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In this paper, based on the study on the low-carbon transformation of urban distribution networks, we conduct research on planning and scheduling energy storage systems for urban distribution networks considering Source-grid-load-storage.. In this paper, based on the study on the low-carbon transformation of urban distribution networks, we conduct research on planning and scheduling energy storage systems for urban distribution networks considering Source-grid-load-storage.. Energy storage as a technology capable of providing timely and safe power–energy output can effectively support the stable operation of novel power systems under normal conditions and enhance resilience under extreme scenarios. However, different types of energy storage systems affect system. . Firstly, we propose a framework of energy storage systems on the urban distribution network side taking the coordinated operation of generation, grid, and load into account. Secondly, we establish a capacity optimization model for energy storage systems by considering the various costs of energy. . A Containerized Battery Energy Storage System (BESS) is rapidly gaining recognition as a key solution to improve grid stability, facilitate renewable energy integration, and provide reliable backup power. In this article, we'll explore how a containerized battery energy storage system works, its. . Containerized energy storage systems (ESS) have emerged as the most scalable and efficient solution for stabilizing energy production and improving project economics. What Is a Container Energy Storage System? A container energy storage system is a fully integrated battery storage solution packaged. . Moreover, the opportunities offered by battery energy storage systems (BESSs) coupled with photovoltaic (PV) systems require an ability to forecast the load power to optimize the size of the entire system composed of PV panels and storage devices. This paper presents a sizing and control strategy. . The complexity and nonlinearity of active distribution network (ADN), coupled with the fast-changing renewable energy (RE), necessitate advanced real-time and safe dispatch approach. This paper proposes a complementary reinforcement learning (RL) and optimization approach, namely SA2CO, to address.