# Dynamic Reconfiguration of Active Distribution Network Based on Improved Equilibrium Optimizer
## 1 Introduction  
The distribution network comprises a large number of normally closed and open switches. The primary objective of network reconfiguration is to alter the network topology by adjusting the status of line switches, thereby achieving load balancing across feeders, mitigating overloads, reducing power losses, and restoring faults to enhance the system's power supply capability [1]. In recent years, the increasing integration of distributed generation (DG), such as wind and photovoltaic energy, into power systems--due to their environmental benefits and renewability--has made active distribution network reconfiguration a prominent research focus. However, the inherently intermittent and fluctuating output of DGs can lead to complications such as bidirectional power flow, voltage violations, and line overloading, which pose challenges to grid reliability. Against this backdrop, distribution network reconfiguration has gained attention as a viable solution to improve operational efficiency and system stability. Reconfiguration approaches are generally classified into static and dynamic methods. Static reconfiguration adjusts the network topology based on the load status at a single time point, often overlooking temporal variations in load, thus failing to meet the requirements of real-time operation. In contrast, dynamic reconfiguration adapts the network structure inresponse to real-time system changes, offering greater practicality. Currently, a key research challenge lies in how to segment load periods and decompose dynamic reconfiguration into a series of interrelated static reconfiguration tasks under fluctuating loads and DG outputs. [PERSON] et al. [2] provided a comprehensive review of static and dynamic distribution network reconfiguration methods, highlighting the potential of dynamic reconfiguration in modern smart grids and classifying existing approaches into five methodological categories: classical methods, heuristic methods, metaheuristic methods, hybrid methods, and methods based on machine learning. Furthermore, [PERSON] et al. [2] noted that while machine learning methods are suitable for dynamic reconfiguration due to their real-time adaptability, metaheuristic algorithms remain highly competitive in dynamic reconfiguration problems, owing to their flexibility, objective versatility, and proven optimization accuracy. The reconfiguration problem in distribution networks is inherently a complex, large-scale, nonlinear, and combinatorial optimization challenge [3]. In recent years, a variety of intelligent optimization algorithms--such as Genetic Algorithms [4; 5], Particle Swarm Optimization [4; 6; 7], Grey Wolf Optimization [8], and Cuckoo Search Algorithm [9]--has been widely adopted for this task. Furthermore, [PERSON] et al. [10] introduced the Electric El Eel Foraging Optimization for joint DG placement and network reconfiguration, achieving over 90% improvement in power loss and voltage deviation in typical test systems. [PERSON] et al. [11] employed a Selective Particle Swarm Optimization algorithm for practical DN reconfiguration, achieving reductions in power loss and improvements in voltage profile under various load scenarios. [PERSON] and [PERSON] [12] applied the Slime Mold Algorithm to reconfigure a 123-bus unbalanced distribution system, effectively reducing power loss and minimizing current and voltage unbalance indices. [PERSON] et al. [13] employed a modified shark smell optimization algorithm for DN reconfiguration in a real Ethiopian system, achieving simultaneous improvements in reliability indices, voltage profile, and power loss reduction. [PERSON] and [PERSON] [14] proposed improved hybrid optimization schemes combining GA and TLBO for joint DER integration and network reconfiguration, achieving significant improvements in power loss and voltage performance across standard test systems. [PERSON] et al.