To fully leverage the application potential of distributed energy storage systems (DESS) and network reconfiguration, a coordinated optimization method is proposed to enhance the economic efficiency of distribution networks under normal conditions and the reliability of a power supply during fault conditions. First, a scenario-generation method is developed based on Latin hypercube sampling and Kantorovich distance synchronous back-substitution reduction is used to obtain the typical scenario of wind and solar output. Next, a planning operation coordinated optimization framework and model are established, considering both normal and fault states of the distribution network. In the planning layer, the objective is to minimize the annual comprehensive capital expenditures for the distribution network to improve the economic efficiency of the distribution network. The operation layer includes both normal operation and fault operation states, with the optimization goal of minimizing the sum of normal operation costs and the fault costs associated with load shedding. Subsequently, a hybrid optimization algorithm combining an improved Aquila Optimizer-Second-Order Cone Programming (IAO-SOCP) is proposed to solve the coordinated optimization model. Finally, the proposed coordinated optimization method is validated using an enhanced IEEE 33-bus distribution network case study. The results demonstrate that the method effectively reduces network losses and minimizes load shedding costs during fault conditions, thereby ensuring a balance between the economic efficiency and reliability of the distribution network.

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.

With a high share of renewable energy and electric vehicle loads, distribution networks face insufficient carrying capacity due to structural weaknesses, reverse power flow, and weakened supply capability. Flexible interconnection devices optimize line power flow, while energy routers (ERs) regulate node‐level power. Their combination effectively addresses these challenges. This paper proposes a two‐layer planning method integrating the rotary power flow controller (RPFC) and ER across spatial and temporal dimensions to improve economy and carrying capacity. RPFC advantages over other flexible devices are analysed, and mathematical models for RPFC and ER are developed. The upper layer minimizes total network cost for siting and sizing, while the lower layer coordinates operation using indicators of stability, balance, economy, and flexibility. A hybrid optimization algorithm combining an improved gravitational field algorithm (IGFA) and second‐order cone programming (SOCP) is introduced, incorporating a tent chaotic map and elite retention strategy. Simulation results show the method reduces network losses by 20.8% and increases carrying capacity by 15.9%. Compared with the GFA, the IGFA‐SOCP improves convergence accuracy by 4.5% and reduces computation time by 42.4%, confirming the effectiveness of the proposed approach.