  Demand flexibility is increasingly important for power grids. Careful
coordination of thermostatically controlled loads (TCLs) can modulate energy
demand, decrease operating costs, and increase grid resiliency. We propose a
novel distributed control framework for the Coordination Of HeterOgeneous
Residential Thermostatically controlled loads (COHORT). COHORT is a practical,
scalable, and versatile solution that coordinates a population of TCLs to
jointly optimize a grid-level objective, while satisfying each TCL's end-use
requirements and operational constraints. To achieve that, we decompose the
grid-scale problem into subproblems and coordinate their solutions to find the
global optimum using the alternating direction method of multipliers (ADMM).
The TCLs' local problems are distributed to and computed in parallel at each
TCL, making COHORT highly scalable and privacy-preserving. While each TCL poses
combinatorial and non-convex constraints, we characterize these constraints as
a convex set through relaxation, thereby making COHORT computationally viable
over long planning horizons. After coordination, each TCL is responsible for
its own control and tracks the agreed-upon power trajectory with its preferred
strategy. In this work, we translate continuous power back to discrete on/off
actuation, using pulse width modulation. COHORT is generalizable to a wide
range of grid objectives, which we demonstrate through three distinct use
cases: generation following, minimizing ramping, and peak load curtailment. In
a notable experiment, we validated our approach through a hardware-in-the-loop
simulation, including a real-world air conditioner (AC) controlled via a smart
thermostat, and simulated instances of ACs modeled after real-world data
traces. During the 15-day experimental period, COHORT reduced daily peak loads
by an average of 12.5% and maintained comfortable temperatures.
