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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.

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The current game theory model method cannot accurately optimize the load control of smart grid, resulting in the problem of high load energy consumption when the smart grid is running. To address this problem, a load optimal control algorithm for smart grid based on demand response in different scenarios is proposed in this paper. The demand response of smart grid under different scenarios is described. Onthis basis, the load rate and actual load of smart grid are calculated by using the rated load of electrical appliances. The load classification of smart grid and the main factors affecting the load of smart grid are analyzed to complete the load distribution of smart grid. According to the evaluation function of smart grid, the number of load clusters is adjusted to calculate the load change rate. The trend of load curve of smart grid is analyzed to realize optimal load control of smart grid under different scenarios. The experimental results show that the proposed method has better control performance and higher accuracy through load control of smart grid.

This paper has developed a practical and economical method for energy storage system (ESS) configuration to smooth the power fluctuation in distribution network feeders. The method firstly uses historical load data and the output of renewable power plant to analyse the power fluctuation in the feeder over the required time period. Then the configuration of ESS system, in terms of rated power and capacity, is calculated and determined to minimise and reduce power fluctuation in the feeder over different time-window and operating period. It is found that the ESS configuration is closely related to the operational period for which the ESS is required to smooth the feed power fluctuation, such as over typical a day or year. Furthermore, it is shown that the requirement of ESS is larger when it is required to smooth the feed power fluctuation over an operation period of one year than a day. Index Terms--Distribution network, Energy storage system (ESS), Operational period, Rated power and capacity, Smoothing fluctuation I.