO₂ single-stage cycle， two-stage cycle and two-stage cycle coupled with mechanical subcooling under different operating conditions was compared and studied. Combined with the annual actual heat supply， environmental analysis and economic evaluation of the system were conducted， and the applicability and sustainability of different system forms were comprehensively discussed. The research results showed that the heating coefficient of performance（COP） of the two-stage cycle was increased by 26% compared with the single-stage cycle. The transcritical CO₂ one-stage throttling intermediate incomplete cooling dual-compression cycle heat pump system + front mechanical subcooling（OTHS+FMC） could effectively reduce the optimal high pressure of the two-stage cycle system. When the evaporation temperature was -30 ℃， its COP could be increased by up to 12.70% compared with the initial transcritical CO₂ heat pump system， with a 5.08% COP improvement under optimal high pressure. The economic analysis showed that the initial investment cost of OTHS+FMC was higher， but the energy efficiency improvement and emission reduction benefits of long-term operation made it more environmentally economical.