Multi uncertainty of hydro-wind-PV systems and its optimal solution with high dimension is a key challenge in the long-term scheduling of hydro-wind-PV multi energy complementary systems.By employing a hydro-wind-PV scene gener-ation method based on Markov chain and Copula function,and utilizing a reduction technique to reduce the number of scenes,the uncertainties of the hydro-wind-PV system can be quantified.Taking the reduced scenes as input,we developed a long-term two-stage stochastic optimal scheduling model that incorporates Benders decomposition algorithm and convex linearization to re-alize high efficient solution for high dimension problems.The model was used to simulate the scheduling process of a clean energy base in downstream of Jinsha River,which demonstrated the method's effectiveness in enhancing adaptability to the uncertain hydro-wind-PV systems and in improving overall benefits.In out-of-sample testing,the proposed method increased 55.2 million kWh power generation and decreased 169.4 million m3 abandoned water compared to traditional methods,demonstrating a greater performance.
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