Mobile energy storage (MES), as a flexible resource, plays a significant role in disaster emergency response. Rational pre‐positioning ahead of disasters can accelerate the dispatch of MES to power outage areas, and further reduce load losses. This paper focuses on typhoon disasters and studies the MES pre‐positioning method for distribution networks coupled with transportation networks. Firstly, a typhoon model considering the typhoon eye was formulated. Secondly, the analysis covering the diverse impacts of typhoons on the ‘generation‐transmission‐load‐road’ system was conducted. Subsequently, a bi‐level pre‐positioning model, considering multi‐index evaluation, was established based on scenario‐based stochastic optimization. Finally, the modified MATPOWER 18‐node test system was utilized to verify the performance of the proposed method, and the simulation results demonstrated its effectiveness and applicability.

Uncertain variables, such as electric power system parameters, have significant impacts on dynamic simulations of power systems. As traditional uncertainty analysis methods for power system dynamic simulations, both the simulation method and the approximation methods are difficult to balance the model complexity, computational efficiency, and simulation accuracy. In order to balance the model complexity, computational efficiency, and simulation accuracy, this paper proposes a method for uncertainty analysis for power system dynamic simulation based on the Nataf transformation and Gaussian-Hermite quadrature. Firstly, the samples on the normal distribution space are determined according to the Gaussian-Hermite quadrature points and the Nataf transformation. Secondly, obtain the simulation samples by inverse Nataf transformation, and perform power system dynamic simulation. Thirdly, the random output is approximated as a linear combination of a single random input, and the mean and standard deviation of the random output under the impact of a single random input are calculated by Gaussian-Hermite quadrature. Then, calculate the mean and standard deviation of the random output under the impact of all random input. Finally, the effectiveness of the proposed method is validated on the IEEE 9-bus system and IEEE 39-bus system. Compared with Monte Carlo simulation and Latin Hypercube sampling, the proposed method can greatly reduce the simulation time for uncertain dynamic simulations while maintaining high accuracy.

The construction of an energy distribution network can improve the system’s ability to absorb new energy, and its stable and efficient operation has become more and more important. The security and stability analysis of a distribution network needs accurate distribution network model parameters as support. At present, the installation of PMU equipment in China’s distribution network synchronous phase angle measurement unit is limited, which brings challenges to the parameter correction of the distribution network. In this paper, an automatic correction algorithm of distribution network parameters based on the CIM model and measurement data is proposed for the distribution network system without PMU. Firstly, the distribution network topology construction technology based on XML files and key fields of the distribution network is proposed, and the non/small impedance devices (such as switches) are merged and reduced. The breadth-first traversal algorithm is used to verify the connectivity of the constructed topology. Then, based on the topology construction and the least squares method, an iterative parameter correction technique is constructed. Finally, the accuracy and effectiveness of the proposed algorithm are verified in a standard IEEE 33-bus system distribution network and an example of the China Southern Power Grid. The topology connections constructed based on the CIM model have significantly enhanced the efficiency of parameter correction.

Wind power hybrid is a comprehensive utilization method of new energy, which can greatly reduce energy pollution, but there is also the problem of tidal flow difference, so the power flow regulation of wind power energy is to be solved at present. In this paper, the digital simulation platform is used to comprehensively judge the grid-connected demand, collect data such as wind speed, power generation and energy storage, and regulate the power flow of the wind power hybrid grid in combination with the problem of wind speed randomness. Secondly, the intelligent control function is used to judge the wind turbine transmission power hybrid grid and the power flow of the distributed power grid to balance the stability of the overall power flow, voltage and current of the wind power hybrid power grid. The results show that the digital simulation cloud platform collects the power flow data of the wind power hybrid power grid, generates abnormal power flow points, and supplements the power flow through alternative methods, and its regulation and control effectiveness is greater than 90%, and the amplitude of maintaining the power flow is between 10~20%. The wind power grid's grid-connected power flow regulation time is within 10s, and there is no damage to the wind turbine and energy storage battery. Therefore, the digital simulation cloud platform can realize the power flow control of the wind power hybrid power grid, maintain its overall stability, and meet the needs of new energy grid expansion.

Typhoon weather poses a significant threat to the security and stability of the power system, as it can potentially lead to the disconnection of transmission lines. Concurrently, there exists substantial uncertainty regarding which specific lines may become disconnected under such weather conditions. The preventive security constrained optimal power flow (PSCOPF) is one of the main methods to address this issue. However, the current PSCOPF exhibits low computational efficiency due to the requirement of analysing all line faults through the N‐1 check. Therefore, this paper proposes a PSCOPF method based on two‐stage optimization. Firstly, the concentric relaxation method is employed to screen and obtain the critical line faults, thereby reducing the total quantity of security constraints associated with N‐1 line fault scenarios. Secondly, the incorporation of scenario transition constraints is introduced to decompose the PSCOPF model into OPF (optimal power flow) models under N‐0 scenarios and N‐1 critical line fault scenarios, then solve iteratively to achieve multi‐scenario decoupling. Finally, the proposed method was validated on the IEEE30 test system to demonstrate its capability to ensure economics in the N‐0 scenario and security in N‐1 scenarios.