| Building a power system integrated with high penetration of renewable energy resources has become the main goal of power system construction in China and even the world in the future.However,renewable energy power generation systems(such as wind power generation systems and photovoltaic power generation systems)are connected to the power grid through power electronic devices,which usually do not provide inertia support.The power system integrated with high penetration of renewable energy resources shows insufficiency of inertia response,which is a great threat to the system frequency stability.To deal with the challenge of frequency stability caused by insufficient inertia,the virtual inertia control of new energy units has been proposed to provide inertia response support.However,some studies have shown that unreasonable virtual inertia parameter setting may lead to system small-signal instability,which makes the problem of system small-signal stability more and more serious.Therefore,when setting the virtual inertia parameters,on the premise of providing sufficient system frequency response,the requirements of system small-signal stability should also be considered.Facing the stable operation requirement of the power system,this paper proposes a virtual inertia parameter optimization method considering the system frequency constraints and small-signal.stability constraints.The specific research contents are as follows:1.The analysis of the relationship between virtual inertia parameters and frequency constraints and small signal stability constraints.Through theoretical derivation,this paper demonstrates that increasing the virtual inertia parameter can help satisfy the system frequency constraint and ensure the system frequency stability.Further,through theoretical derivation and simulation analysis,it is verified that the virtual inertia parameters are closely related to system small-signal stability.The influence of virtual inertia on system small-signal stability does not have a general law.The influence may be related to the location and parameter setting of virtual inertia.The unreasonable virtual inertia parameters setting may cause low-frequency oscillation.Therefore,to ensure the stable operation of the system,the system frequency constraint and small-signal stability constraint should be considered when optimizing the virtual inertia parameters.Thus,this paper proposes a research framework for the virtual inertia parameter optimization method considering system frequency constraints and small-signal stability constraints from the aspects of“deterministic optimization,optimization algorithm acceleration,and probabilistic optimization”.2.A virtual inertia parameter optimization method considering system frequency response and small-signal stability.For the problem that the existing virtual inertia parameter optimization methods usually only consider the system frequency response requirements,but may lead to the deterioration of system small-signal stability,this paper establishes a virtual inertia parameter optimization model considering the system frequency response and small-signal stability.For the system frequency constraints include a transcendental function,the system frequency constraint is transformed into a linear minimum virtual inertia constraint to reduce the difficulty of solving the optimization model.The simulation results demonstrate that the virtual inertia parameters optimized by the proposed method can meet the system frequency constraints and improve small-signal stability,which is helpful to ensure the stable operation of the system.3.A fast frequency response parameter optimization method based on data-driven explicit stability constraints.For the problem that the system frequency constraints include transcendental function,and small-signal stability constraint is an implicit function based on differential equation,which makes the optimization model is difficult to be solved.The data-driven method is used to extract the characteristics of system frequency constraints and small-signal stability constraints,and transform the complex implicit constraints into linear explicit constraints.Thus,the nonlinear nonconvex optimization problem can be transformed into linear optimization problem.For generating the whole scene data is difficult,an efficient data generation method for quickly locating the stable boundary is proposed.By compressing the sampling space,the samples are gathered near the boundary,which can obtain a more accurate stable boundary with fewer samples.The simulation results demonstrate that the proposed method can improve the computational efficiency by about 4 times under the premise of satisfying the system frequency constraints and small-signal stability constraints.4.A virtual inertia parameter optimization method for system probabilistic stability improvementThis paper considers the uncertainty of wind power,further studies the relationship between probabilistic stability and virtual inertia parameter optimization.A virtual inertia parameters optimization model to maximize the probability of satisfying the system frequency constraint and small-signal stability is established.To solve this model,a probabilistic stability optimization algorithm for power system based on sensitivity analysis is proposed.For the difficulty of directly calculating the sensitivity of the objective function and virtual inertia parameters,damping ratio is used as an intermediate variable to obtain this sensitivity.The simulation results demonstrate that the proposed method can improve the probabilistic small-signal stability from 15.25%to 99.57% on the premise of satisfying the system frequency constraint.In summary,the proposed virtual inertia parameter optimization method can satisfy system small-signal stability constraints on the premise of satisfying the system frequency constraint requirements.The proposed method can provide technical support for ensuring the stable operation of power system through the optimization setting of virtual inertia parameters. |
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