Abstract: The access of emerging elecments such as distributed power generation, energy storage, and microgrids has a great impact on the operation characteristics of the power system. Power grid, as a critical component of new-type power systems, requires that its planning and investment decisions fully account for the emerging elements' impact on investment effectiveness, so as to ensure that the grid investment scale and allocation align with the construction objectives of new-type power systems. At present, evaluation of the power grid investment effectiveness mostly focuses on cost input and economic benefits. The construction of new-type power systems, however, requires that the power grid investment effectiveness be guided by the overall benefits, and the key influencing factors be extracted to provide directional guidance for the power grid investment simulation. This paper proposes a power grid investment effectiveness evaluation and investment simulation method for new-type power systems based on machine learning algorithm, and constructs a power grid investment effectiveness evaluation model based on the machine learning algorithm of the least squares support vector machine (LSSVM), and uses the particle swarm optimization algorithm (PSO) to optimize the parameters of LSSVM. The distributed power generation and energy storage scenarios are used for case study. Based on the quantitative mapping relationship between the physical indicators of the power grid, the power grid investment indicators and the power grid investment effectiveness indicators under the new-type power systems, the power grid investment simulation method and model are established, and case study is carried out using differentiated scenarios to verify the feasibility of the proposed power grid investment simulation method. This study can provide a theoretical and technical support for the power grid investment decision-making for the new-type power systems.