Intelligent Topology Optimization of Distribution Networks Using Hybrid PSA-SA Algorithm for Loss Reduction, Voltage Profile Enhancement, and Smart Grid Enablement

In the era of energy transition and smart grid evolution, distribution network reconfiguration has emerged as a cost-effective strategy to reduce active power losses, enhance voltage stability, and improve system reliability. This paper introduces, for the first time, a novel hybrid metaheuristic algorithm named PSA-SA (Particle Swarm Annealing Simulated Annealing), designed to overcome the limitations of standalone PSO (premature convergence) and SA (high computational cost). In PSA-SA, particle swarms explore the solution space under PSO dynamics, while the global best particle undergoes SA-based stochastic refinement at each iteration to escape local optima. Crucially, the inertia weight of PSO is dynamically modulated by the annealing temperature, creating a unique synergy between exploration and exploitation. A second key innovation is the integration of Smart Grid Readiness Index (SGRI) into the multi-objective function quantifying remote switch controllability, IoT monitoring density, and load-response agility. Extensive simulations on IEEE ۳۳-bus and ۶۹-bus test systems demonstrate that PSA-SA outperforms PSO, SA, GA, and GWO by average margins of ۱۸.۷% in loss reduction, ۱۲.۴% in voltage deviation improvement, and ۱۵% in SGRI enhancement. The algorithm is SCADA/EMS-compatible and suitable for real-time deployment in modern intelligent distribution networks.

کلیدواژه ها:

Distribution Network Reconfiguration ، Power Loss Minimization ، Voltage Profile Enhancement ، Particle Swarm Optimization (PSO) ، Simulated Annealing (SA) ، Hybrid PSA-SA Algorithm ، Smart Grid Readiness ، Metaheuristic Optimization