A multi-area decentralized optimal power flow framework for smart grids with interconnected transmission networks

Smart grids (SGs) are revolutionizing modern power systems by enhancing operational efficiency, reliability, and the integration renewable energy through advanced communication protocols, decentralized control architectures, and intelligent automation. The transmission network serves as the critical infrastructure underpinning this transformation, enabling real-time monitoring, adaptive automation, and high-speed data exchange to ensure secure and efficient electricity delivery across large-scale systems. To fully exploit these advancements, optimal power flow (OPF) methodologies are essential for minimizing losses and enhancing grid stability under dynamic and uncertain conditions. This study proposes a fully decentralized OPF framework for multi-area smart grids, utilizing a bio-inspired Dynamic Leader Election Algorithm (DLEA) to optimize decision-making and enhance coordination. The framework extends from a 14-bus, 4-area test system to large-scale networks up to 515 buses, incorporating renewable energy modules and IoT-enabled adaptive loads for real-time optimization. Simulation results on a 515-bus system demonstrate that DLEA achieves a cost of $530.432 with an error of $3.901\times {10}^{-3}\%,$ outperforming benchmark algorithms such as the Optimal Control Design (OCD), which incurs a higher cost of $563.563 and an error of 7.109$\times {10}^{-3}$ %. Additionally, DLEA completes optimization in 14.678 s with 63.291 % efficiency, compared to OCD’s 190.201 s and 62.273 % efficiency. A comparative analysis across different algorithms reveals that DLEA reduces transmission losses, achieves better power line utilization, and maintains all bus voltages within the stable operational range of 1.0–1.1p.u. The algorithm demonstrates a 1.22 % higher efficiency than ADMM and converges faster with fewer iterations. These results confirm DLEA’s superior performance in scalability, stability, and computational efficiency. A feasibility analysis conducted for diverse regions—including Thailand, Pakistan, and Australia—further supports the robustness and practical applicability of the proposed framework in heterogeneous grid infrastructures.