Adaptive energy loss optimization in distributed networks using reinforcement learning-enhanced crow search algorithm

Modern power distribution network incorporates distributed generation (DG) for numerous benefits. However, the incorporation creates numerous challenges in energy management and to handle the challenges it requires advanced optimization techniques for an effective operation of the network. Unlike traditional methods such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and standard Crow Search Algorithm (CSA), which suffer from premature convergence and limited adaptability to real-time variations, Reinforcement Learning Enhanced Crow Search Algorithm (RL-CSA) which is proposed in this research work solves network reconfiguration optimization problem and minimize energy losses. Unlike conventional heuristic methods, which follow predefined search patterns, RL-CSA dynamically refines its search trajectory based on real-time feedback, ensuring superior convergence speed and global search efficiency. The novel RL-CSA enables real-time adaptability and intelligent optimization for energy loss reduction in distributed networks. The proposed model validation is performed on the IEEE 33 and 69 Bus test systems considering diverse performance metrics such as power loss reduction, voltage stability, execution time, utilization efficiency for DG deployment, and energy cost minimization. Comparative results show that RL-CSA achieves a 78% reduction in energy losses, limiting power loss to 5 kW (IEEE 33-Bus) and 8 kW (IEEE 69-Bus) whereas traditional models converge at higher loss levels. The execution time is optimized to 1.4 s (IEEE 33-Bus) and 1.8 s (IEEE 69-Bus), significantly faster than GA, PSO, and CSA, making RL-CSA more efficient for real-time power distribution applications. By balancing exploration-exploitation using CSA while adapting search parameters through reinforcement learning, RL-CSA ensures scalability, improved DG utilization (98%), and better voltage stability (< 0.005 p.u.), making it a robust and intelligent alternative for modern smart grid optimization.