Optimal Pump Control for Water Distribution Networks via Data-Based Distributional Robustness

In this article, we propose a data-based methodology to solve a multiperiod stochastic optimal water flow (OWF) problem for water distribution networks (WDNs). The framework explicitly considers the pump schedule and water network head level with limited information of demand forecast errors for an extended period simulation. The objective is to determine the optimal feedback decisions of network-connected components, such as nominal pump schedules and tank head levels and reserve policies, which specify device reactions to forecast errors for accommodation of fluctuating water demand. Instead of assuming that the uncertainties across the water network are generated by a prescribed certain distribution, we consider ambiguity sets of distributions centered at an empirical distribution, which is based directly on a finite training dataset. We use a distance-based ambiguity set with the Wasserstein metric to quantify the distance between the real unknown data-generating distribution and the empirical distribution. This allows our multiperiod OWF framework to trade off system performance and inherent sampling errors in the training dataset. Case studies on a three-tank WDN systematically illustrate the tradeoff between pump operational cost, risks of constraint violation, and out-of-sample performance.