The quantitative evaluation of cluster wind power output volatility and source-load timing matching is vital to the planning and operation of the future power system dominated by new energy. However, the existing volatility evaluation methods of cluster wind power output do not fully consider timing volatility, or are not suitable for small sample data scenarios. Meanwhile, the existing source-load timing matching evaluation indicator ignores the impact of wind power permeability on the timing matching degree between wind power output and load. Therefore, the authors propose quantitative evaluation methods of cluster wind power output volatility and source-load timing matching in regional power grid. Firstly, the volatility-based smoothing coefficient is defined to quantitatively evaluate the smoothing effect of wind-farm cluster power output. Then, the source-load timing matching coefficient considering wind power permeability is proposed to quantitatively evaluate the timing matching degree of regional wind power output and load, and the corresponding function model of volatility-based smoothing coefficient and source-load timing matching coefficient is established. Finally, the validity and applicability of the proposed methods are verified by MATLAB software based on the actual power output of 10 wind farms and actual grid load in a certain grid dispatching cross-section of northeast China. The results demonstrated that the proposed volatility-based smoothing coefficient can accurately represent the smoothing effect of wind farm cluster power output while maintaining the volatility continuity of wind power output time series and without affect from the data sample size. The source-load timing matching coefficient can accurately characterize the difference in the timing matching degree between wind power output and grid load under different wind power permeability and the influence degree on grid load.
Active frequency drift islanding detection method is the most widely used in the distributed generator islanding detection. Now the domestic researches of islanding detection in China are base on algorithm Improvement but without very effective, because the fundamental reasons about the NDZ have not been researched and explained deeply. Therefore, the paper analyses the performances of the frequency drift islanding method from different aspects and deeply explains the reasons conducting the NDZ of the islanding detection from the aspects of load frequency characteristic and SFS method phasefrequency characteristic, which are the major factors affecting the phase-frequency characteristic of the islanding system.
Abstract. The evolution of the energy markets has been accelerating the use of distributed energy resources (DERs) all over the world. Virtual power plant (VPP) is a new method to management this increasing two-way complexity. In this paper, a bidding model for a VPP via robust optimization in the uncertain environment of the electricity market is presented. The flexible feature embedded in the model with respect to solution accuracy and computation burden would be advantageous to the VPP. Results of a case study are provided to show the applicability of the proposed bidding model.
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