Microgrid Optimization Technique Using Supervised Learning for Resiliency Enhancement in Power Systems

This paper addresses key limitations in transmission–distribution (T&D) co-simulation for resiliency, including fragmented modeling, high complexity, synchronization issues, weak renewable control, and data access constraints. A unified co-simulation framework is proposed to optimize microgrid formation and operation in high-penetration renewable systems, improving resiliency while reducing costs and network losses. The developed co-simulation platform enables modular, conflict-free synchronization between transmission and distribution networks without additional handshake software, allowing independent data transfer and seamless co-optimization. The technique assists in transmission and distribution dynamic coordination, supports economic dispatch, and performs three-phase optimal power flow (OPF). An Adaptive Neuro-Fuzzy Inference System (ANFIS) is used for load forecasting and optimization modeling, enabling fast convergence and computational efficiency. The framework supports both grid-connected and islanded modes, including dynamic islanding, reconnection, and load prioritization. Case studies using IEEE 14-Bus transmission with 15-Bus and modified unbalanced 123-Bus distribution systems validate the approach. Results show up to a 68% reduction in operating costs and significant reductions in loss, demonstrating improved resilience, scalability, and secure data exchange for modern power systems.