冷库蓄冷系统配置与运行策略协同优化研究

肖楚鹏; 牟旭东; 许静; 陈丽娜; 丁胜; 胡文博

doi:10.19799/j.cnki.2095-4239.2026.0109

您当前的位置：

首页 >

文章列表页 >

冷库蓄冷系统配置与运行策略协同优化研究

储热储冷多场景应用专刊 | 更新时间：2026-07-01

- 冷库蓄冷系统配置与运行策略协同优化研究
- Bi-level optimization of configuration and operation strategy for refrigerated warehouse with cold thermal energy storage under time-of-use tariffs
- 储能科学与技术 2026年15卷第3期页码：920-932
- 作者机构：
  
  1.国网电力科学研究院武汉能效测评有限公司，湖北武汉 430074
  2.国网电力科学研究院有限公司，江苏南京 210000
  3.国网甘肃省电力公司天水供电公司，甘肃天水 741000
  4.国网甘肃省电力公司平凉供电公司，甘肃平凉 744000
- 作者简介：
  
  肖楚鹏（1983—），男，硕士，高级工程师，研究方向为电力需求侧管理，E-mail：181259409@qq.com；
  胡文博，工程师，研究方向为电碳协同、负荷管理，E-mail：2698687225@qq.com。
- 基金信息：
  
  国家电网有限公司总部管理科技项目“面向分布式光伏与农业冷链的时空匹配就地消纳研究与应用”(5400-202433204A-1-1-ZN)
- DOI：10.19799/j.cnki.2095-4239.2026.0109
  中图分类号： TK 124
- 收稿：2026-01-30，
  
  修回：2026-03-04，
  
  纸质出版：2026-03-28
- 稿件说明：
移动端阅览
肖楚鹏, 牟旭东, 许静, 等. 冷库蓄冷系统配置与运行策略协同优化研究[J]. 储能科学与技术, 2026, 15(3): 920-932.

XIAO Chupeng, MOU Xudong, XU Jing, et al. Bi-level optimization of configuration and operation strategy for refrigerated warehouse with cold thermal energy storage under time-of-use tariffs[J]. Energy Storage Science and Technology, 2026, 15(3): 920-932.
肖楚鹏, 牟旭东, 许静, 等. 冷库蓄冷系统配置与运行策略协同优化研究[J]. 储能科学与技术, 2026, 15(3): 920-932. DOI： 10.19799/j.cnki.2095-4239.2026.0109.

XIAO Chupeng, MOU Xudong, XU Jing, et al. Bi-level optimization of configuration and operation strategy for refrigerated warehouse with cold thermal energy storage under time-of-use tariffs[J]. Energy Storage Science and Technology, 2026, 15(3): 920-932. DOI： 10.19799/j.cnki.2095-4239.2026.0109.

摘要

为提升冷库在分时电价环境下的运行经济性与负荷调节能力，本研究针对传统冷库制冷系统缺乏容量配置与运行策略协同设计的问题，提出一种面向冷库制冷-蓄冷系统的设备配置与运行策略双层协同优化方法。以一中型冷库为研究对象，构建包含双工况制冷主机与相变蓄冷装置的冷库制冷-蓄冷系统模型。上层优化以系统全生命周期成本最小化为目标，采用遗传算法确定制冷主机与蓄冷装置的最优容量配置；下层优化以典型日运行成本最小化为目标，建立混合整数线性规划模型，对分时电价条件下系统运行策略进行优化，并将运行结果反馈至上层形成迭代求解机制。结果表明，在现行分时电价条件下，优化后系统折现运行成本较基准方案降低30%，折现全生命周期成本为5.23万元，动态投资回收期为8.01年。进一步分析不同峰谷电价比场景发现，随着峰谷电价比由1.45提升至9.25，系统全生命周期成本下降至4.03万元，动态投资回收期缩短至3.29年，蓄冷装置最优配置容量显著增加。研究结果可为分时电价机制下冷库蓄冷系统的容量配置与运行策略制定提供定量参考。

Abstract

To enhance the operational economic performance and load flexibility of cold storage facilities under time-of-use electricity pricing

this study developed a bi-level optimization framework for the capacity configuration and operational strategy of a refrigerated warehouse cold thermal energy storage system. A medium-sized refrigerated warehouse was selected as the case study

and system models including a dual-mode chiller unit and a phase-change thermal energy storage device were established. At the upper level

lifecycle cost minimization was adopted as the optimization objective

and a genetic algorithm was employed to determine the optimal capacities of the chiller unit and thermal energy storage device. At the lower level

a mixed-integer linear programming model was developed to minimize the typical-day operating cost through optimal scheduling under time-of-use electricity pricing

and the operational results were iteratively fed back to the upper level. The results indicated that

under current electricity pricing conditions

the optimized system reduced the discounted operational cost by 30% compared with the baseline scheme

achieving a discounted lifecycle cost of 52.3 thousand CNY and a dynamic payback period of 8.01 years. Further analysis of different peak-valley electricity price ratios showed that

as the ratio increased from 1.45 to 9.25

the lifecycle cost decreased to 40.3 thousand CNY and the dynamic payback period shortened to 3.29 years

while the optimal thermal storage capacity increased significantly. These findings provide quantitative support for capacity planning and operational strategy development of cold storage refrigeration-thermal energy storage systems under time-of-use electricity pricing.

关键词

Keywords

references

农业农村部食物与营养发展研究所. 2022年中国食物与营养发展报告[R]. 北京: 中国农业科学技术出版社, 2023.

Institute of Food and Nutrition Development, Ministry of Agriculture and Rural Affairs. China food and nutrition development report 2022[R]. Beijing: China Agricultural Science and Technology Press, 2023.

LI Y L, DARKO A. Incentive mechanisms and decision-making behaviors in warehouse energy efficiency retrofits: A tripartite evolutionary game analysis[J]. Energy and Buildings, 2025, 349: 116578. DOI:10.1016/j.enbuild.2025.116578.

庄贵阳. 我国实现 "双碳" 目标面临的挑战及对策[J]. 人民论坛, 2021(18): 50-53. DOI:10.3969/j.issn.1004-3381.2021.18.012.

ZHUANG G Y. Challenges and countermeasures for China to achieve the goal of "double carbon"[J]. People's Tribune, 2021(18): 50-53. DOI:10.3969/j.issn.1004-3381.2021.18.012.

游辉, 谢晶. 低温相变蓄冷材料及其应用于冷链的研究进展[J]. 食品与发酵工业, 2021, 47(18): 287-293. DOI:10.13995/j.cnki.11-1802/ts.026944.

YOU H, XIE J. Research progress of low temperature phase change storage materials and their applications in cold chain[J]. Food and Fermentation Industries, 2021, 47(18): 287-293. DOI:10.13995/j.cnki.11-1802/ts.026944.

LI G, HWANG Y, RADERMACHER R. Review of cold storage materials for air conditioning application[J]. International Journal of Refrigeration, 2012, 35(8): 2053-2077. DOI:10.1016/j.ijrefrig. 2012.06.003.

FARAJ K, KHALED M, FARAJ J, et al. Phase change material thermal energy storage systems for cooling applications in buildings: A review[J]. Renewable and Sustainable Energy Reviews, 2020, 119: 109579. DOI:10.1016/j.rser.2019.109579.

SANAYE S, SHIRAZI A. Thermo-economic optimization of an ice thermal energy storage system for air-conditioning applications[J]. Energy and Buildings, 2013, 60: 100-109. DOI:10.1016/j.enbuild.2012.12.040.

SONG X, ZHU T, LIU L C, et al. Study on optimal ice storage capacity of ice thermal storage system and its influence factors[J]. Energy Conversion and Management, 2018, 164: 288-300. DOI:10.1016/j.enconman.2018.03.007.

ODUFUWA O Y, KUSAKANA K, NUMBI B P, et al. Optimal energy management of grid-connected PV for HVAC cooling with ice thermal storage system[J]. Journal of Energy Storage, 2024, 77: 109844. DOI:10.1016/j.est.2023.109844.

江燕涛. 动态冰浆蓄冷湿冷技术在果蔬冷藏中的应用前景分析[J]. 制冷与空调, 2015, 15(10): 5-9, 36.

JIANG Y T. Analysis on application prospect of dynamic ice slurry storage humid cool refrigeration technology in fruit and vegetable storage[J]. Refrigeration and Air-Conditioning, 2015, 15(10): 5-9, 36.

孙静, 陈全, 王萍, 等. 乙二醇蓄冷库性能及其在黄冠梨保鲜上的应用[J]. 农业工程学报, 2018, 34(8): 276-282.

SUN J, CHEN Q, WANG P, et al. Performance of cold storage with ethylene glycol as secondary refrigerant and its application on preservation of 'Huangguan' pear[J]. Transactions of the Chinese Society of Agricultural Engineering, 2018, 34(8):276-2 82.

ZHU K, LI X Q, CAMPANA P E, et al. Techno-economic feasibility of integrating energy storage systems in refrigerated warehouses[J]. Applied Energy, 2018, 216: 348-357. DOI:10.1016/j.apenergy. 2018.01.079.

SHAIBANI A R, KESHTKAR M M, TALEBIZADEH SARDARI P. Thermo-economic analysis of a cold storage system in full and partial modes with two different scenarios: A case study[J]. Journal of Energy Storage, 2019, 24: 100783. DOI:10.1016/j.est. 2019.100783.

SHIRAZI A, TAYLOR R A, MORRISON G L, et al. A comprehensive, multi-objective optimization of solar-powered absorption chiller systems for air-conditioning applications[J]. Energy Conversion and Management, 2017, 132: 281-306. DOI:10.1016/j.enconman.2016. 11.039.

HU L, LIU Y F, WANG D J, et al. Feasibility analysis and feature comparison of cold thermal energy storage for off-grid PV air-conditioned buildings in the tropics[J]. Energy Conversion and Management, 2022, 254: 115176. DOI:10.1016/j.enconman.20 21.115176.

商业部设计院. 冷库制冷设计手册[M]. 北京: 农业出版社, 1991: 167-196.

王森, 刘琛, 邢帅杰. K-means聚类算法研究综述[J]. 华东交通大学学报, 2022, 39(5): 119-126.

WANG S, LIU C, XING S J. Review on K-means clustering algorithm[J]. Journal of East China Jiaotong University, 2022, 39(5): 119-126.

YANG M X, WANG J J, ZHOU Y, et al. Optimal configuration of integrated energy system based on multiple energy storage considering source-load uncertainties under different risk tendencies[J]. Journal of Energy Storage, 2025, 109: 115220. DOI:10.1016/j.est.2024.115220.

ZANG X Y, LI H X, WANG S W. Optimal design of energy-flexible distributed energy systems and the impacts of energy storage specifications under evolving time-of-use tariff in cooling-dominated regions[J]. Journal of Energy Storage, 2023, 72: 108462. DOI:10.1016/j.est.2023.108462.

周雨杰, 黄晓燕, 李博, 等. 复叠式冰浆源热泵系统多目标优化设计研究[J/OL]. 中国电机工程学报, 1-16[2026-01-21]. https://link.cnki.net/urlid/11.2107.TM.20251231.1314.004.

ZHOU Y J, HUANG X Y, Li B, et al. Optimal design of a cascade ice slurry source heat pump system using a multi-objective optimization strategy[J]. Proceedings of the CSEE, 1-16[2026-01-21]. https://link.cnki.net/urlid/11.2107.TM.20251231.1314.004.

国网北京市电力公司. 2025年1月国网北京市电力公司代理购电价格公示表[EB/OL]. (2024-12-28) [2026-01-21]. http://www.bj.sgcc.com.cn.

BLANK J, DEB K. Pymoo: Multi-objective optimization in Python[J]. IEEE Access, 2020, 8: 89497-89509. DOI:10.1109/ACCESS. 2020.2990567.

Gurobi Optimization. Gurobi optimizer reference manual[EB/OL]. (2024-01-01) [2026-01-25]. https://www.gurobi.com.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

混合储能辅助火电机组AGC调频优化配置双层模型

面向风光驱动的固体储热砖耦合供热系统动态建模与典型日运行特性分析

钠离子电池固体电解质界相的溶解性及电解液改善策略

排布构型对锂电池组热失控传播的影响研究

纳米颗粒对Sn-Bi-Zn储热合金显微组织及热物理性能的影响