Real-World Implementation of Reinforcement Learning Based Energy
  Coordination for a Cluster of Households
Gargya Gokhale Niels Tiben Marie-Sophie Verwee Manu Lahariya Bert Claessens Chris Develder
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.