Adjustable Robust Optimization for Integrated Power and Water Systems under Uncertainty

Water distribution systems (WDS) and power distribution systems (PDS) are strongly connected and mutually affect each other. Recently, several studies suggested accounting for this dependency to enhance the overall efficiency of the two critical systems. However, in these studies, the uncertainty inherent in the two systems got less attention. Here, we emphasize the uncertainty modeling by using robust optimization (RO) and adjustable robust optimization (ARO) to co-optimize the day-ahead scheduling of an integrated PDS-WDS. In this problem, we consider the uncertainty in both power loads and water demands and its impact on the operational strategies of the coupled system. Using RO and ARO, we can derive the Pareto frontier between operation cost and system reliability. Our results suggest that for high levels of reliability, the ARO outperforms the RO approach, as it can achieve a high level of reliability with lower operation costs.