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

Abstract

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.

Application of Mobile Energy Storage for Enhancing Power Grid Resilience: A Review

“…The two-stage optimization is solved using the progressive hedging algorithm to minimize the operating cost in both conditions, finding that MESSs provide sufficient value in emergency conditions to justify their installation for both types of services. Zhang et al [46] propose a three-stage stochastic MEG planning model to minimize investment costs and expected penalty costs for load interruptions during outages. Mehrjerdi et al [49] present a coordinated stochastic plan for MESS operation in integrated electrical and heating networks to minimize operation costs and improve load restoration during electrical or natural gas outages.…”

“…Here, the goal is to allocate available energy resources in such a way that it optimizes certain objectives, the most common ones being operation cost and the amount of load served. In [25,39,46], MPSs are modeled as fictitious flows. Kim and Dvorkin [27] and Wang et al [44] model a computationally tractable second-order cone approximation of the optimal power flow problem for radial topologies based on the work of [52][53][54].…”

“…To address this, many researchers apply linearization techniques to the power flow equations. For instance, in [25,28,30,32,37,39,45,46,50], real and reactive power balance equations at each node are represented based on a linearized DistFlow model. Most studies assume a balanced distribution system, mainly because it can significantly improve the tractability of the optimization problem.…”

“…This imposes additional constraints including travel times, road congestion, and potential road network damage. Researchers have integrated transportation cost into the objective function to minimize the travel time of the MER as redundant travel causes extra cost [25,33,34,37,46]. As the unit transportation cost decreases, the benefit of the MER increases due to their increased allotment [33,37].…”

A three‐stage stochastic planning model for enhancing the resilience of distribution systems with microgrid formation strategy

“…Non-anticipativity constraints for a three-stage planning model Multipliers of active and reactive load profiles anticipativity constraints have been used in problems such as MEG planning, natural gas and power network expansion planning, and market clearing[4,27,28].…”