Mobile power sources (MPSs), including electric vehicle fleets, truck-mounted mobile energy storage systems, and mobile emergency generators, have great potential to enhance distribution system (DS) resilience against extreme weather events. However, their dispatch is not well investigated. This paper implements resilient routing and scheduling of MPSs via a two-stage framework. In the first stage, i.e., before the event, MPSs are pre-positioned in the DS to enable rapid pre-restoration, in order to enhance survivability of the electricity supply to critical loads. DS network is also proactively reconfigured into a less impacted or stressed state. A two-stage robust optimization model is constructed and solved by the column-and-constraint generation algorithm to derive first-stage decisions. In the second stage, i.e., after the event, MPSs are dynamically dispatched in the DS to coordinate with conventional restoration efforts, so as to enhance system recovery. A novel mixed-integer programming model that resolves different timescales of MPS dispatch and DS operation, coupling of road and power networks, etc., is formulated to optimize dynamic dispatch of MPSs. Case studies conducted on IEEE 33-node and 123-node test systems demonstrate the proposed method's effectiveness in routing and scheduling MPSs for DS resilience enhancement.

Repair crews(RCs) and mobile power sources(MPSs) are critical resources for distribution system (DS) outage management after a natural disaster. However, their logistics is not well investigated. We propose a resilient scheme for disaster recovery logistics to co-optimize DS restoration with dispatch of RCs and MPSs. A novel co-optimization model is formulated to route RCs and MPSs in the transportation network, schedule them in the DS, and reconfigure the DS for microgrid formation coordinately, etc. The model incorporates different timescales of DS restoration and RC/MPS dispatch, the coupling of transportation and power networks, etc. To ensure radiality of the DS with variable physical structure and MPS allocation, we also model topology constraints based on the concept of spanning forest. The model is convexified equivalently and linearized into a mixed-integer linear programming. To reduce its computation time, preprocessing methods are proposed to pre-assign a minimal set of repair tasks to depots and reduce the number of candidate nodes for MPS connection. Resilient recovery strategies thus are generated to enhance service restoration, especially by dynamic formation of microgrids that are powered by MPSs and topologized by repair actions of RCs and network reconfiguration of the DS. Case studies demonstrate the proposed methodology.

Truck-mounted mobile emergency generators (MEGs) are critical flexibility resources of distribution systems (DSs) for resilient emergency response to natural disasters. However, they are currently under-utilized. For better utilization, this paper proposes dispatching MEGs as distributed generators in DSs to restore critical loads by forming multiple microgrids (MGs). As the travel time of MEGs on road networks (RNs) can greatly influence the outage duration of critical loads, a two-stage dispatch framework consisting of pre-positioning and real-time allocation is introduced, and the traffic issue is considered via the vehicle routing problem. Pre-positioning places MEGs in staging locations prior to a natural disaster, while real-time allocation sends MEGs from staging locations to restore critical loads by forming MGs in DSs after the natural disaster strikes. Specifically, with the objective of minimizing the expected outage duration of loads considering their priorities and demand sizes, pre-positioning is done via a scenario-based two-stage stochastic optimization problem, in which the first-stage pre-positioning decisions are evaluated by numbers of second-stage real-time allocation problems corresponding to considered scenarios of DS damage and RN damage/congestion. A scenario decomposition algorithm is applied to solve this problem. Illustrative cases demonstrate the effectiveness of the proposed dispatch scheme and algorithm.

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 under-utilization 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 post-disturbance MG formation, which is involved in many resilience-oriented DS service restoration and 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.

Extreme weather events, many of which are climate change related, are occurring with increasing frequency and intensity and causing catastrophic outages, reminding the need to enhance the resilience of power systems. This paper proposes a proactive operation strategy to enhance system resilience during an unfolding extreme event. The uncertain sequential transition of system states driven by the evolution of extreme events is modeled as a Markov process. At each decision epoch, the system topology is used to construct a Markov state. Transition probabilities are evaluated according to failure rates caused by extreme events. For each state, a recursive value function, including a current cost and a future cost, is established with operation constraints and intertemporal constraints. An optimal strategy is established by optimizing the recursive model, which is transformed into a mixed integer linear programming by using the linear scalarization method, with the probability of each state as the weight of each objective. The IEEE 30-bus system, the IEEE 118-bus system, and a realistic provincial power grid are used to validate the proposed method. The results demonstrate that the proposed proactive operation strategies can reduce the loss of load due to the development of extreme events.