Chance constrained robust hydrogen storage management to service restoration in microgrids considering the methanation process model.

After a natural disaster, electrical grids might go into a self-healing mode to restore service and reconnect any customers that lost power. In the self-heal system, the fast operation and restoring the critical loads (CLs) during a fault by available resources can increase the flexibility and resilience of the network. In addition to renewable energy sources and electric vehicles (EVs) storage devices, the hydrogen storage system and the process of converting hydrogen into electric energy by micro-gas turbine (MGT) have been modeled in order to assess the impact of this storage system in electrical loads restoration in critical conditions. Self-healing functionality is enhanced by using alternative and novel energy sources. In such a situation, how to interact with the stochastic nature of network components will be challenging. This paper formulates the optimal service restoration (SR) problem using chance-constrained Adaptive Distributionally Robust Optimization (ADRO) to address the ambiguities introduced by the uncertain parameters. A tri-level robust optimization model is developed such that the optimal switching in the network until clearing the fault is determined at the upper level, and the maximization of the SR based on the load demand values is determined at the lower level. The proposed method is demonstrated by simulation results on the modified Civanlar test system. In an equal operating condition and considering β = 3, it can be seen that the initial hydrogen State of the Tank (SoT) by 50 % can improve the load restoration by 7.5 % compared to the uncharged hydrogen tank. By increasing the initial SoT to the nominal value (10 Kg), the load restoration can increase to 13.2 %. [Display omitted] • Customized hydrogen into electric energy conversion model is developed in the energy management field. • Each uncertain parameter is considered ambiguous in the manner that follows an unknown but bounded distribution. • EVs, PHEVs, flexible loads, and the coordinated operation of the hydrogen-to-power system are all taken into account simultaneously. • A tri-level MILP model is formulated to determine the service restoration schedule. • CVaR is employed to effectively deal with the uncertainty of the parameters. [ABSTRACT FROM AUTHOR]