Abstract
Capacity credit (CC) evaluation is a conception used in power system planning, which quantifies the contribution of various generating resources to the power system reliability. CC evaluation is essentially an iterative process of solving equations, where the reliability index of the power system is computed in every round of iteration. Therefore, fast and accurate evaluation of CC requires both a competent reliability assessment method and a compatible iterative method for equation solving. From this perspective, this paper presents a new methodology of CC evaluation for wind energy, by combining an improved cross-entropy-based importance sampling (ICE-IS) method for reliability assessment and a robust secant method for equation solving. The ICE-IS method is able to substantially accelerate the computation of reliability indices, especially when the wind power output and the system load are highly correlated. The robust secant method provides an unbiased estimate of the real CC value and ensures fast convergence, despite the error in the reliability index computed by ICE-IS in each round of iteration. Numerical tests are designed based on the historical data of two actual wind farms to prove the correctness of the proposed methodology. Besides, the results are discussed to analyze the impact of different factors on the CC of wind energy.
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