Real-World Implementation of Reinforcement Learning Based Energy Coordination for a Cluster of Households
This work addresses the need for scalable energy management in the built environment, which accounts for 40% of global power consumption, though it is incremental by extending individual building control to aggregated clusters.
The paper tackled the problem of coordinating power consumption across multiple residential buildings to support the power grid, demonstrating in a real-world pilot that a reinforcement learning-based approach achieved satisfactory power tracking for 8 households over a 4-week period.
Given its substantial contribution of 40\% to global power consumption, the built environment has received increasing attention to serve as a source of flexibility to assist the modern power grid. In that respect, previous research mainly focused on energy management of individual buildings. In contrast, in this paper, we focus on aggregated control of a set of residential buildings, to provide grid supporting services, that eventually should include ancillary services. In particular, we present a real-life pilot study that studies the effectiveness of reinforcement-learning (RL) in coordinating the power consumption of 8 residential buildings to jointly track a target power signal. Our RL approach relies solely on observed data from individual households and does not require any explicit building models or simulators, making it practical to implement and easy to scale. We show the feasibility of our proposed RL-based coordination strategy in a real-world setting. In a 4-week case study, we demonstrate a hierarchical control system, relying on an RL-based ranking system to select which households to activate flex assets from, and a real-time PI control-based power dispatch mechanism to control the selected assets. Our results demonstrate satisfactory power tracking, and the effectiveness of the RL-based ranks which are learnt in a purely data-driven manner.