ELOCESS: An ESS Management Framework for Improved Smart Grid Stability and Flexibility

The Internet of Smart Grid Things (IoSGT) architecture, which incorporates cutting-edge Internet of Things (IoT) and Machine Learning (ML) technologies, is emerging as an effective enhancement for smart grid applications. By embracing the traditional Edge-Fog-Cloud hierarchy, IoSGT markedly advances the capabilities of existing infrastructure. The strategic placement and scheduling of Energy Storage System (ESS) capacities are vital for improving the performance, resilience, and robustness of IoSGT environments. To mitigate peak loads, optimize power profiles, and reduce system losses, this paper introduces an ELOCESS (Effective Location and Optimal Capacity of ESS) framework within the IoSGT architecture. The ELOCESS framework, which employs Time Domain Power Flow (TDPF) and Complex-Valued Neural Networks (CVNN), provides precise voltage fluctuation forecasts. The Plant Growth Simulation Algorithm (PGSA) determines the optimal storage locations and capacities. Our thorough simulation on a standard 33-node network proves that the ELOCESS method works to find the best places and sizes for battery storage systems, with an impressive 97.32% success rate for optimization.