  We present an open-source solution for the operational control of drinking
water distribution networks which accounts for the inherent uncertainty in
water demand and electricity prices in the day-ahead market of a volatile
deregulated economy. As increasingly more energy markets adopt this trading
scheme, the operation of drinking water networks requires uncertainty-aware
control approaches that mitigate the effect of volatility and result in an
economic and safe operation of the network that meets the consumers' need for
uninterrupted water supply. We propose the use of scenario-based stochastic
model predictive control: an advanced control methodology which comes at a
considerable computation cost which is overcome by harnessing the
parallelization capabilities of graphics processing units (GPUs) and using a
massively parallelizable algorithm based on the accelerated proximal gradient
method.
