This paper proposes an integrated optimization model for unbalanced distribution system restoration (DSR) after large-scale power outages caused by extreme events. The model can coordinate the control actions of multiple types of distributed energy resources (DERs), including dispatchable distributed generators (DGs), renewable DGs (mainly wind and solar), and energy storage systems (ESSs). The model also considers topology flexibility by forming dynamic islands through reconfiguration. Besides, the optimal dispatch model of repair crews (RCs) and mobile emergency generators (MEGs) are also proposed to leverage the restoration capabilities of existing DERs installed in the distribution systems. The integrated optimization model is linearized to be a mixed-integer linear programming (MILP) form, which can be effectively solved by off-the-shelf solvers such as Cplex and Gurobi. Numerical results on IEEE 123 and 8500 node test feeders validated the effectiveness of the proposed model and highlighted the necessity of coordinating various flexible resources.

Large-scale blackouts and extreme weather events in recent decades bring up the need for resilient electric power infrastructures. Distribution service restoration (DSR), a fundamental application in outage management systems (OMS), provides restoration solutions for system operators when power outages happen. As distribution generators (DGs) and remotely controllable devices are increasingly installed in distribution systems, an advanced DSR is needed to perform optimally coordinated restoration that will achieve maximal restoration performance. This paper introduces a DSR modeling framework, which can generate optimal switching sequences and estimated time of restoration (ETR) in the presence of remotely controllable switches, manually operated switches, and dispatchable DGs. Two mathematical models, a variable and a fixed time-step model, are presented and compared. The proposed models are formulated as a mixed-integer linear programming model (MILP), and their effectiveness is evaluated via the IEEE 123 node test feeder.

Distribution service restoration (DSR) is critical for improving the resilience and reliability of modern distribution systems by strategically and sequentially energizing the system components and customer loads. Restoring electricity service to affected customers also requires multiple crews with different skill sets to perform multiple tasks that are procedurally interdependent with safety guaranteed. However, in existing DSR practices, switch operations and crew dispatch are scheduled separately, and their interdependence is not fully considered. As advanced technologies are enabling remote communication, control, and dispatch, utilities now desire an integrated DSR framework to achieve seamless coordination among multiple DSR tasks such as switch operation, crew dispatch, and component repair. In this paper, we introduce a synthetic model that integrates the service restoration model and the crew dispatch model based on a universal routing model. The proposed model can provide the estimated time of restoration (ETR) for each load, the switching sequence for safely operating remotely/manually operated switches, and dispatch solutions for crews with different skill sets. The proposed synthetic model is formulated as a mixed-integer linear programming (MILP) model, and its effectiveness is evaluated via the IEEE 123 node test feeder.

Enhancing restoration capabilities of distribution systems is one of the main strategies for resilient power systems to cope with extreme events. However, most of the existing studies assume the communication infrastructures are intact for distribution automation, which is unrealistic. Motivated by the applications of the emergency communication vehicles (ECVs) in quickly setting up wireless communication networks after disasters, in this paper, we propose an integrated distribution system restoration (DSR) framework and optimization models, which can coordinate the repair crews, the distribution system (physical sectors), and the emergency communication (cyber sectors) to pick up unserved power loads as quickly as possible. Case studies validated the effectiveness of the proposed models and proved the benefit of considering ECVs and cyber-physical interdependencies in DSR.

Distributed secondary control stands out for its flexibility and expandability in microgrids (MGs) control, in where communication network plays a fundamental and critical role. The communication topology and link-weights have significant impact on the attack-resilience and dynamic performance of MGs, which should be appropriately designed. However, the joint optimization problem of communication topology and link-weights in the existing literature of consensus-based secondary voltage control of MGs is usually neglected. To bridge this gap, we propose a novel two-stage optimization approach for the communication network design, which jointly optimizes the topological structure to enhance the structural survivability and link weights to improve the dynamic performance (including the speed of convergence and robustness to time-delay). The first stage problem is formulated into a mixed-integer semi-define programming (MISDP) model based on convex relaxation technique, which is then converted equivalently into an integer quadratic programming (IQP) problem and then a MISDP feasibility problem to facilitate the solution. The second stage problem is formulated into a bi-objective SDP model to compromise between the convergence performance and robustness to time-delay. Simulations based on a microgrid with 10 distributed generation (DG) units under different scenarios are implemented to verify the effectiveness of the proposed method.