In response to the challenges of large-scale integration of distributed photovoltaic(PV)sources into dis-tribution networks,an integration capacity planning method for distributed photovoltaic sources based on general-ized Benders decomposition(GBD)is proposed.The approach utilizes a data-driven sequential selection method to determine the optimal sequence of variables in the C-Vine Copula model.In combination with Latin hypercube sam-pling(LHS)and scenario evaluation indicators,typical load-resource correlation scenarios are constructed.Build-ing upon these generated typical scenarios,the paper has established a photovoltaic source integration planning model based on the GBD.This model comprises a main problem for photovoltaic source planning and a sub-problem for distribution network operation,solved using linear programming and optimal power flow methods,respectively.Case studies are conducted on the grid framework of the IEEE 33-bus system.The results demonstrate that the pro-posed method for generating typical scenarios reduces resource and load errors by over 50%compared to traditional methods.Moreover,the computational complexity of the planning model is reduced by 11%,and the computation time is shortened by 9%compared to previous approaches.
C-Vine Copuladata-driven sequential selection methodGBDmain problem of photovoltaic source planningsub-problem of distribution network operation
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