Reserve Decision And Stochastic Small Signal Stability Analysis Of Power Systems With Wind Power

Wind power generation, as a kind of clean and renewable energy, is the most important form of wind energy utilization. The development of wind power generation has a great significance to improving energy structure, diversification of energy supply, combating climate change and environment protection. Large scale, centralized exploitation, long distance and high voltage transmission are the key features of wind power development currently. Compared to schedulable and controllable conventional power source, the stochastic and intermittent nature of wind power has brought significant impact on power system operation and stability. Conventional stability analysis methods which are based on deterministic state equations are sometimes incapable of dealing with the stability problems caused by the stochastic fluctuation of wind power. It is necessary to introduce stochastic differential equation and stochastic stability theory to study the stability of power systems with large scale wind power generation. Focused on the different aspects of the impacts of stochastic wind power on system state equations, this paper presents an in-depth research on the stochastic small signal stability and operating optimization of power systems with wind power generation. As to the impact of wind power uncertaity on system equilibrium point, the wind power uncertainty modelling and power system generation dispatch optimization problem is investigated; based on stochastic differential equation theory, this paper carries out a research on the stochastic dynamic modeling of wind turbine generators, and furthuremore studies the stability modeling and analysis methods for power systems with stochastic excitation and stochastic coefficients respectively. The research aims at expanding stability theory from deterministic environment to stochastic environment. Detailed research outcomes are as follows:1.A wind power uncertainty modeling method is proposed. Aiming at overcoming the drawback of existing scenario reduction methods, which is that they are incapable of dealing with extremely massive initial scenarios, this paper proposes an improved scenario reduction method based on PSO algorithm. The search space of the method is the whole initial scenario set, but the update of velocity and position of each particle are only affected by its previous best position and the Kantorovich Distance between the rest particles of the swarm. Compared to existing method, it no longer needs to traverse the whole initial scenario set during each iteration, which can greatly decrease computing time and effectively solve the extremely massive initial scenarios reduction problem, which laid a foundation of further research on generation dispatch with wind power generation.2. Aiming at the impact of wind power uncertainty on system equilibrium point, the generation and reserve dispatch optimization of power system with wind power generation is studied. A reliability index which considers the reliability requirements of different types of load is proposed based on fault scenarios. Furthermore, the quantification formula of system reserve requirement is deduced based on the reliability index considering the forced outage rate, load forecast error and wind power forecast error. The reserve quantification formula is then used as a constraint to establish a coordinated power generation and reserve dispatch model of power system with wind farms. Through optimization, not only can we get the reserve requirement and total reserve supplied by the system of each period, but also the optimal reserve allocation among thermal units. The effectiveness and accuracy of the model are validated through simulations. The impact of wind power integrated to the system and substitution of conventional units are also discussed. The proposed method can properly handle the reserve quantification and allocation problem with large-scale wind power, enhance system operation stability and provide a basis for calculation of system equilibrium point, which is the foundation of further stability analysis research.3. Based on the Ito stochastic differential equation theory and stochastic stability theory, the stochastic small signal stability mechanism of power systems as affected by stochastic excitation is analyzed. The mechanical power input of asynchronous wind turbine is considered as a stochastic process, and the stochastic dynamic models of asynchronous wind turbine generators considering stochatic excitation are established. These models have avoided the disadvantage of Riemann integral (Riemann integral is unable to deal with the stochastic element in the integrand), and expanded the power system dynamic model from deterministic ordinary differential equation frame to stochastic differential equation frame. Furthermore, the system stochastic mean stability and mean square stability criterion are proposed and proved; furthermore, the expectation and variance of system response are obtained by mathematical deduction. The proposed stability criterion is simple and clear; the calculation method of statistical features of system response can help better depict the dynamic response of systems with stochastic excitation and understand the system operating condition. Numerical simulations are performed to verify the effectiveness and accuracy of the proposed method.4. The stochastic small signal stability mechanism of power systems considering the random coefficients is analyzed. The impacts of stochastic wind mechanical power on coefficients of system state equations are considered and a system dynamic model with random coefficients is estabilished based on Ito stochastic differential equation. By using Ito formula, the stochastic mean square stability problem of this system is converted to mean stability problem of deterministic system. The stochastic mean square stability criterion is proved using Lyapunov function; furthermore, the system stochastic stability probability calculation method is obtained by using power system stochastic parameter sensitivity analysis method. The effectiveness and accuracy of the proposed method are validated by numerical simulation. This method can calculate the system stochastic mean square stability probability fast and effectively. Although the results are a little conservative, the method is strict and reliable and has low time cost due to it is analytical.