Multistage Dynamic Planning of Integrated Hydrogen-Electrical Microgrids under Multiscale Uncertainties

TLDR: In this paper , a multistage stochastic mixed-integer program (MS-MIP) formulation is proposed to keep track with the fast development of hydrogen industry, and a nested decomposition algorithm based on Stochastic Dual Dynamic Integer Programming (SDDiP) is developed.

Abstract: Integrated Hydrogen-Electrical (IHE) microgrids are desirable testbeds for the practice of carbon-neutral energy supply. This paper studies the IHE microgrids planning (IHEMP) under a dynamic perspective. To keep track with the fast development of hydrogen industry, we propose a multistage stochastic mixed-integer program (MS-MIP) formulation. It comprehensively considers the siting and sizing decisions of IHE microgrids, the dynamic expansion of distributed energy facilities, and the detailed operational model to derive a robust, flexible and profitable investment policy. Moreover, a scenario-tree based sampling strategy is leveraged to capture both the large-scale strategic uncertainties (e.g., the long-term growth of electric loads and hydrogen refueling demands, as well as the cost changes of system components) and fine-scale operating uncertainties (e.g., random variation of renewable energy outputs and loads) under different time scales. As the resulting formulation could be computationally very challenging, we develop a nested decomposition algorithm based on Stochastic Dual Dynamic Integer Programming (SDDiP). Case studies on exemplary IHE microgrids validate the effectiveness of our dynamic planning approach. Also, the customized SDDiP algorithm shows a superior solution capacity to handle large-scale MS-MIPs than the state-of-the-art solver (i.e., Gurobi) and a popular scenario-oriented decomposition method (i.e., progressive hedging algorithm).