Control of Urban Drainage Systems (UDS) is studied for cases in which the distribution of runoff through the channels of a system is inefficient, i.e. when the capacity of some structures is not used optimally. In this paper, a decentralized population-dynamics-based control for UDS is presented, particularly using the replicator and projection dynamics. For the design, a methodology to make a partitioning of the system is introduced, and the design of a population-dynamics-based control per each partition is proposed. Moreover, a stability analysis of the closed-loop system is made by using passivity theory. Finally, simulation results show the proposed approach performance in a segment of the Bogotá stormwater UDS case study.
This manuscript describes the MatSWMM toolbox, an open-source Matlab, Python, and LabVIEW-based software package for the analysis and design of real-time control (RTC) strategies in urban drainage systems (UDS). MatSWMM includes control-oriented models of UDS, and the storm water management model (SWMM) of the US Environmental Protection Agency (EPA), as well as systematic-system edition functionalities. Furthermore, MatSWMM is also provided with a population-dynamics-based controller for UDS with three of the fundamental dynamics, i.e., the Smith, projection, and replicator dynamics. The simulation algorithm, and a detailed description of the features of MatSWMM are presented in this manuscript in order to illustrate the capabilities that the tool has for educational and research purposes.
A hierarchical control strategy is proposed to solve the optimal drainage problem in sewer systems by combining an optimization technique known as minimum scaled consensus control (MSCC) with the deep deterministic policy gradient (DDPG) algorithm. The MSCC strategy operates at the global control level, and is used to determine the flows of the hydraulic structures of the drainage system, such that the water is optimally distributed, i.e., wastewater flows are controlled to minimize saturation of water levels and/or flooding events, filling each of the drainage system components (e.g., pipes, tanks, wastewater treatment plants) proportionally to their capacity. On the other hand, the DDPG uses a model-free approach at the local control level, setting the drainage flows by operating valves and gates, without any knowledge of the inherent dynamics, so that it can be used to handle the nonlinearities of the system. Finally, a case study is presented to show the effectiveness of the proposed strategy.
Modeling transient flow in networked dynamical systems characterized by hyperbolic partial differential equations (PDEs) is essential to engineering applications. Solutions of hyperbolic PDEs are commonly found using the method of characteristics (MOC), particularly when modeling the water hammer phenomenon in water distribution systems (WDSs), which is critical for design and operation. For applications that require fast modeling, existing methods for speeding up traditional MOC simulations either trade off accuracy for simulation time, or do not scale properly due to memory restrictions and prolonged computational times. This work proposes a novel parallel implementation of the MOC for networked systems, which relies on vectorization and distributed parallel computing to evaluate the transient dynamics of WDSs. The proposed method, referred to as distributed and vectorized MOC (DV-MOC), relies on aligned memory allocation for vectorization and distributed-memory parallelization to further accelerate vectorized operations and ensure scalability for arbitrary network topologies. The algorithm has been applied to a WDS from the battle of the sensor networks (BWSN-II) composed by 14,824 pipes and nearly 6 × 10 11 solution points. Through rigorous analyses, we show that the performance of DV-MOC surpasses that of sequential MOC, with speeding up factors in the order of thousands for sufficiently dense numerical grids.
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