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.

通过观测室内数据，我们展示了一种实施简便且易于扩展的强化学习方法，该方法在一个真实世界的场景中验证了基于层次控制系统的协调策略的可行性，并展示了满意的电能追踪效果。