Optimizing the Control of Decentralized Rainwater Harvesting Systems for Reducing Urban Drainage Flows

Abstract

The practice of rainwater harvesting (RWH) has been studied extensively in recent years, as it has the potential to alleviate some of the increasing stress on urban water distribution systems and drainage networks. Within the field, an approach of real-time control of rainwater storage is emerging as a method to improve the ability of RWH systems to reduce runoff and urban drainage flows. As applying real-time control on RWH tanks means releasing water that could be used for supply, applying controlled-release policies often hinders the RWH system’s ability to supply water. The suggested study presents an approach that has the potential to improve the capability of a distributed network of RWH systems to mitigate peak drainage flows while substantially reducing the impact on harvested rainwater availability. The suggested method uses a genetic algorithm to generate release policies, which are tailored for any given rain event and initial conditions. The algorithm utilizes the modeled drainage system’s response to a given rainfall pattern and manages to substantially reduce peak drainage flows with little impact on available rainwater when compared to the conventional no-release alternative and other active release methods.