Mobile emergency generator planning in resilient distribution systems: a three-stage stochastic model with nonanticipativity constraints - Zhang, Gang; Zhang, Feng; Zhang, Xin; Wang, Zhaoyu; Meng, Ke; Dong, Zhao Yang (IEEE, 2020)
Mobile emergency generators (MEGs) can effec-tively restore critical loads as flexible backup resources after power network disturbance from extreme events, thereby boosting the distribution system resilience. Therefore, MEGs are re-quired to be optimally allocated and utilized. For this purpose, a novel three-stage stochastic planning model is proposed for MEG allocation of resilient distribution systems in consideration of planning stage (PLS), preventive response stage (PRS) and emergency response stage (ERS). Moreover, the nonanticipativity constraints are proposed to guarantee that the MEG allocation decisions are dependent on the stage-based uncertainties. Specifically, in the PLS, the intensity uncertainty (IU) of disasters and the outage uncertainty (OU) incurred by a given disaster are considered with probability-weighted scenarios for the effective MEG allocation. Then, with the IU that can be observed in the PRS, the MEGs are pre-positioned in the consideration of OU. It is noted that the pre-position decisions should only correspond to the IU realizations, according to nonanticipativity constraints. Last, with the further realization of OU in the ERS, the MEGs are re-routed from the pre-position to the target location, so that the provisional microgrids can be formed to restore critical loads. The proposed planning model can be large-scale due to multiple sce-narios. Therefore, the progressive hedging algorithm (PHA) is customized to reduce the computational burden. The simulation results in 13 and 123 node distribution systems show the effec-tiveness and superiority of the proposed three-stage MEG plan-ning model over the traditional two-stage model.
Keywords: uncertainty; resilience; Mobile generator planning; three-stage stochastic programming; nonanticipativity constraints