- By:
- Ravi, Abhijith; Bai, Linquan; Lian, Jianming ; Dong, Jin ; Kuruganti, Phani Teja V
- Page Number:
- 1-6
- Book Title:
- 2024 56th North American Power Symposium (NAPS)
- Publication Date:
- March 12, 2026
- Publisher Location:
- IEEE, New Jersey, United States of America
- Conference Name:
- The 56th North American Power Symposium (NAPS)
- Conference Location:
- El Paso, Texas, United States of America
- Conference Sponsor:
- El Paso Electric
- View DOI Listing:
- https://doi.org/10.1109/NAPS61145.2024.10741615
Abstract
The future of grid control requires a hybrid approach combining centralized and decentralized methods to fully utilize the potential of smart edge devices with artificial intelligence (AI) capabilities. This paper aims to develop and evaluate a federated deep reinforcement learning (FDRL) framework for decentralized adaptive volt-var optimization (VVO) of behind-the-meter (BTM) distributed energy resources (DERs). First, this paper models a single deep reinforcement learning (DRL) agent using the Markov Decision Process (MDP) framework for decentralized adaptive VVO of BTM DERs. Two DRL algorithms, soft actor-critic (SAC) and twin-delayed deep deterministic policy gradient (TD3), are compared for their effectiveness in optimizing VVO. Results show that TD3 outperforms SAC, achieving a 71.3% improvement in mean reward. Finally, the DRL agent is deployed within the FDRL framework, using the Flower platform, to enhance learning, provide adaptive control, and ensure data privacy for BTM DERs.
