Study On Reliability Assessment Model Of Wind Power Integration In Bulk Power Systems

With the increasing severity of energy and environment problems, renewable energy is competitively developed all over the world. The wind energy was developing fastest among all kinds of renewable resources due to its a kind of renewable green power resources, its low cost and technical maturity. Unlike the other power plants, the active power from wind farms varies all the time, which may influence the operation and stability of power network after connection. To study the impact of the wind farm on the system reliability, this thesis presents a reliability assessment model of wind power integration in power systems based on the Monte Carlo simulation approach, which considers the randomicity of the wind and wind power generation forced outage rate. The main work has been done as follows:①At first the thesis introduces the development, method, significance and research progress of reliability assessment of wind power integration in Bulk Power Systems at home and abroad.②The reliability evaluation methods are usually divided into two kinds, that are analytical and simulation methods, and the simulation approach has been received consideration attention for its flexibility and practicality. Based on different sampling techniques, there are three kinds of simulation methods which are nonsequential Monte Carlo simulation, sequential Monte Carlo simulation and state transition sampling. This thesis details their fundamental principles and analyzes their merits and drawbacks. Moreover, utilizing the annual reliability indices samples and nonparametric kernel estimation technique, this thesis realizes the probability density estimation for reliability indices. This probability density information can facilitate us to discover system risks from the internal distribution laws and structural features of reliability indices. The proposed methods are verified using RBST and IEEE-RTS79 systems.③Wind speed forecast is an important task of wind farms planning. Based on time series analysis, the thesis presents a wind speed forecast model, which considers timing and auto-correlation characteristic of wind speed. The distribution characteristic of the forecasted wind speed are compared with those of measured wind speed in a calculative example, which shows that the auto-regressive and moving average (ARMA) model is feasible in wind speed forecast for wind farms.④This thesis presents a wind farm reliability assessment model based on the sequential Monte Carlo simulation using the reliability models of the conventional generators, lines and the transforms, which considers the randomicity of the wind and wind power generation forced outage rate. The reliability of Bulk Power Systems contain wind farms is evaluated in term of the principle that wind power is employed sufficiently, according to meeting system security constraints.⑤Combining the wind farm reliability assessment model with the Bulk Power Systems, the reliability of wind power integration in Bulk Power Systems is evaluated. The impacts of adding wind farms to system, changing of penetration and wind speed on reliability of power system is analysed. The study provides the important reference value for wind farm operation and planning. Moreover, the probability density estimation of the five casesare given and compared based on kernel density estimation technique.