Multi-agent Deep Reinforcement Learning for Energy Management in Grid-Responsive Networked Greenhouses

Greenhouses play a pivotal role in achieving food security and paving the way for sustainable urban agriculture. Yet, to optimize crop growth, they demand substantial energy, mainly for climate control and artificial lighting. Given this energy-intensive nature, greenhouses are prime candidates for demand response programs, enhancing the power grid's flexibility and resilience in urban areas. In this study, we propose a multi-agent deep reinforcement learning (DRL) framework to manage energy in interconnected renewable energy integrated greenhouses. The framework addresses the challenges of fluctuating on-site renewable energy outputs, and interaction with a dynamic electricity tariff grid. Our multi-agent DRL strategy employs an actor-critic algorithm infused with a shared attention mechanism to ensure scalability and performance. We demonstrate the efficacy of our approach using a New York City (NYC) case study with a network of five greenhouses in each borough. We also compare the performance of our multi-agent DRL method to that of the rule-based and the deep deterministic policy gradient algorithms.