Reinforcement Learning-Assisted Evolutionary Optimization for Distribution Network Operational Planning

Active distribution networks are undergoing a rapid transition driven by the increasing penetration of distributed resources, including renewable generation, demand response programs, and electric vehicles, which introduces new technical and operational challenges for day-ahead network management. Coordinating conventional control devices together with emerging distributed flexibility leads to a nonlinear, nonconvex, mixed-discrete, and multi-period optimization problem with high computational complexity. To address this challenge, this paper proposes an intelligent solution methodology based on genetic algorithms, reinforcement learning, and dynamic programming for short-term operational planning of distribution systems. The methodology adopts a two-level structure. At the aggregator level, electric-vehicle charging is optimally scheduled to flatten the loading profile of secondary distribution transformers. At the distribution system operator level, controllable resources, including demand response actions, OLTC tap positions, capacitor banks, and network reconfiguration switches, are coordinated to minimize operating costs associated with energy losses, voltage violations, branch congestion, and controllable-resource use. To enhance search efficiency and reduce dependence on manual parameter tuning, a tabular Q-learning agent is embedded into the genetic algorithm to adaptively control selection, crossover, and mutation strategies during execution. The best hourly operating candidates obtained by the genetic algorithm are then linked through dynamic programming to determine the optimal day-ahead schedule while preserving temporal consistency. The proposed framework combines engineering-grade power-flow simulation with adaptive artificial-intelligence techniques, providing an effective and practical decision-support tool for modern distribution control centers.