| In recent years,blackouts caused by extreme natural disasters have occurred frequently.As the last link of providing power services,the reliability of distribution network is highly susceptible to extreme weather events.Therefore,it is of great significance to improve the resilience of distribution network in the face of extreme disasters to ensure the reliability of power supply.With the continuous development of DG and the large-scale deployment of clean energy,the randomness and complexity of power supply bring challenges to the operation of the system,and its load supporting role in extreme disasters is gradually explored.This study focuses on the resilience improvement technology of distribution network under extreme disasters.By mining the short-term support ability of various distributed power sources to the power loss load,a load recovery model of distribution network after extreme disasters is constructed.In order to improve the system economy and resilience,a solution for long-term resilience planning before disaster and shortterm load recovery after disaster is proposed.Firstly,the distribution network state model and resilience evaluation method are determined.In this thesis,starting from the three aspects of source-network-load,the initial models of the distribution network resilience problem are given respectively,and sets corresponding resilience indicators in stages to comprehensively evaluate its emergency power supply capacity.On the source side,the energy source model of resilient improvement of distribution network under extreme disasters is analyzed and established.On the grid side,by analyzing the influence mechanism and key parameters of typhoon disturbance,the typhoon intensity and component force model are formed,and the time-varying failure rate curve of distribution line is generated based on the component vulnerability model.On the load side,the normal distribution is used to fit the historical data of different load days at the same time to form a typical daily load change curve.Secondly,a post-disaster resilience improvement strategy for distribution network considering the uncertainty of DG is proposed.Considering the uncertainty of renewable energy output and fault location,the uncertainty scenarios are generated by LHS and Monte Carlo sampling respectively.An emergency optimal scheduling model for load recovery of post-disaster distribution network is established,and the original nonlinear model is transformed into a mixed integer second-order cone programming model by Big-M method and cone relaxation.For the two scenarios of large-scale power outage and typhoon disaster,different resilience improvement schemes are selected to verify the effectiveness of the proposed strategy.Finally,a stochastic optimization method for post-disaster resilience improvement of distribution network considering pre-disaster capacity configuration is proposed.The process is modeled as a two-stage stochastic optimization model combining long-term resilient programming and short-term post-disaster recovery.The first stage model optimizes the configuration scheme of distributed generation with the goal of minimizing the equivalent daily comprehensive cost.The second stage stochastic model takes into account the economy and resilience of post-disaster emergency scheduling.After transforming the random model into a deterministic model,the simulation verification is carried out in the improved IEEE-33 node distribution network.The simulation results are evaluated from two aspects of disaster prevention and mitigation.The results show that the proposed method can significantly reduce the investment operation and maintenance cost of the normal operation of the distribution network,and improve the resilience of the post-disaster distribution network.The thesis contains 42 figures,22 tables,and 74 references. |
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