Abstract
It is widely acknowledged that distributed water systems (DWSs), which
integrate distributed water supply and treatment with existing
centralized infrastructure, can mitigate challenges to water security
from extreme events, climate change, and aged infrastructure. However,
there is a knowledge gap in finding beneficial DWS configurations, i.e.,
where and at what scale to implement distributed water supply. We
develop a meso-scale representation model that approximates DWSs with
reduced backbone networks, which enable efficient system emulation while
preserving key physical realism. Moreover, system emulation allows us to
build a multi-objective optimization model for computational policy
search that addresses energy utilization and economic impacts. We
demonstrate our models on a hypothetical DWS with distributed direct
potable reuse (DPR) based on the City of Houston’s water and wastewater
infrastructure. The backbone DWS with greater than 92% link and node
reductions achieves satisfactory approximation of global flows and water
pressures, to enable configuration optimization analysis. Results from
the optimization model reveal case-specific as well as general
opportunities, constraints, and their interactions for DPR allocation.
Implementing DPR can be beneficial in areas with high energy intensities
of water distribution, considerable local water demands, and
commensurate wastewater reuse capacities. The meso-scale modeling
approach and the multi-objective optimization model developed in this
study can serve as practical decision-support tools for stakeholders to
search for alternative DWS options in urban settings.