An integration capacity planning method for distributed photovoltaic sources based on generalized Benders decomposition

Abstract

In response to the challenges of large-scale integration of distributed photovoltaic （PV） sources into distribution networks， an integration capacity planning method for distributed photovoltaic sources based on generalized 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 sampling （LHS） and scenario evaluation indicators， typical load-resource correlation scenarios are constructed. Building 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 proposed 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.

Keywords

• c-vine copula
• data-driven sequential selection method
• gbd
• main problem of photovoltaic source planning
• sub-problem of distribution network operation