Scenario-Based Hierarchical and Distributed MPC for Water Resources Management with Dynamical Uncertainty

Abstract

A real-time control scheme informed by a streamflow forecast is presented for the optimal operation of water resources systems composed of multiple and spatially distributed systems, affected by hydroclimatic disturbances. The approach uses a two-layer scenario-based hierarchical and distributed model predictive controller (HD-MPC) to deal with the operational water management problem under dynamical uncertainty. The higher layer collects and coordinates forecast information, which is rendered into possible realizations of the uncertainties and sent to the local agents. The lower layer solves a distributed optimization problem related to the actual management objectives. The HD-MPC method is demonstrated through a simulation of the North Sea Canal system as a real-world case study. The results show the benefits of the proposed compared to over other types of MPC controllers.