Integrating Model Predictive Control With Stormwater System Design: A Cost-Effective Method of Urban Flood Risk Mitigation During Heavy Rainfall
文献类型：期刊论文
| 作者 | Sun, Lanxin1,2; Xia, Jun1,3; She, Dunxian1,2 |
| 刊名 | WATER RESOURCES RESEARCH
 |
| 出版日期 | 2024-04-01 |
| 卷号 | 60期号:4页码:18 |
| 关键词 | model predictive control (MPC) stormwater system design model framework urban flooding peak flow |
| ISSN号 | 0043-1397 |
| DOI | 10.1029/2023WR036495 |
| 通讯作者 | Xia, Jun(xiaj@igsnrr.ac.cn) ; She, Dunxian(xiaj@igsnrr.ac.cn) |
| 英文摘要 | The integration of green-gray infrastructures with advanced control approaches is revolutionizing the stormwater system retrofitting, emerging as an innovative strategy to mitigate urban flood risks. However, a major challenge lies in balancing the substantial investments of these infrastructure projects with their environmental benefits, such as reduced flooding volume and lower peak flow. Model predictive control (MPC), a dynamic and intelligent control approach, optimizes these environmental benefits but is underutilized in the system design phase for cost-effectiveness analysis. This study introduces a multi-scenario model framework that incorporates MPC and other control approaches into stormwater system designs, including the implementation of controlled storage tanks and green infrastructures. This framework provides comprehensive modeling tools for practitioners to evaluate the flood control benefits and costs across various infrastructure designs and control scenarios, ultimately identifying solutions that are both environmentally and economically viable. A case study conducted in a small urban catchment area in Shenzhen City, China, demonstrates the effectiveness of this framework. The results indicate that MPC outperforms other control scenarios, particularly under heavy or extreme rainfall conditions. Notably, MPC not only provides superior environmental benefits but also yields considerable cost savings, ranging from 1,787 to 9,371 USD per hectare compared to static control, equating to a 5% reduction in cost relative to rule-based control. Such findings suggest that integrating MPC is a cost-effective alternative to extensive infrastructure expansion for flood management, which significantly enhances the benefit contribution of controlled infrastructures without substantial additional expenses. Implementing advanced control methods for green-gray infrastructures is a new method to reduce urban flooding. However, constructing and updating these infrastructures can be very expensive, which is a significant challenge for many urban areas. Our research explores how to use a smart control approach, specifically the model predictive control (MPC), to enhance environmental benefits and save money in the system design phase. We present a multi-scenario model framework that combines MPC and other methods into the design of stormwater systems, which include controlled storage tanks and green infrastructures. This framework can be used to evaluate the flood control benefits and costs across various infrastructure designs and control scenarios, and to identify the solutions that are both environmentally and economically viable. We conducted a case study in Shenzhen City, China, to test our framework. The results show that MPC is effective particularly during heavy or extreme rainfalls, offering higher environmental benefits and cost savings compared to the scenarios without MPC. Integrating MPC is more cost-effective than expanding infrastructures for flood management as it notably increases the benefit contribution of controlled infrastructures at a modest cost. A framework is proposed to assess the environmental and economic impacts of integrating model predictive control (MPC) with stormwater infrastructure designs Assessments are conducted in a small urban catchment involving heavy rainfall events The MPC yields higher environmental benefits and saves economic costs compared to other control approaches |
| WOS关键词 | REAL-TIME CONTROL ; LOW-IMPACT DEVELOPMENT ; DRAINAGE SYSTEMS ; GREY INFRASTRUCTURE ; WATER-QUALITY ; OPTIMIZATION ; RTC ; PERFORMANCE ; RUNOFF |
| 资助项目 | the Ministry of Water Resources of the People's Republic of China[SKS-2022014] ; Ministry of Water Resources of the People's Republic of China[41890823] ; National Natural Science Foundation of China[XDA23040304] ; Strategic Priority Research Program of Chinese Academy of Sciences ; Urban Planning and Design Institute of Shenzhen |
| WOS研究方向 | Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources |
| 语种 | 英语 |
| WOS记录号 | WOS:001194182300001 |
| 出版者 | AMER GEOPHYSICAL UNION |
| 资助机构 | the Ministry of Water Resources of the People's Republic of China ; Ministry of Water Resources of the People's Republic of China ; National Natural Science Foundation of China ; Strategic Priority Research Program of Chinese Academy of Sciences ; Urban Planning and Design Institute of Shenzhen |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/204022]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Xia, Jun; She, Dunxian |
| 作者单位 | 1.Wuhan Univ, State Key Lab Water Resources & Hydropower Engn Sc, Wuhan, Peoples R China 2.Wuhan Univ, Hubei Key Lab Water Syst Sci Sponge City Construct, Wuhan, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Sun, Lanxin,Xia, Jun,She, Dunxian. Integrating Model Predictive Control With Stormwater System Design: A Cost-Effective Method of Urban Flood Risk Mitigation During Heavy Rainfall[J]. WATER RESOURCES RESEARCH,2024,60(4):18. |
| APA | Sun, Lanxin,Xia, Jun,&She, Dunxian.(2024).Integrating Model Predictive Control With Stormwater System Design: A Cost-Effective Method of Urban Flood Risk Mitigation During Heavy Rainfall.WATER RESOURCES RESEARCH,60(4),18. |
| MLA | Sun, Lanxin,et al."Integrating Model Predictive Control With Stormwater System Design: A Cost-Effective Method of Urban Flood Risk Mitigation During Heavy Rainfall".WATER RESOURCES RESEARCH 60.4(2024):18. |
入库方式： OAI收割
来源：地理科学与资源研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。