Against the backdrop of the accelerated advancement of energy transition

the high proportion of new energy access makes it difficult for traditional distribution network planning to adapt

leading to problems such as power fluctuations

unbalanced power flow and decreased voltage stability.It proposes an adaptive partition collaborative planning method for distribution networks that integrates deep learning and multi-dimensional feature clustering.Firstly

it establishes a new energy output probability model and a dynamic power flow model.It divides the distribution network into sub-regions with similar characteristics by using multi-dimensional feature clustering.Factors such as load

it comprehensively considers new energy density and line transmission capacity to enhance the power flow balance capability.Secondly

with the aid of deep learning algorithms

it constructs an objective function to achieve collaborative optimization and global coordination of the partitioning scheme.Finally

experiments show that this method can effectively reduce power fluctuations under different working conditions

improve the stability of the power grid and the power flow balance rate

providing effective support for the planning and stable operation of high proportion of new energy distribution systems.