Distributed Smart Multihome Energy Management Based on Federated Deep Reinforcement Learning

Le résumé fourni par la source

In recent years, there’s been a surge in the popularity and affordability of distributed power generation equipment, such as photovoltaic systems (PV) and energy storage systems. At present, however, most solutions target individual user’s energy management. Given the varied energy consumption habits, networking neighboring users and managing energy as a unified system could boost efficiency. Yet, this comes with challenges: unpredictable dynamic demands, significant computational loads, and concerns over data privacy. To tackle these challenges, we introduce a management system that merges deep reinforcement learning (DRL) with federated learning (FL) techniques, named PDDPG-FL. In this setup, each home possesses an agent responsible for decisions like charging/discharging and trading energy with other users. For every agent, we employ a priority-aware deep deterministic policy gradient (PDDPG) algorithm. This not only addresses fluctuating demand adeptly but also offers computational advantages over the conventional DDPG algorithm. Moreover, by incorporating the FL framework, agents can collaborate without risking data privacy breaches. Simulation results show that PDDPG-FL can reduce dependency on main supply grids by up to 9.7% and offers a more streamlined computational process.

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