Energy management for demand response in networked greenhouses with multi-agent deep reinforcement learning

Abstract

Greenhouses are key to ensuring food security and realizing a sustainable future for agriculture. However, to ensure crop growth efficiency, greenhouses consume a significant amount of energy, primarily through climate control and artificial lighting systems. Owing to this high energy consumption, a network of greenhouses exhibits immense potential to participate in demand response programs for power grid stability. In this work, a multi-agent deep reinforcement learning (MADRL) control framework utilizing an actor-critic algorithm with a shared attention mechanism is proposed for energy management in networked greenhouses. A network of renewable energy integrated greenhouses is constructed to interact with the power grid, when necessary, to address the fluctuations associated with renewable energy generation and dynamic electricity prices. The viability and scalability of this multi-agent approach is demonstrated by evaluating its capabilities for a network of five greenhouses of varying capacities. The proposed MADRL-based control approach for demand-side energy management in networked greenhouses demonstrates efficiency in maintaining indoor climate in all greenhouses while ensuring a 28% reduction in net load demand as compared to well-known algorithms.