Modeling And Methodogy Study Of Several Power System Problems With Renewable Energy

The rapid developing of modern society has caused the severe dependency of energy.In order to prevent the potential energy crisis,many countries have been working on finding replacement of traditional energies(e.g.thermal power,gas power)just like hydro power,solar power,wind power,etc..Not only be valuable supplement for the traditional power,renewable energies could even be the dominant types of energy in the near future.Therefore,the study of renewable energy has both strong theoretical value and profound social significance.Based on the analysis of up-to-date researches at home and abroad,several research topics around power system optimization with renewable energy are discussed in this thesis.The main research contents and achievements are summarized as follows:(1)With the study of wind power investment under an electric power market,we propose a bilevel stochastic programming with the consideration of topology control.The upper level problem is to optimize the profit of wind power investment by the decision maker;the lower level problem is to minimize the carbon emission in the market clearing problem by the power system operator.According to the bilevel structure,we propose a customized decomposition method.The experimental results show the efficiency and effectiveness of proposed models and method.(2)With the study of dynamic pricing with wind power,we propose a wind power investment problem with bilevel structure.In the upper level problem,the wind power investor parallelly make the decision of siting and sizing of wind power while the pricing of wind power is determined by the lower level problem;The lower level model is to optimize the power transmission plan in the market clearing problem.The pricing of wind power is associated with the locational marginal price(LMP)in the lower level problem.The model is solved with Column-and-Constraint Generation(C&CG)algorithm.The experimental results illustrate the application of dynamic pricing in this bilevel problem.(3)With the consideration of uncertainty from wind power intensity and demand level,we propose a pessimistic robust bilevel programming with hybrid investment of wind power and transmission line.The investor in the upper level problem is sensitive with the optimal profit under "worst conditions" of uncertainty.The system operator in the lower level problem is to optimize the transmission plan based on the investment plan from upper level.The uncertainty factors are described as a set of linear constraints.Then we propose a iterative decomposition method to recognize those "worst conditions" and report to the investor.The results show the effectiveness of proposed method.(4)With the concern of power safety,we propose a islanding problem to intentionally isolate the transmission network with topology control.The islanding strategy could prevent global damage of transmission system by isolating the potential crisis in the subnet.The bugs could be rapidly awared and fixed within the isolated subnet.Due to the lack of ability to solve large scaled system by islanding model,we propose a column generation based method.The experimental results show the validity of model and the efficiency of proposed method.