Smart scheduling of microgrids: An integrated approach for power management, voltage control, and distributed solutions

• Integrates dynamic power management with voltage control, addressing losses. • Uses online optimization with load shedding to boost efficiency. • Categorized into ARP control, with optimal DG trajectories via APC & RPC. • Uses network-topology method, integrating load flow constraints in cost. • Sub-gradient technique for local optimization, implemented by DACs. • Validated by simulations, effective in ARP and voltage regulation for MG modes. This research proposes a new distributed control strategy for smart grids, leveraging Distributed Agent Controllers (DACs) to enable real-time optimization and fault-tolerant operation. The proposed system integrates active and reactive power control modules within the DACs. This architecture enables localized power flow optimization while simultaneously tackling power loss and voltage regulation. A dual-cost-function optimization framework is introduced, decomposing the problem into active power optimization (minimizing power loss, self-voltage deviation, Distributed Generation (DG) power changes, and load shedding) and reactive power optimization (minimizing power loss and voltage deviation). The system employs sensitivity matrices to achieve precise voltage regulation. An iterative gradient algorithm solves the decomposed cost functions locally, ensuring high-speed optimization and convergence to the optimal solution. Key features of the proposed method include a real-time load shedding mechanism and a DG power management strategy, which dynamically adjusts to system conditions to maintain stability and efficiency. The system ensures bus voltages and system frequency remain within acceptable limits during both grid-connected and islanded modes of operation. The effectiveness of the proposed approach is validated through simulations, demonstrating its ability to manage active and reactive power and voltage regulation in a unified control loop. The results highlight the system’s robustness, scalability, and adaptability, making it a significant advancement over existing methods for intelligent grid control.