Multi-stage stochastic long-term planning of grid-connected hydrogen-based energy system based on improved SDDIP

Hydrogen-based energy systems (HESs) have shown great potential to promote the process of decarbonization. Conventional studies mainly focus on the sizing and operation of HESs in a determined static situation, and the dynamic planning model of HESs considering large-scale uncertain scenarios of future developments should be considered. This paper proposes a multi-stage stochastic programming (MSP) long-term planning model to find the optimal sequential planning results of the grid-connected HES. The planning model considers the long-term uncertainties of the investment cost decrease and the load increase. Additionally, the short-term uncertainties of renewable energies are also considered to obtain robust results in each stage. The improved stochastic dual dynamic integer programming (SDDIP) is then employed to solve the MSP long-term planning model with consideration of the realized uncertainties. Specifically, the sequential planning order is developed to improve the efficiency of the SDDIP. Numerical case studies are constructed to show the convergence process of the improved SDDIP and the planning results of the HES. Moreover, the improved SDDIP shows greater efficiency compared with the traditional SDDIP and the method which solves the model directly.