Leveraging Open Source Software and Parallel Computing for Model Predictive Control Simulation of Urban Drainage Systems Using EPA-SWMM5 and Python
Leveraging Open Source Software and Parallel Computing for Model Predictive Control Simulation of Urban Drainage Systems Using EPA-SWMM5 and Python
复制标题
利用开源软件和并行计算，使用 EPA-SWMM5 和 Python 进行城市排水系统的模型预测控制仿真
DOI：
10.1007/978-3-319-99867-1_170
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Sadler J.M., Goodall J.L.
中科院分区：
文献类型：
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作者：
Sadler J.M., Goodall J.L.
由于气候变化和随之而来的海平面上升，主动控制雨水系统是解决地势低洼、地势低洼的沿海城市街道洪水增加的潜在解决方案。模型预测控制 (MPC) 已被证明是一种成功的控制策略，尤其适用于城市排水管理。本研究描述并演示了使用开源软件（Python 和美国环境保护局 (EPA) 雨水管理模型 (SWMM5)）在城市排水系统中实现 MPC。该系统使用一个简化的用例进行演示，其中模拟了滞留池的主动控制出水口。蓄水池出水口的控制会影响下游节点的洪水风险。对于 SWMM5 模型中的每个步骤，都会评估一系列控制出水口的策略。然后使用进化算法选择最佳策略。根据目标函数对策略进行评估，该函数主要惩罚洪水，其次惩罚滞留池水位与目标水位的偏差。免费提供的 Python 库为 MPC 工作流程提供了关键功能：逐步运行 SWMM5 模拟、进化算法实现以及利用并行计算。滞留池的水位更接近目标水位（与基于规则的方法不同）。
The active control of stormwater systems is a potential solution to increased street flooding in low-lying, low-relief coastal cities due to climate change and accompanying sea level rise. Model predictive control (MPC) has been shown to be a successful control strategy generally and as well as for managing urban drainage specifically. This research describes and demonstrates the implementation of MPC for urban drainage systems using open source software (Python and The United States Environmental Protection Agency (EPA) Storm Water Management Model (SWMM5). The system was demonstrated using a simplified use case in which an actively-controlled outlet of a detention pond is simulated. The control of the pond’s outlet influences the flood risk of a downstream node. For each step in the SWMM5 model, a series of policies for controlling the outlet are evaluated. The best policy is then selected using an evolutionary algorithm. The policies are evaluated against an objective function that penalizes primarily flooding and secondarily deviation of the detention pond level from a target level. Freely available Python libraries provide the key functionality for the MPC workflow: step-by-step running of the SWMM5 simulation, evolutionary algorithm implementation, and leveraging parallel computing. For perspective, the MPC results were compared to results from a rule-based approach and a scenario with no active control. The MPC approach produced a control policy that largely eliminated flooding (unlike the scenario with no active control) and maintained the detention pond’s water level closer to a target level (unlike the rule-based approach).
使用 SWMMH 进行模型预测控制
DOI：
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发表时间：
2011
期刊：
影响因子：
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作者：
Steffen Heusch;M. Ostrowski
通讯作者：
M. Ostrowski