Presenting an enhanced particle swarm optimization method for decentralized operation planning of an integrated transmission and distribution network

Abstract

This paper proposes a decentralized operational planning framework for integrated transmission and distribution (TN–DN) networks that enables coordinated yet autonomous decision-making between network operators. A fully nonlinear alternating-current optimal power flow (AC-OPF) model is formulated for both TN and DN subsystems to capture voltage and reactive-power constraints accurately. A iterative coordination mechanism is introduced to preserve data privacy while ensuring operational consistency through limited exchange of active and reactive power at boundary buses. To solve the resulting high-dimensional, highly constrained nonlinear optimization problem efficiently, an enhanced particle swarm optimization (PSO) algorithm is developed, incorporating time-varying learning coefficients, adaptive local search, and chaotic diversity control to accelerate convergence and improve solution quality. In addition, a robust max–min optimization strategy is integrated to identify worst-case sudden generator outages without relying on scenario enumeration or probabilistic assumptions. The approach is validated on standard IEEE test systems. Numerical results show that the decentralized framework attains solutions close to centralized optimality while lowering computational effort, improving scalability, and enhancing resilience to generator contingencies, demonstrating its practical suitability for coordinated TN–DN operation planning.