A distribution network planning method based on the integration of operation and planning and coordinated with the transmission network

Abstract

With the increasing integration of renewable energy into the power grid, the traditional roles of the transmission and distribution networks have become less distinct at the operational level. The integration between distribution network planning (DNP) and the transmission and distribution networks operation is crucial to ensure grid stability. Existing research has primarily focused on collaborative operation control between transmission and distribution networks, leaving a gap in integrated DNP, since few works can handle the integer variables. This study proposes a distribution network planning method based on the integration of operation and planning and coordinated with the transmission network. It aims to minimize investment and operational costs while considering local generation units, distributed renewables, and network constraints. Using a heterogeneous decomposition algorithm (HGD), the optimization model alternates between the two networks, assisted by injected parameters for global optimality. A convolutional neural network (CNN) surrogate model is then used to rapidly optimize precise distribution network plans that coordinate with the transmission network. Experimental results on IEEE 30 and IEEE 69 cases demonstrate that the proposed approach offers valuable engineering benefits, reducing iteration counts by up to 20% and improving accuracy compared to other distributed algorithms.