A hierarchical co-optimal planning framework for microgrid considering hydrogen energy storage and demand-side flexibilities

In order to cope with the fluctuations of renewable energy sources (RES) and the impact of random charging loads of electric vehicles (EV), this paper proposes a hierarchical co-optimal planning framework for flexible energy management of microgrid (MG). Various flexible resources including hydrogen energy and vehicle-to-grid (V2G) are refinedly modeled to explore their synergistic effects on the reliability, economy, and greenness of MG. The upper-layer model is V2G optimal scheduling to simultaneously minimize the MG's load variance and the EV owners' charging cost. NSGA-II is adopted to achieve mutual benefits for MG investors and EV consumers. The lower-layer model is MG optimal planning for harmonization of economic cost and RES consumption ability. It's worth noting that a novel relaxation strategy based on ε-constraint method is proposed to equivalently transform the lower-layer nonlinear problem into a mixed-integer linear programming problem. Then, Gurobi solver is applied to solve it efficiently. Case study validates that V2G on average reduces the MG's load variance by 56.22 % and reduces the EV owners' charging cost by 61.74 %. A novel finding is that hydrogen as a zero‑carbon fuel supplied for hydrogen-fueled vehicles provides significant flexibility value comparable to energy storage, as demonstrated by an additional 68.52 % reduction in the renewable energy curtailment ratio (RECR) than hydrogen only used for energy storage, which is usually overlooked in existing research. Moreover, co-optimizing hydrogen energy and V2G can reduce MG's annualized total cost by 0.77 %–1.38 % and reduce RECR by 10.85 %–22.47 % compared to considering only one flexible resource, which is adopted in most studies.