论文标题
Astlingen城市排水基准网络的偶然限制的随机MPC
Chance-constrained Stochastic MPC of Astlingen Urban Drainage Benchmark Network
论文作者
论文摘要
在城市排水系统（UDS）中，一种基于模型预测控制（MPC）的实时控制（RTC）是一种验证的降低下水道溢出（CSO）污染的经过验证的方法。文献中UDSS RTC的MPC方法依赖于基于确定性降雨预测的最佳控制策略的计算。但是，实际上，在降雨预测中存在不确定性，这些预测会影响计算最佳控制策略的严重准确性。在这种情况下，这项工作旨在集中于与降雨预测及其影响相关的不确定性。一种选择是使用有关控制器中的降雨事件的随机信息。在使用MPC方法的情况下，可以使用称为随机MPC的类，包括几种方法，例如偶然受限的MPC方法。在这项研究中，我们使用机会受限的方法将随机MPC应用于UDS。此外，我们还将基于雨水预测的不同随机场景的偶然预测和偶然受限的MPC进行了比较经典MPC的操作行为。该应用程序和比较是基于使用Astlingen Urban Drainage基准网络的SWMM模型的模拟。
In urban drainage systems (UDS), a proven method for reducing the combined sewer overflow (CSO) pollution is real-time control (RTC) based on model predictive control (MPC). MPC methodologies for RTC of UDSs in the literature rely on the computation of the optimal control strategies based on deterministic rain forecast. However, in reality, uncertainties exist in rainfall forecasts which affect severely accuracy of computing the optimal control strategies. Under this context, this work aims to focus on the uncertainty associated with the rainfall forecasting and its effects. One option is to use stochastic information about the rain events in the controller; in the case of using MPC methods, the class called stochastic MPC is available, including several approaches such as the chance-constrained MPC method. In this study, we apply stochastic MPC to the UDS using the chance-constrained method. Moreover, we also compare the operational behavior of both the classical MPC with perfect forecast and the chance-constrained MPC based on different stochastic scenarios of the rain forecast. The application and comparison have been based on simulations using a SWMM model of the Astlingen urban drainage benchmark network.
