Co-planning of hydrogen-based microgrids and fuel-cell bus operation centers under low-carbon and resilience considerations

The hydrogen-driven fuel-cell electric buses, which bear the advantages of emission free, high storage content and long mileage range, provide a promising choice for decarbonizing the public transportation system. Also, the operation centers for fuel-cell electric buses can be upgraded as the hydrogen-based microgrids to achieve higher economic benefits and resilience levels. To support the low-carbon and resilience-oriented planning of microgrid-aided bus centers, this paper presents a two-stage robust optimization formulation with integer corrective decisions. In particular, the first-stage problem is to optimize the microgrid siting decisions as well as the capacity configuration of renewable-powered electrolyzers, fuel-cell stacks and hydrogen refueling stations for maximizing the annualized benefits. Then, a min–max problem is introduced in the second-stage to improve the load survival value subject to the planning decisions and the worst-case contingency scenario. Also, various corrective actions, e.g., the emergency response of fuel-cell electric buses using vehicle-to-grid functionality, the energy scheduling of stationary equipment in hydrogen refueling stations and the adaptive network reconfiguration, are considered for resilience enhancement of the local power network. As the resulting robust optimization formulation is with a challenging mixed-integer recourse structure, the nested column-and-constraint generation algorithm is designed. Numerical results on exemplary power network and public transportation system validate the effectiveness of the proposed planning methodology, which has the potential to be profitable under normal operations while improving the load survivability under severe disastrous occasions. Moreover, our customized nested column-and-constraint generation shows a satisfactory solution performance, which facilitates prompt disaster relief actions of energy-transportation network.