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In this study, a technique for developing a distribution management system (DMS), which possesses the flexibility to take both preventive and corrective actions against thermal overloading of branches in active distribution networks (ADNs), has been demonstrated. An ADN comprises microgrids that consist of photovoltaic and battery energy storage systems (BESSs). The DMS primarily minimizes the hourly cumulative cost incurred by loads due to energy pricing of utility, by effectively dispatching the BESSs. Besides, the DMS regulates BESS state of charge and bus voltages within their limits. It also controls loading of branches by taking corrective measures during overloading or preventive measures during critical loading conditions. This DMS has been designed using a reinforcement learning based technique, namely, adaptive critic design (ACD). This study elaborates the formulation of ACD algorithm so that an effective performance of the controller can be achieved. As case study, a modified IEEE 5‐bus system along with a microgrid and its controllers have been modelled in detail and simulated in real‐time by developing a simulation‐in‐the‐loop testbed using OPAL‐RT and DSpace. This testbed facilitates simulation of the detailed model along with its power electronic components, such that both transient and steady‐state performance of the system can be observed.