Reliability Evaluation Of Sichuan Power Network Using Monte Carlo Simulation Technique

Probabilistic risk evaluation can provide us an effective tool to quantitatively analyze the reliability performance and identify the weak parts of bulk power systems. With the rapid development of power systems today characterized by its interconnection, long distance transmission lines, ultrahigh voltage and large generation set, modern power systems have been the highly complex and dynamic systems., therefore the blackout risk resulted from random failures of system components is increasing. In addition, the probabilistic characteristics of power market operation in the new deregulated environment result in frequent changes of power network operation modes. To protect the power system health form the above random factors and maintain adequate reliability performance, the operators of control room often face great pressures because the existing deterministic analysis tools can not provide the quantitative indices of system risks. However, the probabilistic risk assessment can reveal the real risks of system interruptions in probabilistic sense, and provide significant reference information for power system planning, design and operation.The 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 (state sampling), sequential Monte Carlo simulation (state duration sampling) and state transition sampling. This thesis details their fundamental principles and analyzes their merits and drawbacks. Then the convergence features of Monte Carlo simulation are researched to discover the probabilistic uncertainty relation between the calculation accuracy and sampling sizes. 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, IEEE-RTS79 and IEEE-RTS96 systems.According to the network topology and components reliability and electric parameters of Sichuan 500kV power network, this thesis carries out reliability evaluation using these three kinds of Monte Carlo simulation approaches. Four cases of power grid operation modes are taken into consideration, which are peak load in flood Season, peak load in dry season, valley load in flood season and valley load in dry season. The reliability indices of the four cases are compared and their weak parts are analyzed based on the sensitivity indices. Moreover, the probability density estimation of the four casesare given and compared based on kernel density estimation technique.