Mobile energy storage systems (MESSs) provide promising solutions to enhance distribution system resilience in terms of mobility and flexibility. This paper proposes a rolling integrated service restoration strategy to minimize the total system cost by coordinating the scheduling of MESS fleets, resource dispatching of microgrids and network reconfiguration of distribution systems. The integrated strategy takes into account damage and repair to both the roads in transportation networks and the branches in distribution systems. The uncertainties in load consumption and the status of roads and branches are modeled as scenario trees using Monte Carlo simulation method. The operation strategy of MESSs is modeled by a stochastic multi-layer time-space network technique. A rolling optimization framework is adopted to dynamically update system damage, and the coordinated scheduling at each time interval over the prediction horizon is formulated as a two-stage stochastic mixed-integer linear program with temporal-spatial and operation constraints. The proposed model is verified on two integrated test systems, one is with Sioux Falls transportation network and four 33-bus distribution systems, and the other is the Singapore transportation network-based test system connecting six 33-bus distribution systems. The results demonstrate the effectiveness of MESS mobility to enhance distribution system resilience due to the coordination of mobile and stationary resources.

Battery swapping-charging system (BSCS) is a promising solution to provide better battery swapping service to electric vehicles (EVs). To achieve the optimal operation of BSCSs, a closed-loop supply chain (CLSC) based BSCS model is proposed to realize the combined operation of battery charging stations (BCSs) and battery swapping stations (BSSs) while the quality of battery swapping service at BSSs is ensured with a network calculus based service model. The battery logistics within the BSCS is modeled based on a time-space network (TSN) technique. The charging and the logistics of depleted batteries and wellcharged batteries are optimally managed to maximize the revenue of the BSCS. The optimization problem is formulated as a mixedinteger linear programming (MILP) model with various TSN and operation constraints. Random-permuted alternating direction method of multipliers (RP-ADMM) is applied as a heuristic to solve the problem in a distributed way. Simulation results verify the feasibility of the proposed model and the heuristic solution with small optimality losses and less computation time.

Mobile energy storage systems (MESSs) is a promising solution to enhancing the operational flexibility of coupled distribution and transportation networks (CDTNs), as well as the conversion capacities of hybrid AC/DC microgrids (MGs). To achieve the coordination among MESSs, hybrid AC/DC microgrids and CDTNs, while considering the system uncertainties from variable renewable energy (VRE) sources and the daily traffic demands of common vehicles in transportation networks, a two-stage stochastic management scheme is proposed to minimize the expected system operational cost. In the first-stage, the vehicle scheduling problem of MESSs is modeled and optimized based on the proposed user-equilibrium-based multi-layer multi-timescale time-space network model to fully utilize the mobility of MESSs under traffic uncertainties, i.e., MESSs are scheduled to travel within CDTNs for energy transfer or local energy scheduling. In the second-stage, the actual charging/discharging behaviors of MESSs, outputs of VRE sources and the power conversions at each MG are adjusted in accordance with uncertainty realizations. The proposed model is verified on a test system with Sioux Falls transportation network and a 33-bus distribution system. The results demonstrate the effectiveness of MESSs mobility to serve as potential power conversion enhancements for MGs with mismatched generation and conversion capabilities.

Energy systems, e.g., power systems and natural gas systems, are increasingly interdependent, which brings benefits under normal conditions and introduces risks under extreme weather events, e.g., hurricanes. A comprehensive method is proposed to reflect the spatial-temporal hurricane impacts on interdependent power and natural gas systems. Mechanism analysis based and data based component failure models are used to map hurricane effects on exposed component failure probabilities based on which system states are generated using Monte Carlo simulation. For system states with failed components, the integrated energy flow is developed to determine minimum joint load shedding considering inherent properties of each system and characteristics of interdependence coordinately. Operational metrics and infrastructure metrics are employed to quantify the service adequacy and network resistance of the interdependent energy systems, respectively. The modified IEEE 24-reliability test system and the 12-node natural gas system under a simulated hurricane are used to validate the effectiveness of the proposed method. Simulation results show the method can identify weak parts and quantify the performance of interdependent power and natural gas systems under hurricanes to provide information for effective and coordinative preparedness.

Electrification of vehicles is strengthening the interaction between power systems and transportation systems, formulating the coupled transportation power systems (CTPSs). A novel optimal traffic power flow (OTPF) problem is proposed to analyze the spatial and temporal congestion propagation on CTPSs, under congested roads, transmission lines, and charging stations. The traffic flow is depicted by the spatial and temporal distribution of electric vehicles (EVs) on roads and charging stations, connected by multilayer time-space networks (TSNs). This distribution is the solution to a mesoscale traffic assignment problem of EV fleets on TSNs, where the charging, discharging, routing, and origin–destination pairing can be optimized simultaneously. The power flow is captured by the optima of dynamic optimal power flow problems with security constraints. An extended alternating direction multiplier method algorithm with the convex-concave procedure is proposed to solve OTPFs. Results verify the effectiveness of the proposed scheme for congestion management on CTPSs.

In the face of extreme events, e.g., hurricanes, the transmission systems, especially the transmission lines, are affected across time and space. To mitigate these impacts on the day-ahead market from a probabilistic perspective, a resilient unit commitment (UC) problem is formulated as a two-stage distributionally robust and robust optimization (DR&RO) problem. In the first stage, the commitment, energy, and reserves of generators are pre-scheduled to minimize the operational cost, responding to the worst load forecasting and line failure scenario in the operating day. The operating status of transmission lines are depicted by a novel uncertainty set with a distributionally chance constraint considering the repair of failed lines. This chance constraint is reformulated to its deterministic equivalence. Using both load shedding and generation curtailment, recourse problems are formulated in the second stage considering the time-varying operating status of transmission lines. The formulated DR&RO problem is solved using a hybrid Benders decomposition and constraint-and-column generation scheme. Simulations are conducted on the modified IEEE reliability test system (RTS) and two-area IEEE RTS-96 under hurricanes. Results verify the effectiveness of the proposed method, in comparison with prevalent two-stage stochastic and robust optimization methods.