Abstract
With the massive application of new energy, the contradiction of power grid regulation has become increasingly prominent. How to effectively predict the range of the power grid is a huge challenge faced by the day-ahead dispatch of power system. Aiming at this problem, this paper proposes a method for generating day-ahead scenarios for renewable energy based on historical data analysis. First, the deep embedding clustering (DEC) algorithm is used to analyze historical data, and periods with similar characteristics are divided into one group. Then the conditional deep convolutions generative adversarial network (C-DCGAN) model generates a day-ahead scenario set for renewable energy. At last, the Belgian Elia renewable energy data is used for simulation analysis, and the results show that the proposed method can accurately describe the uncertainty of renewable energy.
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Wang, H. et al. (2023). Day-Ahead Scenario Generation Method for Renewable Energy Based on Historical Data Analysis. In: Tian, Y., Ma, T., Jiang, Q., Liu, Q., Khan, M.K. (eds) Big Data and Security. ICBDS 2022. Communications in Computer and Information Science, vol 1796. Springer, Singapore. https://doi.org/10.1007/978-981-99-3300-6_4
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DOI: https://doi.org/10.1007/978-981-99-3300-6_4
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