热活性化建筑柔性负荷优化控制方法

作者

  • 杨晓晨 天津市建筑环境与能源重点实验室,环境科学与工程学院,天津大学,天津 300072,中国; 安徽建筑大学智能建筑与建筑节能安徽省重点实验室,合肥 230601,中国
  • 王睿智 天津市建筑环境与能源重点实验室,环境科学与工程学院,天津大学,天津 300072,中国
  • 张振亚 安徽建筑大学智能建筑与建筑节能安徽省重点实验室,合肥 230601,中国
  • 王萍 安徽建筑大学智能建筑与建筑节能安徽省重点实验室,合肥 230601,中国
  • 刘定洲 天津市建筑环境与能源重点实验室,环境科学与工程学院,天津大学,天津 300072,中国
  • 江怡萱 天津市建筑环境与能源重点实验室,环境科学与工程学院,天津大学,天津 300072,中国
Article ID: 213
142 Views

DOI:

https://doi.org/10.18686/cncest213

关键词:

TABS;建筑能源柔性;模型预测控制;热惰性;多目标优化

摘要

随着可再生能源在能源系统中的渗透率持续增加,能源供给侧的波动性与随机性不断提升,这对供能侧与需求侧的动态协同提出了新的挑战。建筑自身的热质量具有一定的蓄能潜力,相对于传统的主动蓄能方式,其经济性和潜在体量更为显著。在考虑建筑热惰性的前提下,如何对建筑柔性负荷进行活性化及精准控制成为当前的研究热点。本文基于一个热活性化建筑(Thermally Activated Building System, TABS)的实际案例,分析了建筑需求侧能源柔性潜力的激发方法及限制条件。通过建立多种模型预测控制(Model Predictive Control, MPC)方法,包括白箱MPC、灰箱MPC和黑箱MPC,本文对比分析了不同控制策略的室温控制效果及系统的经济性。与传统的基于规则的控制方法相比,MPC在室内温度控制、运行经济性、柔性负荷利用率上均表现出更优的性能,可有效节约能源成本20%,提高灵活性利用率近40%。此外,对比不同的MPC方法,白箱MPC在室温控制方面表现最优,而灰箱MPC在降低能源成本和提高能源灵活性方面更为有效。本文的结论将为建筑柔性负荷的高效利用以及控制方法的选用提供理论指导。

##submission.downloads##

已出版

2025-01-21

文章引用

杨晓晨, 王睿智, 张振亚, 王萍, 刘定洲, & 江怡萱. (2025). 热活性化建筑柔性负荷优化控制方法. 清洁能源科学与技术, 3(1), 213. https://doi.org/10.18686/cncest213

栏目

原创研究型文章

参考

1. 范丽佳. 中国建筑业碳排放现状及光储直柔碳中和路径. 重庆建筑. 2021; 20(10): 23-25.

2. Fan LJ. Current status of carbon emissions in China’s construction industry and the path to carbon neutrality through “solar energy storage, direct current and flexible power generation” (Chinese). Chongqing Architecture. 2021; 20(10): 23-25.

3. Jensen SØ, Marszal-Pomianowska A, Lollini R, et al. IEA EBC Annex 67 Energy Flexible Buildings. Energy and Buildings. 2017; 155: 25-34. doi: 10.1016/j.enbuild.2017.08.044

4. Hassan MA, Abdelaziz O. Best practices and recent advances in hydronic radiant cooling systems – Part II: Simulation, control, and integration. Energy and Buildings. 2020; 224: 110263. doi: 10.1016/j.enbuild.2020.110263

5. Li T, Merabtine A, Lachi M, et al. Experimental study on the thermal comfort in the room equipped with a radiant floor heating system exposed to direct solar radiation. Energy. 2021; 230: 120800. doi: 10.1016/j.energy.2021.120800

6. Guo J, Dong J, Wang H, et al. On-site measurement of the thermal performance of a novel ventilated thermal storage heating floor in a nearly zero energy building. Building and Environment. 2021; 201: 107993. doi: 10.1016/j.buildenv.2021.107993

7. Le Dréau J, Heiselberg P. Energy flexibility of residential buildings using short term heat storage in the thermal mass. Energy. 2016; 111: 991-1002. doi: 10.1016/j.energy.2016.05.076

8. Foteinaki K, Li R, Heller A, et al. Heating system energy flexibility of low-energy residential buildings. Energy and Buildings. 2018; 180: 95-108. doi: 10.1016/j.enbuild.2018.09.030

9. Chen T. Application of adaptive predictive control to a floor heating system with a large thermal lag. Energy Build. 34(2002): 45–51.

10. Rhee KN, Olesen BW, Kim KW. Ten questions about radiant heating and cooling systems. Building and Environment. 2017; 112: 367-381. doi: 10.1016/j.buildenv.2016.11.030

11. Kattan P, Ghali K, Al-Hindi M. Modeling of under-floor heating systems: A compromise between accuracy and complexity. HVAC&R Research. 2012; 18(3): 468-480. doi: 10.1080/10789669.2012.649881

12. Dussault JM, Sourbron M, Gosselin L. Reduced energy consumption and enhanced comfort with smart windows: Comparison between quasi-optimal, predictive and rule-based control strategies. Energy and Buildings. 2016; 127: 680-691. doi: 10.1016/j.enbuild.2016.06.024

13. 刘羽岱, 潘毅群, 黄治钟. 基于模型预测控制的空调系统运行优化仿真研究. 上海市制冷学会2013年学术年会论文集; 2013年12月18日; 上海, 中国. pp. 413-419.

14. Liu YD, Pan YQ, Huang ZZ. Simulation study on air conditioning system operation optimization based on model predictive control (Chinese). In: Proceedings of the Shanghai Society of Refrigeration 2013 Annual Academic Conference; 18 December 2013; Shanghai, China. pp. 413-419.

15. EnergyPlus weather database. Available online: https://energyplus.net/weather (accessed on 25 July 2024).

16. GB50736-2012 民用建筑供暖通风与空气调节设计规范. 网址: http://www.iwuchen.com/m/show.php?filename=1081 (2024年7月20日访问).

17. GB50736-2012 Design code for heating ventilation and air conditioning of civil buildings. Available online: http://www.iwuchen.com/m/show.php?filename=1081 (accessed on 20 July 2024).

18. Drgoňa J, Arroyo J, Cupeiro Figueroa I, et al. All you need to know about model predictive control for buildings. Annual Reviews in Control. 2020; 50: 190-232. doi: 10.1016/j.arcontrol.2020.09.001

19. Juhl R, Møller JK, Madsen H. ctsmr—Continuous Time Stochastic Modeling in R, 2016.

20. Lofberg J. YALMIP: a toolbox for modeling and optimization in MATLAB. 2004 IEEE International Conference on Robotics and Automation (IEEE Cat No04CH37508).: 284-289. doi: 10.1109/cacsd.2004.1393890

21. Gurobi Optimization. Gurobi Optimizer Reference Manual. Available online: https://www.gurobi.com (accessed on 25 July 2024).