Research On Intentional Islanding And Vulnerability Analysis Of Complex Power Systems

With the development of economy and the increase of electricity demand,the scale of power grid is expanding,and meanwhile the pattern is becoming more and more complicated.The changes in the scale and complexity of power grids have highlighted the problem of safe operation.On one hand,the rapid development and extensive permeation of distributed power generation,distributed energy storage,electric vehicles,controllable loads and various intelligent devices,which have caused significant changes in the power system,especially in the medium-voltage and low-voltage distribution networks,breaking the pattern of one-way power flow in the traditional distribution network.These facilities are capable of two-way interaction,and may participate in the operation of the power system.In emergency situations,we can divide the distribution network into multiple isolated islands with different sizes and independent operation to improve the reliability of power supply.On the other hand,the modern power grid has gradually morphed into a deeply coupled cyber-physical power system(CPPS).The extensive application of advanced information and communication technology effectively improves the controllability and observability of power systems.At the same time,the complex coupling relationship between the communication system and the power grid brings potential security threats to the operation of the power grid.A minor fault in a unilateral network may lead to large-scale spread through interaction between systems.In order to ensure the safe and stable operation of the power grid,we need to analyze the coupling relationship between the cyber layer and physical layer,explore the co-evolution mechanism of faults in the double-layer coupled system,and evaluate the impact of the communication system on the vulnerability of the power grid.Therefore,our research mainly focuses on intentional islanding of the distribution network,community detection in the power distribution network,the interaction mechanism in the cyber-physical grid,the coupling relationship,integrated vulnerability assessment,and the modeling of fault cooperative propagation in two-layer networks and so on.The main contributions of this thesis are summarized as follows.Firstly,intentional islanding is an effective approach to avoid large-scale blackouts and minimize outage losses through utilizing distributed power generations.This paper proposes an intentional islanding method based on the artificial bee colony(ABC)algorithm.The artificial bee colony algorithm has fewer control parameters,strong robustness,and high accuracy.We study the node electrical relevance and employ the ABC algorithm to search islands,fully take into account the power system constraints such as load priority and capacity.And then the initial island scale are checked and modified to ensure stable operation.We test the proposed method on IEEE69-bus system and compare it with other strategies.The simulation results demonstrate that the proposed strategy is more feasible and efficient.Secondly,in view of the similarity between the comm unity detection in complex networks and the regional autonomous operation of each generator in distribution networks,we put forward an intentional islanding method for distribution networks based on the theory of community detection.In this method,a new index "electrical edge betweenness" is defined,which fuses electrical characteristics with grid topology of actual power lines.Based on this,the Girvan-Newman algorithm is employed to detect the community structure in the power grid.Referring to the modularity value(function Q),we obtain a reasonable amount of communities.Considering coherent generator groups and the stable operation constraints,we can get the regions of communities,and then the whole distribution network is partitioned into several self-sustainable islands.The effectiveness of the proposed method is verified by simulation and comparison with other methods.Thirdly,the strong coupling between the power grid and the communication system may contribute to failure propagation,which may easily lead to cascading failures or large-scale blackouts.In this paper,in order to quantitatively analyze the impact of interdependency on power system vulnerability,we put forward a“degree-electrical degree” independent model of cyber-physical power systems(CPPS).That's a new type of assortative link,through identifying the important nodes in a power grid based on the proposed index –electrical degree,and coupling them with the nodes in a communication system with a high degree,based on one-to-one correspondence.Using the double-star communication system and the IEEE 118-bus power grid to form an artificial interdependent network,we evalua te and compare the holistic vulnerability of CPPS under random attack and malicious attack,separately based on three kinds of interdependent models: “degree-betweenness”,“degree-electrical degree” and “random link”.The simulation results demonstrated that different link patterns,coupling degrees and attack types all can affect the vulnerability of CPPS.The CPPS with a “degree-electrical degree” interdependent model proposed in this paper presents a higher robustness in the face of random attack,while behaves miserably in the face of malicious attack.Fourthly,from the perspective of propagation dynami cs in complex networks,the fault propagation in cyber-physical power systems is analogous to epidemic spreading,and then the cyber nodes and power nodes are regarded as individuals in two groups respectively.In this paper,a two-layer interdependent network model of the cyber-physical power system is established,where each sub-network adopts the SIS(Susceptible Infected Susceptible)epidemic spreading model.On this basis,we construct a fault cooperation propagation model of cyber physical power syste ms.Furthermore,we introduce the node protection mechanism to ensure the normal operation of key nodes.The generated scale-free cyber network and IEEE118-bus power system are used for simulation to analyze the influence of the coupling effect between them on the final failure scale.