Juan Wen

The occurrence of multiple coupled extreme weather disasters leads to complex operational mechanisms and resilience characteristics in ADN. Considering the fault evolution mechanism under multiple extreme weather disasters, a comprehensive resilience evaluation method of ADN is proposed. First, the spatiotemporal evolution patterns of typical extreme weather events such as typhoon, rainstorms and lightning are analyzed. Based on the material properties of components and the structural characteristics of distribution lines, a mathematical model is developed to quantify the fault probability of ADN under compound extreme weather conditions. Second, the post-fault dynamic response characteristics of ADN are investigated. A unified fault modeling approach is established using Monte Carlo sampling combined with Shannon entropy theory to extract the operational and load features of ADN at different stages. Then, a full-stage resilience evaluation metric system is constructed, covering pre-disaster prevention, in-disaster adaptation and post-disaster recovery. Load performance curves are analyzed from both temporal and power dimensions, and Metric weights for each stage are determined using a hybrid subjective-objective weighting method. The overall system resilience score is calculated using the Euclidean weighted distance method.Finally, a feasible ADN model was constructed for simulation experiments.The simulation results indicate that, the proposed approach can precisely evaluate the overall performance loss of the system subjected to extreme weather events while successfully detecting its resilience-critical segments. With demonstrated accuracy, this framework provides a reliable reference for disaster resilience planning and weak link identification in ADN.

The robustness of complex networks to various kinds of attacks that could trigger cascading failures has attracted increasing attention. Most existing studies fail to consider that the cost to attack each network component (node or edge) may be unequal. Therefore, in this paper, we explore the network robustness to cascaded attacks based on heterogeneous costs. We introduce an attack cost model with both cost-sensitive and budget-constraint parameters. On this basis, three attack strategies are considered, including hub strategy, average degree strategy, and leaf strategy. Their cascaded attack effects are compared by considering the load local preferential redistribution rule. Both the fraction of failed nodes and the value of a new robustness metric, i.e. the budget-constraint threshold, are monitored in different complex networks. Numerical experiments indicate that as the attack cost changes from homogeneity to heterogeneity, the performance of the classic hub strategy decreases gradually. For the situation of weak heterogeneity of attack cost of each node, leaf strategy achieves the maximum attack performance gradually. Moreover, the budget, network structure, and robustness metrics may all affect the selection of the optimal attack strategy.

Vulnerability assessment is one of the effective ways to prevent cascading failures of a distribution network (DN). Aiming to accurately identify the vulnerable parts of the DN, a comprehensive assessment method based on Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) considering topological structure and operation status is proposed. Considering the radial topology of DN, the structural vulnerability indices based on complex network theory are extended, such as degree and network efficiency. According to the system power flow and the network topology, the state vulnerability indices are established, such as electrical betweenness and power flow transfer entropy. Combining the Analytic Hierarchy Process (AHP) and the entropy weight method to obtain the comprehensive weight to give weight to each vulnerability index. The weighted vulnerability indices are substituted into the TOPSIS method to calculate the comprehensive vulnerability of each node and line in the DN where the obtained results are corrected by the grey correlation degree. The system transmission efficiency index is established to describe the degree of system performance degradation of the DN under different attack strategies. The simulation results of IEEE 33-bus test case show that the proposed method can effectively identify vulnerable nodes and lines in the DN.

Network optimization is one of an effective ways to enhance the performance of an active distribution network (ADN). Aiming to improve the operation and power quality of the ADN considering time variations in load and renewable distributed generation (RDG) power, a multi-time period optimization model and its dynamic solution method are proposed. Considering the real time load demand and power generation variation of RDG versus input parameters like wind speed and solar irradiance, the time variation models of load and RDG power output are developed. The minimum power loss and maximum absorption of RDG power are served as the optimization indexes to construct the dynamic muti-time period optimization model. A hybrid particle swarm optimization (HPSO) algorithm is presented based on integer coding and random coding technique, which can find the most satisfactory solutions for the proposed dynamic model. Considering the time variation of load and RDG power of ADN, the optimal network structure and RDG allocation scheme at any time interval are determined by analyzing the obtained solutions. Additionally, two ADNs with time variation in load and RDG are tested to verify the effectiveness and superiority of the proposed dynamic optimization model and HPSO algorithm. The simulation results show that the proposed method can improve the operation performance and RDG optimal utilization of the ADNs through muti-time period dynamic optimization.

Service restoration of distribution networks in contingency situations is one of the highly investigated and challenging problems. In the conventional service restoration method, utilities reconfigure the topological structure of the distribution networks to supply the consumer load demands. However, the advancements in renewable distributed generations define a new dimension for developing service restoration methodologies. This paper proposes a hierarchical service restoration mechanism for distribution networks in the presence of distributed generations and multiple faults. The service restoration problem is modeled as a complicated and hierarchical program. The objectives are to achieve the maximization of loads restored with minimization of switch operations while simultaneously satisfying grid operational constraints and ensuring a radial operation configuration. We present the service restoration mechanism, which includes the dynamic topology analysis, matching isolated islands with renewable distributed generations, network reconfiguration, and network optimization. A new code scheme that avoids feasible solutions is applied to generate candidate solutions to reduce the computational burden. We evaluate the proposed mechanism on the IEEE 33 and 69 systems and report on the collected results under multitype fault cases. The results demonstrate the importance of the available renewable distributed generations in the proposed mechanism. Moreover, simulation results verify that the proposed mechanism can obtain reasonable service restoration plans to achieve the maximization of loads restored and minimization of switching operations under different faults.

Network reconfiguration is a vital analysis process for optimising and controlling distribution systems (DSs). Given a stable initial DS, the distribution feeders can be reconfigured by changing the status of the switches to improve the operational performance. Owing to the fault trip, equipment maintenance, and system extension, the removal or addition of a branch can change the structure of the initial topology. This study proposes a dynamic reconfiguration method that considers the real‐time initial topology variation. The method combines the dynamic topology analysis with network reconfiguration to address the optimisation problem of the real‐time distribution network. A set of independent topological parameters represents the initial topology of a DS. After the changes in the initial topology are detected, the dynamic topology analysis is used to restore network connectivity and identify out‐of‐service areas. Then the real‐time initial topology is obtained by updating the topological parameters. In accordance with network reconfiguration, the optimal configuration is identified to reduce power loss and improve the voltage profile of the real‐time distribution network. Illustrative examples verify the effectiveness of the proposed method.

Node importance degree is a vital index in distribution network reconfiguration because it reflects the robustness of the network structure by evaluating node importance. Since the traditional reconfiguration ignores this index, the reconstructed network structure may be vulnerable which would reduce the security and stability of the distribution systems. This paper presents a novel reconfiguration strategy considering the node importance. The optimization objectives are the improvement of the node importance degree and the reduction of power loss. To balance the objectives, the reconfiguration mathematical model is formulated as a compound objective function with weight coefficients. Then the quantum particle swarm algorithm is employed to address this compound objective optimization problem. The strategy can model different scenarios network reconfiguration by adjusting the weight vector based on the tendencies of the utility decision maker. Illustrative examples verify the effectiveness of the proposed strategy.

The structure of a distribution network has great effects on economy, power supply reliability and investment of a power system. To obtain an optimal topology from possible topologies, we need to solve an optimisation problem which aims to find a radial structure satisfying operating constraints. As a basis of solving this optimisation problem, the encoding scheme, is to represent the candidate configurations by a series of codes. Numerical candidate topologies and unfeasible codes would lead low efficiency or premature convergence. This paper presents an effective scheme which can rapidly produce all radial configurations of a distribution network. In order to reduce the computational requirement of solution space, initial network is simplified as a topological graph which reserves loop branches and T-nodes. And a loop-branch chain incidence matrix is derived from analyzing the relationship between any two loops. Then the principles of selecting switches of each variable are designed to determine the ranges of the variables. All radial candidate solutions are available rapidly through applying the theory of combination. The scheme presented minimizes the number of solutions and avoids tedious radial checking procedure in view of avoiding any infeasible solutions. The validity of the proposed scheme is verified by illustrative examples.