Research On Robust Transmission Network Expansion Planning And Operation Optimization With High-penetration Wind Power Generation

Wind power is a kind of renewable energy,the development of wind power is conducive to improving the energy structure.Because wind power has the characteristics of non-control,stochastic volatility,and anti-peak regulation,the traditional transmission expansion planning and operation optimization method will be not applicable when the large-scale wind power integration on power system.Based on research of expansion planning and operation optimization of transmission system with wind power connected at home and abroad,this paper points out that advanced planning and dispatch technology is the foundation and guarantee for accommodating the wind power fully.Aiming at the access problem of high proportion wind power,this paper improves the existing transmission network planning and reserve optimization from three aspects.Firstly,the robust expansion planning strategy based on the extreme scenario is adopted to ensure the robustness of the planning strategy to the extreme scenario of wind output.Secondly,a robust planning method for transmission network embedded with wind power probability information is proposed,which can reasonably weigh the conservativeness and economy of the planning scheme.Thirdly,a multi-source cooperative reserve optimization model considering wind power uncertainty is established to improve wind power admissibility from the perspective of robust reserve operation.The specific work is as follows:Firstly,the robust optimization theory which can deal with uncertain parameters is introduced in detail,and the basic principles of Benders decomposition algorithm,C&CG decomposition algorithm and genetic algorithm are introduced,which lays the foundation for the following chapters.Secondly,the physical concept of wind electrode limit scenario is proposed,and a two-stage robust planning method based on wind electrode limit scenario is constructed.A two-stage algorithm based on Benders decomposition is proposed to solve the model effectively.Compared with the classical three-level robust optimization method,the proposed method can guarantee the robustness of the planning strategy to the wind electrode limit scenario and effectively reduce the excessive conservatism.At the same time,the method proposed in this chapter avoids the appearance of non-convex bilinear terms and is more suitable for practical engineering applications.Through the simulation of Garver’s 6-bus system and IEEE RTS-79 system,it is found that the proposed robust planning model of transmission network can effectively reduce the amount of air discarded and load shedding in operation phase,and significantly reduce the operation cost and total planning cost of the system.Thirdly,a mixed probability uncertain set is constructed by modeling the uncertain probability characteristics of renewable energy,and a probability-driven transmission planning investment planning model is established to deal with the worst scenario probability fluctuations.To solve the proposed model,a linearization method with mutual exclusion constraints is proposed to eliminate the absolute terms in the mixed probabilistic uncertain sets,and a parallel C&CG decomposition algorithm is constructed to adapt to the application of large-scale transmission systems.The effectiveness and superiority of the proposed model and algorithm are verified by an example of a practical power grid system in the Northwest China.Finally,in view of the new difficulties brought by the randomness and fluctuation of wind power output to the optimal allocation of operation reserve,the uncertainty of the wind power is described with polyhedron uncertainty set instead of conventional box uncertainty set,and an expected risk model based on the probability of wind power is constructed.In order to reasonably make a trade-off between benefits and risks in spinning reserve optimization,an uncertainty-set optimization model of wind power with cooperatively scheduling of multi-power units is established by evaluating the potential risk of the uncertainty of wind power.Besides,a two-level optimization method combined with adaptive genetic algorithm and branch & cut algorithm is proposed to solve the MINLP model.The results indicate that,the proposed method can adaptively search for the optimal uncertainty-set of wind power,which can reasonably balance the benefit and risk of spinning reserve.Moreover,the proposed model is adaptable to different wind power prediction accuracy,different load costing coefficients and different wind power inversion characteristics,so as to achieve the optimization of the comprehensive cost.