Adaptive Deep Reinforcement Learning for VVC in High DER-Integrated Distribution Grids

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Abstract
The increasing penetration of Distributed Energy Resources (DERs) in modern distribution grids has created sig-nificant challenges in maintaining voltage profiles and minimizing reactive power losses. While Deep Reinforcement Learning (DRL) approaches have been explored for Volt/VAR Control (VVC), existing methods often rely on single-agent frameworks or lack adaptive mechanisms to balance competing grid objectives dy-namically. Multi-Agent (MA) DRL frameworks have emerged but typically require explicit inter-agent communication, limiting scalability and practicality. To address these limitations, we propose a novel MA-DRL framework that incorporates adaptive reward computation to implicitly coordinate special-ized agents, including a Voltage Control Agent for maintaining voltage profiles and a Reactive Power Agent for optimizing reactive power dispatch. Unlike prior approaches, our method dynamically adjusts local and global rewards based on real-time grid conditions, enabling scalable and efficient grid control without explicit inter-agent communication. We evaluated the framework on the IEEE 123-bus test system under high DER penetration. The results demonstrated that the proposed MA-DRL approach successfully eliminated voltage violations, reduced power losses through optimized reactive power dispatch, and decreased transformer tap changes and capacitor operations, thereby extending equipment lifespan. These results demonstrate the scalability and practicality of adaptive reward-based MA-DRL for addressing modern grid management challenges.
Funding Information
  • NSF (CNS-2105269)

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