Extreme weather, such as rainstorms, often triggers faults in the distribution network, and power outages occur. Some serious faults cannot be repaired by one team alone and may require equipment replacement or engineering construction crews to work together. Rainstorms can also lead to road damage or severe waterlogging, making some road sections impassable. Based on this, this paper first establishes a road network model to describe the dynamic changes in access performance and road damage. It provides the shortest time-consuming route suggestions for the traffic access of mobile class resources in the post-disaster recovery task of power distribution networks. Then, the model proposes a joint repair model with general repair crew (GRC) and senior repair crew (SRC) collaboration. Different types of faults match different functions of repair crews (RCs). Finally, the proposed scheme is simulated and analyzed in a road network and power grid extreme post-disaster recovery model, including a mobile energy storage system (MESS) and distributed power sources. The simulation finds that considering road damage and severe failures produces a significant difference in the progress and load loss of the recovery task. The model proposed in this paper is more suitable for the actual scenario requirements, and the simulation results and loss assessment obtained are more accurate and informative.

With the increasing scale of multi-energy microgrids (MGs) and complicated operation modes, the coordinated operation of microgrids and the distribution network (DN) has posed great challenges. In this paper, a bi-level optimal coordinated dispatch framework of the DN and multi-energy MGs based on CCHP (combined cooling, heating, and power) is proposed. The first level studies the optimal operation of the DN with power interaction on tie lines between MGs considering the coupling relationship and constraints of the equipment and network. The network reconfiguration with limited control actions is considered to increase the flexibility of the topology and further improve the working state. For the second level, MGs receive orders from the DN and determine the optimal strategies of multi-energy devices to achieve optimized operation under the condition of satisfying the different types of load and requirement for the DN. To solve the optimal dispatch problem of both the DN and the multi-energy MGs considering the DN reconfiguration, a method combining particle swarm optimization algorithm (PSO) with mixed-integer linear programming (MILP) is proposed. Cases studied in an IEEE33-node DN with renewable power sources and grid-connected MGs validate that the proposed method is very effective in reducing the power loss and voltage offset of the DN while ensuring the benefits of the MGs.

As essential components in the energy hub, the multi-energy devices are diverse, and their working conditions are complex. The relevant energy efficiency level of devices in the energy hub is also an important factor for the long-term operation of the energy systems. The device efficiency is always considered constant in modeling the energy hub. However, it is necessary to study the efficiency variation and optimization of devices under off-design working conditions. In this paper, we propose a coordinated dispatch method of the multi-energy system considering the device off-design characteristics in the energy hub. The energy efficiency optimization is included in the dispatch process. Then, we use the analysis target cascade (ATC) to obtain the optimal solution. The method is tested in the system, including distribution network, natural gas network, and energy hub. Compared to the traditional method, the proposed method can describe the operation of the whole system more authentically and thereby prove the effectiveness of energy efficiency optimization. The corresponding nonlinear constraints are linearized by piecewise linearization. Besides, we discuss the results of energy efficiency optimization at different load levels and capacity combinations, which is of guiding significance to optimize system working conditions.

Power-quality resilience is an important component of sustainable industrial electricity use, as voltage sag events can cause production interruptions, equipment damage, and inefficient allocation of mitigation costs and benefits among stakeholders. However, high initial investment costs and the lack of a viable commercial operation scheme have hindered the large-scale deployment of mitigation devices. To support sustainable power-quality governance, this study proposes an integrated framework that connects the technical compensation performance of the mitigation device with the economic foundation of a tripartite commercial operation model. First, an adaptive switching compensation strategy dynamically shifts between different modes based on the real-time voltage sag depth, establishing a mapping relationship with avoided-loss benefits. Then, a bi-level Nash bargaining model is constructed to allocate costs and benefits among the government, the enterprise, and the user, deriving closed-form analytical solutions for both the upper- and lower-level games. Through pilot operations at a large public service facility, economic losses of 480,000 CNY caused by a single voltage sag can be effectively avoided. Meanwhile, under the proposed scheme, all three parties achieve positive net present values. Compared to the user self-funding mode, the user’s NPV increases by 21.9%. Furthermore, unlike bilateral or equal-sharing alternatives, the Nash bargaining solution ensures all parties remain within the strong feasible region. The government and enterprise recover their costs within 4.14 and 6.20 years, respectively. These results indicate that the proposed framework can enhance the economic sustainability of power-sensitive users, encourage shared public–private investment in power-quality improvement, and support more resilient and efficient industrial electricity use.

During the process of vehicle–grid interaction, the charging load of electric vehicles shows significant uncertainty, which is driven by multiple user behavior variables: including the differentiated characteristics of users’ daily travel needs, as well as personalized charging habits, random charging periods, and dynamic changes in charging power demands. To address the scheduling challenges arising from the uncertainty of electric vehicle loads in the interaction between electric vehicles and the power grid, this paper proposes a multi-objective optimization scheduling method for the interaction between electric vehicles and the power grid, which takes into account the uncertainty of power sources and loads. This method can enhance the economic operation level of the power grid, increase the acceptance capacity of renewable energy, and improve the stability of the system. Firstly, this paper proposes an improved K-means clustering algorithm, which combines Monte Carlo sampling to achieve the generation and reduction of scenarios for electric vehicle load and photovoltaic output. Secondly, a scheduling framework based on the vehicle–grid collaborative interaction mode is constructed, and a random optimization scheduling model for photovoltaic storage electric vehicles is established. Finally, an example of a photovoltaic storage charging station in an industrial park is used for verification. The simulation results demonstrate the economic feasibility and effectiveness of this scheduling strategy.