Decentralized Control of Crop Growth Conditions in Vertical Farms Under Dynamic Energy Markets

Abstract

The growing global population and the increasing scarcity of arable land highlight the urgent need for reliable and efficient food production systems. With their controlled environments, vertical farms (VFs) offer a promising solution for sustainable food security. Nevertheless, their high energy demands call for innovative approaches to optimize energy consumption while maintaining optimal growing conditions. This paper introduces a novel control-oriented model for VFs, capturing the interactions between crop growth conditions and energy consumption. To address the high energy demand of VFs, the model is integrated into a dynamic energy market characterized by time-varying energy prices and a demand response scheme, which includes a discrete reward to encourage flexible energy consumption. Then, centralized and decentralized receding horizon control approaches are proposed to minimize the energy cost of the VF while ensuring optimal crop growth. Experimental evaluations on real systems of varying scales demonstrate the effectiveness of the proposed approaches in reducing costs and ensuring sustainable agricultural practices. Note to Practitioners–This work addresses a growing challenge in operating vertical farms: reducing energy costs while maintaining optimal conditions for crop growth. We introduce a control system that helps vertical farms schedule energy-intensive activities to take advantage of dynamic electricity prices or incentives from grid operators. In particular, we focus on a binary reward structure reflecting real-world demand response programs, where financial incentives are granted only if strict consumption targets are fully met. The approach relies on forecasting and optimization techniques already compatible with standard industrial automation systems. Two control systems are proposed: a centralized controller that manages the entire facility from a single decision point and a decentralized version that allows each unit (e.g., a room or a growing tray) to make decisions independently. The decentralized version offers better scalability and can more easily adapt to farm layout or crop type changes. This framework could also be applied to greenhouses, food storage systems, or other indoor environments with high energy demand.