Implementation of simplified sequential stochastic model predictive control for operation of hydropower system under uncertainty

Abstract

Sequential stochastic model predictive control (MPC) is a control framework that sequentially employs two optimizers. The high-level optimizer generates a coarse long-term plan based on nominal uncertainty values, while the low-level optimizer refines the short-term plan by considering all possible realizations of these uncertainties. This paper highlights the advantages of the sequential stochastic MPC framework over the traditional multistage MPC approach. It offers faster computation times, improved feasibility of the optimal control problem (OCP), less conservative solutions, and enhanced flexibility in framework tuning. These advantages of sequential stochastic MPC are demonstrated through a case study on flood control in a hydropower station. Additionally, computational efficiency is further boosted with the simplified method which was proposed previously. The results reveal a remarkable performance improvement with an approximately 85 times faster computation time than the standard multistage MPC. These findings establish sequential stochastic MPC framework with the potential for practical implementation.