Allocation of Resources Using a Microgrid Formation Approach for Resilient Electric Grids

176

in scenario s, otherwise set to 0. P t,s G,i , Q t,s G,i Real/reactive power generation at bus i in interval t in scenario s. P t,s r,i , Q t,s r,i Load restoration at bus i in interval t in scenario s. P t,s ij , Q t,s ij Real/reactive power of branch (i, j) in scenario s. v t,s i Voltage magnitude at bus i in interval t in scenario s. P t,s G,m , Q t,s G,m Aggregated active/reactive power in microgrid m in scenario s. P t,s DG,m , Q t,s DG,m Active/reactive power generation of equivalent dispatchable DG in microgrid m in scenario s. P t,s d,m and Q t,s d,m Active/reactive load in microgrid m in scenario s. E t,s DG,m Energy of equivalent dispatchable DG by the end of interval t in scenario s.

Section: Mathematical Formulationmentioning

confidence: 99%

“…10-(c), branch (9, 10) is repaired at t = 10, and the topology is reconfigured by opening switches (9,15), (12,22), (27,28). At t = 20, branch (24, 25) is repaired, the distribution network takes the new topology and switches (6,26), (8,21), (9, 10), (12,22) are open.…”

Section: (B) Impact Of Damage and Repair To Branchesmentioning

confidence: 99%

Rolling Optimization of Mobile Energy Storage Fleets for Resilient Service Restoration

Yao

Wang

Liu

et al. 2020

176

“…1) The model in [18] is designed for radial systems. It is extended in [25] to deal with meshed systems. Still, the extended model does not eliminate loops.…”

Section: Application To Resilient Mg Formationmentioning

confidence: 99%

Radiality Constraints for Resilient Reconfiguration of Distribution Systems: Formulation and Application to Microgrid Formation

Lei

Chen

Song

et al. 2020

128

Network reconfiguration is an effective strategy for different purposes of distribution systems (DSs), e.g., resilience enhancement. In particular, DS automation, distributed generation integration and microgrid (MG) technology development, etc., are empowering much more flexible reconfiguration and operation of the system, e.g., DSs or MGs with flexible boundaries. However, the formulation of DS reconfiguration-related optimization problems to include those new flexibilities is non-trivial, especially for the issue of topology, which has to be radial. That is, the existing methods of formulating the radiality constraints can cause underutilization of DS flexibilities. Thus, in this work, we propose a new method for radiality constraints formulation that fully enables the topological and some other related flexibilities of DSs, so that the reconfiguration-related optimization problems can have extended feasibility and enhanced optimality. Graph-theoretic supports are provided to certify its theoretical validity. As integer variables are involved, we also analyze the issues of tightness and compactness. The proposed radiality constraints are specifically applied to postdisaster MG formation, which is involved in many DS resilienceoriented service restoration and/or infrastructure recovery problems. The resulting new MG formation model, which allows more flexible merge and/or separation of the sub-grids, etc., establishes superiority over the models in the literature. Demonstrative case studies are conducted on two test systems.

“…Comparing with other MEG real-time dispatch models such as in [10], the proposed model has innovative steps over 4 aspects of: 1) integrating the network reconfiguration (considering tie lines) and provisional microgrid formation with MEG dispatch to restore critical loads; 2) developing a single-commodity flow method to avoid the impractical assumption that all loads can be energized by the substations or MEGs after disaster; 3) avoiding a large number of binary variables to considerably reduce the computational burden; 4) enabling the participation of non-black start distributed generators (NBGs) in emergency response operation. Specifically, the proposed MEG real-time dispatch model comprises of the topology constraints (8)- (10), MEG re-routing model (11a)-(11c), energization status model (12)- (14), outage duration model (15)- (19), and operational constraints (20)- (25).…”

Section: B Meg Pre-position In Prsmentioning

confidence: 99%

“…In the PRS, emergency resources can be pre-allocated, and preventive operation strategies can be customized in preparation for the upcoming disaster events, such as the pre-position of the MEGs [10] and repair crews [11], and the proactive day-ahead scheduling methods [12] - [13]. In the ERS, critical loads can be restored by the real-time responsive strategies based on the available resources, such as the network configuration and provisional microgrid formation based on the MEGs [14] and other distributed energy resources such as battery storages [15]. In the RTS, the maintenance crews are dispatched to repair the damaged facilities [16] - [17], and the distribution system can return to the normal condition step by step, which has been investigated elsewhere [18] and is not the main focus of this MEG planning paper.…”

Section: Introductionmentioning

confidence: 99%

Mobile Emergency Generator Planning in Resilient Distribution Systems: A Three-Stage Stochastic Model With Nonanticipativity Constraints

Zhang

et al. 2020

Mobile emergency generators (MEGs) can effectively restore critical loads as flexible backup resources after power network disturbance from extreme events, thereby boosting the distribution system resilience. Therefore, MEGs are required to be optimally allocated and utilized. For this purpose, a novel threestage 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 probabilityweighted 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 preposition 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 rerouted from the preposition 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 scenarios. 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 effectiveness and superiority of the proposed three-stage MEG planning model over the traditional two-stage model.