An Alternative Method for Estimating Wind-Power Capacity Credit based on Reliability Evaluation Using Intelligent Search

More and more wind power is being integrated into power grids in recent years. However, due to its intermittent characteristic, it is usually difficult to determine the appropriate penetration level to ensure a specified reliability requirement. For this purpose, the proper calculation of wind power capacity credit is of particular importance which is useful in both operations and planning stages of hybrid power systems with multiple power sources. The capacity credit of wind power can usually he calculated based on a reliability index termed Loss of Load Expectation (LOLE). In this study, the population-based intelligent search (PIS) procedure is adopted to calculate LOLE, which has turned out to be quite effective in reliability evaluation in certain scenarios such as highly reliable and complex systems. Here genetic algorithm, a representative PIS procedure, is used to find out dominant failure states which can be used to calculate the LOLE. A comparison study is conducted in relation to the Monte Carlo simulation conceptually and numerically. Also, the chronological method is examined to illustrate that the proposed method is a viable alternative approach by achieving comparable results.