Ensemble prediction aided multi-objective co-design optimizations of grid-connected integrated renewables for green hydrogen production

Abstract

The use of renewables including wind and photovoltaic (PV) powers to produce green hydrogen will be of great importance for promoting cleaner productions. However, there is a need for the co-design optimizations of integrated renewables-based hydrogen productions considering interactions between renewables and power grid. This work investigates this type of green hydrogen production which is achieved in a distributed manner through the power grid. An overall modelling framework is provided to describe characteristics of the main subsystems including wind farms, power grid, PV array, pumped hydro storages and the electrolyzers considering wake steering conditions of wind farms. Then, a co-design optimization methodology using ensemble learning aided wind farm power predictions is proposed. The proposed methodology is tested by using design experiments based on the IEEE power grid. The results indicate that wind farm power prediction errors can be reduced from around 13%–4% by using the ensemble learning compared with a gaussian method. It is then possible that parameters of the renewables and water electrolyzer can be optimized simultaneously such that the grid-connected hydrogen productions can be kept stable. Also, the voltage oscillations (grid cost) can be well kept within appropriate stable range of [0, 0.15] p. u. under different wind conditions.