Seasonal operation planning of hydrogen-enabled multi-energy microgrids through multistage stochastic programming

As a carbon-free storage medium, hydrogen has advantages in large-scale and long-term energy shifting, thereby mitigating the seasonal imbalance between energy supply and demands. By integrating hydrogen, electricity, heating and cooling, the hydrogen-enabled multi-energy microgrid (HMM) provides a desirable test bed for decarbonizing the energy and power systems. In this paper, we study the networked HMMs operation planning (NHOP) that optimizes the multi-timescale synergy of seasonal and short-term energy storage. To hedge against the complex demand–supply uncertainties (e.g., seasonal fluctuation and hourly variation of renewable power generation and energy demands), the NHOP problem is recast as a multistage stochastic mixed-integer program (MS-MIP). Moreover, to overcome the computational challenges, a nested decomposition algorithm based on stochastic dual dynamic integer programming (SDDiP) is tailored and implemented. Case studies on a 33-bus test network with multiple HMMs demonstrate the economic benefits of our operation planning strategy. The proposed NHOP model can well capture the seasonal and intra-day dynamics of multi-type storage operation, which helps improve the cost-benefits under versatile practical situations. Also, the customized SDDiP algorithm shows a strong scalable capacity for efficiently computing large-scale MS-MIPs.