Capacity value evaluation of wind farms considering the correlation between wind power output and load

Abstract

A method of capacity value evaluation for wind farms considering the correlation between wind power and load is presented. The paper starts with defining the metric of capacity value called capacity credit, and its basic evaluation process. Then the core part of capacity credit evaluation, which is the reliability assessment of power systems, is focused on. In this core part, two limitations of the frequently used cross entropy based importance sampling method are analysed. To solve the problems, an improved method is proposed by using truncated Gaussian mixture model as the proposal distribution of the cross entropy based importance sampling methods. This improved method is adopted to speed up the reliability assessment of composite power systems in the capacity credit evaluation. Finally, several numerical tests are designed and performed on the IEEE-RTS 79 and IEEE-RTS 96 test systems. The results show that the improved method is faster than traditional cross entropy based importance sampling methods when assessing the reliability of power system. Besides, the efficiency of the improved method is almost impervious to the correlation of load and wind power output, which ensures its applicability in different scenarios.