Research on the Energy-Economy-Environment Synergistic Optimization of Solar Combined Cooling, Heating and Power System Driven by Multi-Energy Storage

Lin Jianqing; Yao Chunyu; Wang Gang; Shi Huangxia; Zhong Haobai; Jiang Tieliu

doi:10.19799/j.cnki.2095-4239.2026.0247

您当前的位置：

首页 >

文章列表页 >

Research on the Energy-Economy-Environment Synergistic Optimization of Solar Combined Cooling, Heating and Power System Driven by Multi-Energy Storage

更新时间：2026-05-14

- Research on the Energy-Economy-Environment Synergistic Optimization of Solar Combined Cooling, Heating and Power System Driven by Multi-Energy Storage
- Energy Storage Science and Technology Pages: 1-15(2026)
- 作者机构：
  
  1.东北电力大学，吉林省吉林市 132012
  2.新疆工程学院，新疆，乌鲁木齐市 830023
- 作者简介：
- 基金信息：
- DOI：10.19799/j.cnki.2095-4239.2026.0247
  CLC： TK 02
- Received：27 March 2026，
  
  Revised：2026-05-06，
  
  Online First：14 May 2026，
- 稿件说明：
移动端阅览
林建清, 姚春宇, 王刚, 等. 多元储能驱动的太阳能冷热电联供系统能量-经济-环境协同优化研究[J]. 储能科学与技术, XXXX, XX(XX): 1-15.

Lin Jianqing, Yao Chunyu, Wang Gang, et al. Research on the Energy-Economy-Environment Synergistic Optimization of Solar Combined Cooling, Heating and Power System Driven by Multi-Energy Storage[J]. Energy Storage Science and Technology, XXXX, XX(XX): 1-15.
林建清, 姚春宇, 王刚, 等. 多元储能驱动的太阳能冷热电联供系统能量-经济-环境协同优化研究[J]. 储能科学与技术, XXXX, XX(XX): 1-15. DOI： 10.19799/j.cnki.2095-4239.2026.0247.

Lin Jianqing, Yao Chunyu, Wang Gang, et al. Research on the Energy-Economy-Environment Synergistic Optimization of Solar Combined Cooling, Heating and Power System Driven by Multi-Energy Storage[J]. Energy Storage Science and Technology, XXXX, XX(XX): 1-15. DOI： 10.19799/j.cnki.2095-4239.2026.0247.

摘要

针对低碳社区太阳能冷热电联供（CCHP）系统在负荷差异、多能耦合及储能协同方面存在的优化不足，构建了一种多元储能驱动的双层协同优化方法。基于TRNSYS平台建立全年8760h动态仿真模型，面向办公、商业、住宅与医院四类差异化建筑负荷特性，建立了设备容量配置与运行调度优化的双层框架，明确蓄热水箱、相变储热罐与蓄冰槽在不同季节与能流中的功能分工及调度优先级。在此基础上，引入归一化能量-经济-环境综合目标函数，实现多目标协同优化，并通过基准方案对比及电价敏感性分析验证方法有效性。与系统优化前相比，经双层协同优化后，光伏上网率由44%提升至46%，市政购电量降低2.4%；设备容量显著减小，其中采暖相变储热罐与蓄冰槽体积分别减小16.1%和28.7%；系统初投资降低19.8%，费用年值下降14.6%，碳排放降低3.0%。在统一储能配置条件下，相较于单层优化方案，双层协同优化使市政购电量、费用年值和碳排放进一步分别降低4.8%、2.1%和6.8%，综合目标函数值下降3.7%。电价敏感性分析表明，当峰谷价差扩大20%时系统经济性进一步提升，而在价差缩小10%条件下性能仅小幅波动，表明所提方法具有良好的鲁棒性与适应性。研究证实，多元储能系统与建筑差异化负荷的精准耦合的可有效削减系统冗余容量、提升能源利用效率，双层协同优化机制能够在不显著改变外部能源输入的前提下，实现系统经济性与环境性的协同提升，为社区级太阳能CCHP系统的优化设计与工程应用提供了坚实的理论支撑与技术参考。

Abstract

To address the limitations of solar combined cooling

heating and power (CCHP) systems in low-carbon communities

particularly regarding load heterogeneity adaptation

multi-energy coupling coordination

and energy storage system scheduling optimization

a bi-level collaborative optimization method driven by multi-energy storage is proposed. An annual 8760-hour dynamic simulation model is established using the TRNSYS platform. Considering the differentiated load characteristics of four typical building types (office

commercial

residential

and hospital)

a bi-level framework integrating equipment capacity configuration and operational scheduling optimization is constructed. The functional division and scheduling priority of hot water storage tanks

phase-change thermal storage tanks

and ice storage tanks are clarified under different seasons and energy flow scenarios. On this basis

a normalized energy-economy-environment comprehensive objective function is introduced to achieve multi-objective collaborative optimization

and the effectiveness and feasibility of the proposed optimization method are verified through baseline scheme comparison and electricity price sensitivity analysis.Compared with the system before optimization

the bi-level collaborative optimization increased the photovoltaic grid feed-in rate from 44% to 46% and reduced the municipal electricity purchase by 2.4%. The equipment capacities were significantly reduced: the volumes of the heating phase-change thermal storage tank and the ice storage tank decreased by 16.1% and 28.7%

respectively. The initial system investment was reduced by 19.8%

the annualized cost decreased by 14.6%

and the carbon emission was lowered by 3.0%. Under the same energy storage configuration

compared with the single-layer optimization scheme

the bi-level collaborative optimization further reduced the municipal electricity purchase

the annualized cost

and the carbon emission by 4.8%

2.1%

and 6.8%

respectively

and the comprehensive objective function value decreased by 3.7%. Electricity price sensitivity analysis indicates that the system exhibits strong robustness under electricity price fluctuations: when the peak-valley price difference increases by 20%

the system's economic performance is further improved; when the peak-valley price difference decreases by 10%

only slight fluctuations occur in various system performance indicators

demonstrating good environmental adaptability of the proposed method. The research confirms that the precise coupling of multi-energy storage systems with the differentiated building loads can effectively reduce the redundant system capacity and improve energy utilization efficiency. The bi-level collaborative optimization mechanism can realize the coordinated improvement of economic and environmental performance without significantly changing the external energy input

providing a solid theoretical support and technical reference for the optimal design and engineering application of community-scale solar CCHP systems.

关键词

Keywords

references

IPCC. Climate Change 2021: The Physical Science Basis[R]. 2021.

国家能源局. 2025年1-9月份全国电力工业统计数据[EB/OL]. 北京: 国家能源局, [2025-10-26]. https://www.nea.gov.cn/2025-10/26/c_1310721365.htm.

National Energy Administration. National Electric Power Industry Statistics for January-September 2025[EB/OL]. Beijing: National Energy Administration, [2025-10-26]. https://www.nea.gov.cn/2025-10/26/c_1310721365.htm.

Fang J, Yang M, Fan Y, et al. Thermodynamic evaluation of a combined cooling, heating, hydrogen, and power multi-generation system for full-spectrum solar energy utilization[J]. Energy Conversion and Management, 2024, 300: 118019.

Su Z, Yang L, Wang H, et al. Solar-assisted combined cooling and power system integrating energy storage and desulfurization for coal-fired power plants[J]. Thermal Science and Engineering Progress, 2023, 45: 102110.

Alharthi M A, Khaliq A, Alqaed S, et al. Investigation of new combined cooling, heating and power system based on solar thermal power and single-double-effect refrigeration cycle[J]. Energy Reports, 2023, 9: 289-309.

Yang X, Zhong D, Zeng K, et al. Performance analysis of a novel biomass thermochemical conversion cascade utilization system driven by concentrated solar energy[J]. Energy, 2025, 323: 135803.

Lepiksaar K, Kajandi G M, Sukumaran S, et al. Optimizing solar energy integration in Tallinn's district heating and cooling systems[J]. Smart Energy, 2025, 17: 100166.

Toker S C. A Comprehensive Thermodynamic Assessment of an Innovative Solar-Geothermal Driven Multigeneration System for Sustainable Production of Energy, Hydrogen, Freshwater, Heating and Cooling[J]. Renewable Energy, 2025: 123571.

Li T, Qin H, Wang J, et al. Energetic and exergetic performance of a novel polygeneration energy system driven by geothermal energy and solar energy for power, hydrogen and domestic hot water[J]. Renewable Energy, 2021, 175: 318-336.

Huang Z F, Chen W D, Wan Y D, et al. Techno-economic comparison of different energy storage configurations for renewable energy combined cooling heating and power system[J]. Applied Energy, 2024, 356: 122340.

Li M, Li H, Zhai X, et al. Study of a solar-wind-hydrogen-gas-grid multi-energy system with CCHP distributed cooperative operation[J]. Applied Thermal Engineering, 2025, 280: 128562.

Jiang R, Yang X. Performance analysis and application of a novel combined cooling, heating and power system integrated with multi-energy storage system[J]. Journal of Energy Storage, 2024, 86: 111276.

Ye J, Dong Q, Yang G, et al. Multi-objective optimal configuration of CCHP system containing hybrid electric-hydrogen energy storage system[J]. Energy Informatics, 2024, 7(1): 111.

Wang Z, Cai W, Tao H, et al. Research on capacity and strategy optimization of combined cooling, heating and power systems with solar photovoltaic and multiple energy storage[J]. Energy Conversion and Management, 2022, 268: 115965.

Li Y, Wang J, Zhou Y, et al. Multi-dimension day-ahead scheduling optimization of a community-scale solar-driven CCHP system with demand-side management[J]. Renewable and Sustainable Energy Reviews, 2023, 185: 113654.

Liu C, Wang H, Liu Z, et al. Research on a Bi-Level Collaborative Optimization Method for Planning and Operation of Multi-Energy Complementary Systems[J]. Energies, 2021, 14(23): 7930.

Zeng S, Zhang H, Wang F, et al. Two-Stage Optimization Scheduling of Integrated Energy Systems Considering Demand Side Response[J]. Energies, 2024, 17(20): 5060.

Zhou J, Zhang L, Zhang G. Two-Level Optimization Model of Integrated Energy System Based on Dynamic Pricing Mechanism[J]. IEEE Transactions on Industry Applications, 2024, 60(1): 1048-1057.

Yuan J, Gang W, Xiao F, et al. Two-level collaborative demand-side management for regional distributed energy system considering carbon emission quotas[J]. Journal of Cleaner Production, 2024, 434: 140095.

Wang Z, GAO Y. Optimization of Distributed Photovoltaic Energy Storage System Double-Layer Planning in Low-Carbon Parks Considering Variable Operating Conditions and Complementary Synergy of Energy Storage Devices[J]. Energies, 2025, 18(8): 1881.

Wang J D, Chen B Y, Chen YB, et al. Bi-level sizing optimization of a distributed solar hybrid CCHP system considering economic, energy, and environmental objectives[J]. INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2023, Vol. 145: 108684.

中华人民共和国住房和城乡建设部. 公共建筑节能设计标准: GB 50189-2015[S]. 北京: 中国建筑工业出版社, 2015.

Ministry of Housing and Urban-Rural Development of the People's Republic of China. Design standard for energy efficiency of public buildings: GB 50189-2015[S]. Beijing: China Architecture & Building Press, 2015.

Gao Y, Deng Y, Yao W, et al. Optimization of combined cooling, heating, and power systems for rural scenario based on a two-layer optimization model[J]. Journal of Building Engineering, 2022, 60: 105217.

禹丽爽,曾渊,李南帆,等. 低碳社区清洁能源冷热电联供系统优化研究[J]. 建筑科学,2023,39(6): 197-205.

Yu L S, Zeng Y, Li N F, et al. Optimal Design of an Integrated Clean Energy CCHP System for Low-Carbon Community Energy Supply[Z]. Building Science, 2023, 06 - 0197 - 09

Moser I. Hooke-Jeeves revisited[C] //2009 IEEE Congress on Evolutionary Computation. Trondheim, Norway: IEEE, 2009: 2670 - 2676[ 2025 - 10 - 30].

机械工业信息研究院编. 2016机电产品报价手册通用设备分册上[M]. 北京:机械工业出版社,2016.

MACHINERY INDUSTRY INFORMATION INSTITUTE OF CHINA. 2016 mechanical and electrical products quotation handbook: General equipment volume, Part 1[M]. Beijing: China Machine Press, 2016.

隆众资讯. 国内石蜡挂牌价格一览表 [EB/OL]. 上海: 隆众石化网[2023版]. https://www.oilchem.net.

LONGZHONG INFORMATION. List of domestic paraffin wax listed prices[EB/OL]. Shanghai: Oilchem.net, [2023]. https://www.oilchem.net.

国家发展改革委, 建设部发布. 建设项目经济评价方法与参数第3版[M]. 北京:中国计划出版社,2021.

NATIONAL DEVELOPMENT AND REFORM COMMISSION, MINISTRY OF CONSTRUCTION OF THE PEOPLE'S REPUBLIC OF CHINA. Methods and parameters for economic evaluation of construction projects[M]. 3rd ed. Beijing: China Planning Press, 2021.

李智勇,黄滔,陈少淼,等. 约束优化进化算法综述[J]. 软件学报,2017, 28(6): 1529-1546.

LI Z Y, HUANG T, CHEN S M, et al. A review of constrained optimization evolutionary algorithms[J]. Journal of Software, 2017, 28(6): 1529-1546.

中华人民共和国生态环境部, 国家统计局. 2021年电力二氧化碳排放因子[DS]. 北京: 中华人民共和国生态环境部, 2024.

Ministry of Ecology and Environment of the People's Republic of China, & National Bureau of Statistics. (2024). 2021 Electricity Carbon Dioxide Emission Factors[Data set]. Beijing: Ministry of Ecology and Environment of the People's Republic of China.

Pedram H, Javad E, Pouria A, et al. Evaluation and sizing of a CCHP system for a commercial and office buildings[J]. Journal of Building Engineering, 2016, 5: 67-78.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Coordinated planning of carbon-neutral campus integrated energy system incorporating multi-energy storage

Design and performance of high-efficiency phase change cold storage plate

A grid-side energy storage system optimization method based on improved twin deep deterministic policy gradient and adaptive distributed model predictive control

Research on collaborative optimization model of power metering and energy storage based on demand response

Collaborative optimization of shared energy storage based on prosumers’ decision-making in a stochastic game framework

Related Author

DU Xiaoze

WANG Xi

DOU Yanzhe

XU Baoping

QIAN Gao

GUO Chan

FANG Xiang

WANG Weiling

Related Institution

School of Energy Power and Mechanical Engineering, North China Electric Power University

Jiangsu Jinhe Energy Technology Company Limited

Research Institute of the Ministry of Civil Affairs

State Grid Xinjiang Electric Power Co.,Ltd.

Electric Power Research Institute, State Grid Xinjiang Electric Power Co., Ltd.

⁰