Virtual power plants can participate in power market transactions by combining wind and solar power generation and energy storage technologies, They participate in energy market transactions for aggregating distributed energy and user-side resources, Meanwhile, the carbon trading market is an effective tool to control the greenhouse effect, As a market subject with unlimited potential, VPP of industrial park plays its environmental benefits by participating in the carbon trading market, It is able to promote the growth of VPP benefits and reduce carbon emissions, Firstly, this paper introduces the composition of virtual power plants and the carbon trading market, Secondly, this paper puts forward the influence of the uncertainty of wind turbines on virtual power plants, Thirdly, a virtual power plant model is build under the uncertainty of wind turbines, Finally, the operation of virtual power plants is optimized considering carbon trading, so as to maximize the benefits and explore the carbon reduction capacity of virtual power plants
In this paper, sparse representation classification (SRC) model based on discriminant dictionary is proposed to train loads of different categories by K-SVD algorithm. Firstly, The absolute value of sparse coefficient and the category of atoms are used to determine the load category. Secondly, the identification of load types is improved in the traditional SRC method based on dictionary learning. Thirdly, the actual identify effect is optimized. Finally, an example is given to illustrate the recognition process. The discriminant dictionary-based SRC method is compared with the traditional SRC method. It proved the feasibility and effectiveness of the method.
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