Research on Investment Decision Optimization Considering Power Grid Carrying Capacity

In the context of global energy transition, photovoltaic, wind power-based new energy scale, extensive access is the inevitable trend of grid energy transition development, with the new energy in the various voltage levels of distribution grid scale continues to rise, the grid presents new energy multi-level access, integrated consumption characteristics, in order to protect the new energy consumption and realize the precise investment, it is necessary to carry out the optimization of the investment decision considering the carrying capacity of the power grid research. This paper firstly establishes a grid carrying capacity assessment model to measure the carrying capacity of each voltage level for new energy, then considers the grid carrying capacity and other influencing factors to build an investment demand model, and coordinates the investment capacity of grid enterprises to establish a dynamic balance optimization model, by adjusting the investment capacity and the range of adjustable indicators in the investment demand model, and taking the minimum difference between investment demand and investment capacity as the target, and adopts the particle swarm algorithm to solve the problem, and uses the particle swarm algorithm to solve the problem. By adjusting the range of adjustable indexes in the investment capacity and investment demand measurement model, and taking the minimum difference between investment demand and investment capacity as the goal, the particle swarm algorithm is used to solve the model, and a regional power grid is used as an example to simulate and optimize the model to verify the feasibility.