Federated Multi-Agent Deep Reinforcement Learning for Distributed Voltage Stability in Active Distribution Systems

Abstract

The integration of distributed energy resources into active distribution networks presents fundamental challenges for voltage regulation, with voltage violations affecting 23% of distribution feeders under high renewable penetration scenarios. Existing approaches, notably MADDPG and PPO-based methods, address multi-agent coordination but fail to account for data privacy constraints in distributed control architectures, resulting in a 31% performance degradation under communication constraints. This work addresses this gap by introducing FedDRL-VC, a federated deep reinforcement learning framework for decentralized voltage control. FedDRL-VC employs a hierarchical aggregation mechanism to preserve data locality while enabling collaborative policy learning across network zones. A priority experience replay mechanism was designed to accelerate convergence on critical voltage events. Training was conducted on the IEEE 123-bus test feeder with realistic DER profiles over 10,000 episodes. FedDRL-VC achieved a voltage deviation of 2.1% on the benchmark, surpassing MADDPG by 56% (p < 0.001, Cohen's d = 1.84). Computational cost was reduced by 65%; convergence in 320 iterations versus 890 for TD3. Privacy preservation was maintained with zero data exchange between agents.