As the proportion of new energy access increases year by year, the resulting energy imbalance and voltage/trend distribution complexity of the distribution network system in the spatio-temporal dimension become more and more prominent. The joint introduction of electromagnetic rotary power flow controller (RPFC) and energy router (ER) can improve the high proportion of new active distribution network (ADN) consumption and power supply reliability from both spatial and temporal dimensions. To this end, the paper proposes an ADN expansion planning method considering RPFC and ER access. A two-layer planning model for RPFC and ER based on spatio-temporal characteristics is established, with the upper model being the siting and capacity-setting layer, which takes the investment and construction cost of RPFC and ER as the optimization objective, and the lower model being the optimal operation layer, which takes the lowest operating cost of the distribution network as the objective. The planning model is solved by a hybrid optimization algorithm with improved particle swarm and second-order cone planning. The proposed planning model and solving algorithm are validated with the IEEE33 node example, and the results show that the joint access of RPFC and ER can effectively improve the spatial-temporal distribution of voltage in the distribution network and has the lowest equivalent annual value investment and operation cost.

The energy router (ER) is a crucial component in smart distribution networks, and its optimal configuration is essential for enhancing the operational efficiency, economy, and security of the grid. However, existing research rarely considers both the location and sizing costs of the ER in conjunction with flexible load demand response. Therefore, this paper proposes an optimal configuration method for the energy router in active distribution networks, incorporating demand response. First, to balance the comprehensive operational characteristics of the active distribution network throughout the year with computational efficiency, an improved K-means clustering algorithm is employed to construct multiple representative scenarios. Then, a bi-level programming model is established for ER location and sizing, considering demand response. The upper level optimizes the location and capacity configuration of the ER to minimize the overall cost of the distribution network. The lower level focuses on multi-objective optimization, including peak shaving, valley filling, network losses, and voltage deviations, to achieve energy scheduling within the distribution network. Finally, an improved bi-level particle swarm optimization algorithm is employed to solve the model. Simulation results based on the IEEE 33-node system demonstrate that the peak shaving and valley filling optimization rate after ER integration into the active distribution network is at least 9.19%, and it is improved to 14.35% when combined with demand response. Concurrently, the integration of the ER enhances the distribution network’s ability to absorb renewable energy, reduces network losses, and improves power quality.