Reference optimisation of uncertain offshore hybrid power systems with multi-stage nonlinear model predictive control

Abstract

This paper presents a modified multi-stage economic nonlinear model predictive controller (M-ENMPC) for reference optimisation of isolated, uncertain offshore hybrid power systems (OHPSs). These systems require control strategies that can handle significant stochastic disturbances in exogenous power demand and wind, given uncertain forecasts of the disturbances. An M-ENMPC modified with a certainty horizon is formulated to hande uncertain forecasts of these disturbances for reference optimisation of OHPSs. The certainty horizon models the increase in uncertainty of forecasts with time to decrease the cost in the M-ENMPC. Monte Carlo simulations with different realisations of the considered disturbances show that explicitly considering scenarios of the disturbances with the M-ENMPC can decrease greenhouse gas (GHG) emissions by operating the gas turbines in the hybrid power system more efficiently while achieving an acceptable satisfaction of the exogenous power demand. Furthermore, the Monte Carlo simulations show that using the modified M-ENMPC decreases the average computational time by 17% compared with the conventional M-ENMPC from the literature.

Reference optimisation of uncertain offshore hybrid power systems with multi-stage nonlinear model predictive control