Abstract
This paper concentrates on the capacity credit (CC) evaluation of wind energy, where a new method for constructing the joint distribution of wind speed and load is proposed. The method is based on the skew-normal mixture model (SNMM) and D-vine copulas, which is used to model the marginal distribution and the correlation structure, respectively. Then a cross entropy based importance sampling (CE-IS) is improved to enhance the efficiency of the power system reliability assessment, which is a crucial part of the CC evaluation. After that, the proposed methods are adopted to combine with the secant method to develop a complete algorithm to calculate the CC of wind energy. Numerical tests are designed and carried out based on the IEEE-RTS 79 system and wind speed data obtained from four wind farms in Northwest China. In order to show the superiority of SNMM and D-vine copula, the goodness-of-fit is quantified by different statistics. Besides, the improved CE-IS method is validated by comparison with Monte Carlo sampling (MCS) and traditional CE-IS in the efficiency of reliability assessment. Finally, the proved methods are combined with the secant method to calculate the CC of four wind farms, which can provide information for wind farm planning.
Highlights
Clean energy resources such as renewable energy sources and flexible demand have been introduced into power systems in order to establish an environment-friendly society [1,2,3,4]
When we evaluate the capacity credit (CC) of wind energy, we use the data of wind power rather than wind speed
This paper focuses on the capacity credit evaluation of wind energy, whose key points are modeling the uncertainty of the correlated wind speed and load and assessing the power system reliability
Summary
Clean energy resources such as renewable energy sources and flexible demand have been introduced into power systems in order to establish an environment-friendly society [1,2,3,4]. Gaussian copula in modeling asymmetrical dependence structure [23] It was concluded in [24] that the performance of multivariate Archimedean copulas deteriorates drastically if the number of wind and load sites increases. There have been researchers introducing the pair copula into the optimal planning and reliability assessment of power systems, whose graphical structure is called C-vine structure [26,27]. To solve the first and second problems, we develop a method of constructing a joint PDF by combining SNMM with D-vine copulas. On the basis of the SNMM, D-vine copulas and DRCE-IS, we can obtain reliability indices rapidly and precisely, offering necessary information for the secant method to further evaluate the CC of each wind farm.
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