Demand Flexibility of Pre-Cooling Strategies for City-Scale Buildings Through Urban Building Energy Modeling
With the increasing demand for electricity, it is causing a growing burden on the power grid. In order to alleviate the pressure on the power system, a series of demand response (DR) strategies have emerged. This paper studied the DR potential and energy flexibility on city-scale building clusters u...
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MDPI AG
2025-03-01
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| Online Access: | https://www.mdpi.com/2075-5309/15/7/1051 |
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| author | Anni Xu Chengcheng Song Wenxian Zhao Yixing Chen |
| author_facet | Anni Xu Chengcheng Song Wenxian Zhao Yixing Chen |
| author_sort | Anni Xu |
| collection | DOAJ |
| description | With the increasing demand for electricity, it is causing a growing burden on the power grid. In order to alleviate the pressure on the power system, a series of demand response (DR) strategies have emerged. This paper studied the DR potential and energy flexibility on city-scale building clusters under pre-cooling combined with temperature reset. This study firstly selected 18 types of buildings, each containing three construction years as prototype buildings, to represent the 228,539 buildings in Shenzhen. Then several pre-cooling strategies were developed, and after comparative analysis, the optimal strategy was obtained and applied to the entire Shenzhen building cluster, with simulation and analysis conducted for the nine administrative districts. Among them, this paper used AutoBPS-DR and added pre-cooling code based on the Ruby language to automatically generate building models with DR strategies and finally simulated the energy consumption results by EnergyPlus. The results showed that a pre-cooling duration of 0.5 h and a change of 2 °C in both pre-cooling temperature and reset temperature was the optimal strategy. Under this strategy, small and medium prototype buildings can achieve better results, with a maximum load reduction of 23.89 W/m<sup>2</sup> and a reduction rate of 56.82%. In the simulation results of the building cluster, Guangming District showed the best results. Finally, the peak electricity reduction amount and reduction rate of the entire building cluster were calculated to be 0.007 kWh/m<sup>2</sup> and 21.87%, respectively, with the maximum cost saving and saving percentage of 0.081 CNY/m<sup>2</sup> and 15.05%, respectively. From this, it can be seen that the Shenzhen building cluster had shown considerable DR potential under the pre-cooling strategy. |
| format | Article |
| id | doaj-art-c2bd5c3dd4554f608fc7fcf6cc6e9a80 |
| institution | DOAJ |
| issn | 2075-5309 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
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| series | Buildings |
| spelling | doaj-art-c2bd5c3dd4554f608fc7fcf6cc6e9a802025-08-20T03:06:29ZengMDPI AGBuildings2075-53092025-03-01157105110.3390/buildings15071051Demand Flexibility of Pre-Cooling Strategies for City-Scale Buildings Through Urban Building Energy ModelingAnni Xu0Chengcheng Song1Wenxian Zhao2Yixing Chen3College of Civil Engineering, Hunan University, Changsha 410082, ChinaCollege of Civil Engineering, Hunan University, Changsha 410082, ChinaCollege of Civil Engineering, Hunan University, Changsha 410082, ChinaCollege of Civil Engineering, Hunan University, Changsha 410082, ChinaWith the increasing demand for electricity, it is causing a growing burden on the power grid. In order to alleviate the pressure on the power system, a series of demand response (DR) strategies have emerged. This paper studied the DR potential and energy flexibility on city-scale building clusters under pre-cooling combined with temperature reset. This study firstly selected 18 types of buildings, each containing three construction years as prototype buildings, to represent the 228,539 buildings in Shenzhen. Then several pre-cooling strategies were developed, and after comparative analysis, the optimal strategy was obtained and applied to the entire Shenzhen building cluster, with simulation and analysis conducted for the nine administrative districts. Among them, this paper used AutoBPS-DR and added pre-cooling code based on the Ruby language to automatically generate building models with DR strategies and finally simulated the energy consumption results by EnergyPlus. The results showed that a pre-cooling duration of 0.5 h and a change of 2 °C in both pre-cooling temperature and reset temperature was the optimal strategy. Under this strategy, small and medium prototype buildings can achieve better results, with a maximum load reduction of 23.89 W/m<sup>2</sup> and a reduction rate of 56.82%. In the simulation results of the building cluster, Guangming District showed the best results. Finally, the peak electricity reduction amount and reduction rate of the entire building cluster were calculated to be 0.007 kWh/m<sup>2</sup> and 21.87%, respectively, with the maximum cost saving and saving percentage of 0.081 CNY/m<sup>2</sup> and 15.05%, respectively. From this, it can be seen that the Shenzhen building cluster had shown considerable DR potential under the pre-cooling strategy.https://www.mdpi.com/2075-5309/15/7/1051peak demand reductionpre-cooling strategyurban building energy modelingdemand responsetime-of-use electricity price |
| spellingShingle | Anni Xu Chengcheng Song Wenxian Zhao Yixing Chen Demand Flexibility of Pre-Cooling Strategies for City-Scale Buildings Through Urban Building Energy Modeling Buildings peak demand reduction pre-cooling strategy urban building energy modeling demand response time-of-use electricity price |
| title | Demand Flexibility of Pre-Cooling Strategies for City-Scale Buildings Through Urban Building Energy Modeling |
| title_full | Demand Flexibility of Pre-Cooling Strategies for City-Scale Buildings Through Urban Building Energy Modeling |
| title_fullStr | Demand Flexibility of Pre-Cooling Strategies for City-Scale Buildings Through Urban Building Energy Modeling |
| title_full_unstemmed | Demand Flexibility of Pre-Cooling Strategies for City-Scale Buildings Through Urban Building Energy Modeling |
| title_short | Demand Flexibility of Pre-Cooling Strategies for City-Scale Buildings Through Urban Building Energy Modeling |
| title_sort | demand flexibility of pre cooling strategies for city scale buildings through urban building energy modeling |
| topic | peak demand reduction pre-cooling strategy urban building energy modeling demand response time-of-use electricity price |
| url | https://www.mdpi.com/2075-5309/15/7/1051 |
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