Optimal scheduling for microgrids with hydrogen fueling stations considering uncertainty using data-driven approach

Hydrogen fueling stations can convert electricity to hydrogen with onsite hydrogen production technologies. When using renewables and cheap grid energy, hydrogen fueling stations present an economical and secure option for hydrogen production and supply for hydrogen vehicles. Microgrids with hydrogen fueling stations can thus serve dual functions: producing hydrogen and meeting the demand of hydrogen vehicles. Previous researches focus on hydrogen production and consumption in isolation, this paper integrates the two issues for achieving whole-system efficiency for the micrgrids. This paper proposes an optimal scheduling model for microgrids with hydrogen fueling stations taking consideration of wide range of uncertainties: renewable generation, electrical and hydrogen loads and electricity price. The uncertainty of renewable energy power and loads is addressed using a data-driven chance constrained approach; while the uncertainty of electricity price is handled with a distributionally robust optimization approach based on Wasserstein metric. When mitigating uncertainty, the proposed approaches only utilize historical data and do not need any prior knowledge about the true probability distribution of the uncertainty. Moreover, affine policy based techniques are developed to convert the complicated optimization problem with huge uncertain constraints into a tractable mixed integer linear programming problem. Finally, numerical analyses are carried out on an IEEE 33-bus distribution microgrid, the results show that the proposed model not only coordinate the distributed energy resources and hydrogen fueling stations optimally, but also enables the schedule to strike a balance between the conservatism and optimism against uncertainties.