CMOS-Based Optimal Microgrid Structuring with Renewable Distributed Generators and Electric Vehicles

The increasing penetration of renewable distributed generators (RDGs) and electric vehicles (EVs) introduces significant operational complexity in modern microgrids, demanding efficient optimization and low-power control architectures. This paper proposes a CMOS-based optimal microgrid structuring framework integrated with intelligent energy management for coordinated RDG and EV operation. A low-power CMOS control architecture is designed to enable real-time sensing, load forecasting, and bidirectional EV charging control while minimizing hardware energy consumption. The structural optimization of generation units, storage systems, and EV charging stations is achieved using the Waterwheel Plant Algorithm (WPA), which ensures enhanced convergence and global search capability. The proposed framework optimally determines generator placement, power dispatch scheduling, and EV charging strategies under varying load and renewable intermittency conditions. Simulation results demonstrate a reduction in total operational cost by 18.6%, power loss minimization by 21.3%, and voltage deviation improvement by 15.8% compared to conventional particle swarm and genetic algorithm-based approaches. The CMOS-based controller further reduces control power consumption by approximately 32% under sub-threshold operation. The integrated hardware–algorithm co-design approach enhances system reliability, scalability, and energy efficiency, making it suitable for next-generation smart microgrids with high EV penetration.