Research On Complex Power Grid Modeling Based On Complex Network Theory

Although the large-scale interconnection of power grid enables the comprehensive utilization of distributed energy and optimal dispatch of electric power resources, it can expand the scope of system disturbance. With the growing size of power grid and the increasing complexity of its structure, studying the mechanisms of cascading failures and large-scale blackouts with traditional methods based on reductionism has shown some limitations. Applications of complex network theory in studying the statistical properties and dynamics of complex power grid have become a hot research subject in power system.This dissertation firstly introduces the complex network theory and summarizes its application in power system research area, and then focuses on the topology evolving model of power grid, the model for cascading failure, and the model for self-organized critical state evaluation. This dissertation is organized as follows:A new evolving network model is proposed and studied. The model is based on the new concept of neighbourhood connectivity, which exists in many physical complex networks. The statistical properties and dynamics of the proposed model is analytically studied and compared with those of Barabasi-Albert (BA) scale-free model. Numerical simulations indicate that this network model yields a transition between power-law and exponential scaling, while the BA scale-free model is only one of its special (limiting) cases. Particularly, this model can be used to enhance the evolving mechanism of complex networks in the real world.A novel local-world evolving network model for complex power grid is proposed and studied, which is based on the basic local-world evolving network model and considers connections outside the "local-world" of new nodes. The degree distribution and its expressions of this new model are analytically studied based on mean-field theory. It is found that the degree distribution of this new model has a power-law tail, and its scaling exponent is between 3 and∞. Numerical simulations indicate that the degree distribution of the new model fits that of the Northern China power grid and that of the Western America power grid well. Furthermore, it is proved that power grid is neither a random network nor a scale-free network.A temporal and spatial evolving network model for complex power grid is proposed and studied, which considers the impact of physical distance. The concept of growing-points defined as positions nearby the existing nodes but not occupied is proposed. The position of a new node is selected from candidate growing-points randomly. The probability of an existing node connecting to the new node is calculated based on the degree of the existing node and the physical distance between them. The statistical properties of the proposed model are analytically studied. Comparisons of the Western America power grid and the Northern China power grid with some typical network models on some important topological properties such as average degree, clustering coefficient, characteristic path length, and degree distribution indicate that the new network model captures the essential features of real-world power grid.An improved cascading failure model considering topology evolvement of power grid is proposed. This model integrates the basic OPA model with temporal and spatial evolving network model for power grid topology. OPA model simulates the evolvements such as load increase, transmission line update, and cascading failures. Temporal and spatial evolving network model simulates the evolvement of topology. Influences of different evolving parameters on the blackouts are studied based on the new model. This model provides a way to study the long-term effects of different planning schemes and parameters for power grid.An identification model for self-organized critical state of power grid is proposed based on the entropy theory and the new concept of power flow entropy. Simulation results on the IEEE 118-bus system and the New England 39-bus system indicate that power flow entropy has important influence on the spread of cascading failures, and is one of the key factors determining if power grid enters the self-organized critical state. The values of power flow entropy of the IEEE 118-bus system and the New England 39-bus system when they enter the self-organized critical state under given load levels are analyzed.