Day-Ahead Flexible Dispatch Method for Ice Thermal Storage Systems in Response to Dual Signals of Carbon and Price

Zhang Pengbo; Lü Shilei; Jia Yanbing; Wang Ran; Wu Zhifu

doi:10.12465/issn.0253-4339.20260804004

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Day-Ahead Flexible Dispatch Method for Ice Thermal Storage Systems in Response to Dual Signals of Carbon and Price

更新时间：2026-09-24

- Day-Ahead Flexible Dispatch Method for Ice Thermal Storage Systems in Response to Dual Signals of Carbon and Price
- Journal of Refrigeration Pages: 1-10(2026)
- 作者机构：
  
  1.天津大学环境科学与工程学院天津 300350
  2. 国网浙江省电力有限公司杭州 310007
- 作者简介：
  
  Lü Shilei， male， professor， School of Environmental Science and Engineering， Tianjin University， 86-18602214455， E-mail： lvshilei@tju.edu.cn. Research fields： thermal/cold and electric energy storage for buildings， urban flexible energy.
- 基金信息：
  
  the National Key R&D Program of China(2023YFC3807100)
- DOI：10.12465/issn.0253-4339.20260804004
  CLC： TB61⁺1;TU831.3
- Received：04 August 2026，
  
  Revised：2026-08-11，
  
  Accepted：21 August 2026，
  
  Online First：24 September 2026，
- 稿件说明：
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张鹏勃，吕石磊，贾艳冰，等. 响应“碳责任-价格”双重信号的冰蓄冷系统日前调度方法［J］. 制冷学报，XXXX，XX（XX）：1-10.

Zhang Pengbo，Lü Shilei，Jia Yanbing，et al. Day-Ahead Flexible Dispatch Method for Ice Thermal Storage Systems in Response to Dual Signals of Carbon and Price［J］. Journal of Refrigeration，XXXX，XX（XX）：1-10.
张鹏勃，吕石磊，贾艳冰，等. 响应“碳责任-价格”双重信号的冰蓄冷系统日前调度方法［J］. 制冷学报，XXXX，XX（XX）：1-10. DOI： 10.12465/issn.0253-4339.20260804004.

Zhang Pengbo，Lü Shilei，Jia Yanbing，et al. Day-Ahead Flexible Dispatch Method for Ice Thermal Storage Systems in Response to Dual Signals of Carbon and Price［J］. Journal of Refrigeration，XXXX，XX（XX）：1-10. DOI： 10.12465/issn.0253-4339.20260804004.

摘要

电力动态碳排放责任因子（

）具有显著时变特征，其与固定时段分时电价的时序错位可能导致冰蓄冷系统经济与低碳调度目标冲突。针对该问题，本文提出一种响应“碳责任-价格”双重信号的冰蓄冷系统日前调度方法。构建计及系统运行收益与碳减排量的多目标优化模型，针对4类典型

场景生成Pareto非支配方案集，并采用熵权-优劣解距离法进行方案评价与经济偏好系数优选。杭州某园区案例

表明：4类

场景优选经济偏好系数分别为0.27、0.20、0.22和0.40。与低碳策略相比，多目标优选策略在保留93.9%~98.3%碳减排效益的同时，使运行收益提高12.7%~107.7%；与经济策略相比，实现了87.7%~95.8%的碳排放责任改善潜力。所提方法可适应“碳-价”的时序错位特征，实现冰蓄冷系统经济与低碳效益的协调。

Abstract

Objective

The dynamic carbon emission responsibility factor of electricity （

） varies considerably over time because of changes in the power generation mix， system load， and unit operational conditions. Its temporal profile might not coincide with the time-of-use electricity prices defined by fixed periods. As a result， low-price periods are not necessarily the same as low-carbon periods， which might create the conflicts between the economic and low-carbon operation of ice thermal storage systems. This study aims to develop a day-ahead dispatch method that can respond simultaneously to the carbon-responsibility and electricity-price signals. The method is designed to coordinate the operational revenue and carbon-reduction performance under different temporal relationships between the two signals.

Methods

An equivalent charging and discharging power model was firstly developed to elucidate the influence of ice storage operation on the user-side electricity demand. The model considers the charging state of the ice storage tank， the operational characteristics of the dual-mode chiller， and auxiliary equipment power consumption. A linear programming model of multi-objective day-ahead mixed-integer was then established. The two objectives were the incremental electricity cost and incremental carbon emission responsibility caused by the ice storage operation. An economic preference coefficient was introduced to balance the normalized economic and low-carbon objectives. A total of 100 preference coefficients ranging from pure low-carbon strategy to pure economic strategy were evaluated for each

scenario. After the duplicate and dominated solutio

ns were removed， the Pareto non-dominated solution sets were obtained. Operational revenue， carbon emission responsibility， and low-carbon matching degree were selected as the performance indicators. The entropy-weighted technique for order preference by similarity to the ideal solution （TOPSIS） was used to evaluate the Pareto solutions and determine the preferred economic preference coefficient. A case study was conducted for an industrial park in Hangzhou using the 2024 time-of-use electricity price and four typical

profiles， i.e.， an overall high-carbon profile， a low-carbon low-variation profile， a single-peak profile， and a double-peak profile.

Results and Discussion

The entropy weights and the preferred economic preference coefficient varied markedly with the temporal characteristics of Cr. For Cr1， the weights of carbon emission responsibility and low-carbon matching degree are 0.40 and 0.36， respectively. Regarding Cr3， where high-Cr periods clearly overlap with the midday low-price period， the low-carbon matching degree receives the highest weight of 0.47. The preferred economic preference coefficients for Cr1-Cr4 are 0.27， 0.20， 0.22， and 0.40， with the recommended ranges of 0.15-0.47， 0.08-0.30， 0.14-0.43， and 0.24-0.47， respectively. Dispatch results further show that the economic strategy tends to charge during the low-price but high-Cr periods in Cr1 and Cr3， whereas the preferred multi-objective strategy shifts charging away from these periods. Compared with the low-carbon strategy， operational revenue are increased by 78.5%， 12.7%， 107.7%， and 14.7% for Cr1-Cr4， while 93.9%-98.3% of the carbon-reduction benefit is retained. The carbon-responsibility improvement potential relative to the economic strategy reaches 87.7%-95.8%， with the low-carbon matching degrees of -0.79 to -0.91.

Conclusions

The proposed day-ahead dispatch method can effectively coordinate the economic and low-carbon operation of ice thermal storage systems under the temporally mismatched carbon and price signals. The four Cr scenarios require different economic preference coefficients， namely， 0.27， 0.20， 0.22， and 0.40， confirming that a fixed preference setting cannot adequately represent different carbon-price coupling conditions. Relative to the low-carbon strategy， the preferred multi-objective strategy retains 93.9%-98.3% of the carbon-reduction benefit while increasing the operational revenue by 12.7%-107.7%. Relative to the economic strategy， it achieves 87.7%-95.8% of the available improvement in the carbon emission responsibility. These results demonstrate that scenario-adaptive preference selection can suppress the high-carbon charging induced by low electricity prices and provide a practical basis for the coordinated carbon-energy interaction on the demand side.

关键词

Keywords

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