Work

Federated Deep Reinforcement Learning for Decentralized VVO of BTM DERs

Year: 2024

Type: conference-paper

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.

Source: North American Power Symposium (NAPS)

DOI: 10.1109/naps61145.2024.10741615

Authors: Abhijith Ravi, Linquan Bai, Jianming Lian, Jin Dong, Teja Kuruganti

Institutions: University of North Carolina at Charlotte, Oak Ridge National Laboratory

Language: English

Topic: Optimal Power Flow Distribution

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: green

Funders: U.S. Department of Energy, UT-Battelle, Office of Electricity