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Demand flexibility is increasingly important for power grids. Carefulcoordination of thermostatically controlled loads (TCLs) can modulate energydemand, decrease operating costs, and increase grid resiliency. We propose anovel distributed control framework for the Coordination Of HeterOgeneousResidential Thermostatically controlled loads (COHORT). COHORT is a practical,scalable, and versatile solution that coordinates a population of TCLs tojointly optimize a grid-level objective, while satisfying each TCL's end-userequirements and operational constraints. To achieve that, we decompose thegrid-scale problem into subproblems and coordinate their solutions to find theglobal optimum using the alternating direction method of multipliers (ADMM).The TCLs' local problems are distributed to and computed in parallel at eachTCL, making COHORT highly scalable and privacy-preserving. While each TCL posescombinatorial and non-convex constraints, we characterize these constraints asa convex set through relaxation, thereby making COHORT computationally viableover long planning horizons. After coordination, each TCL is responsible forits own control and tracks the agreed-upon power trajectory with its preferredstrategy. In this work, we translate continuous power back to discrete on/offactuation, using pulse width modulation. COHORT is generalizable to a widerange of grid objectives, which we demonstrate through three distinct usecases: generation following, minimizing ramping, and peak load curtailment. Ina notable experiment, we validated our approach through a hardware-in-the-loopsimulation, including a real-world air conditioner (AC) controlled via a smartthermostat, and simulated instances of ACs modeled after real-world datatraces. During the 15-day experimental period, COHORT reduced daily peak loadsby an average of 12.5% and maintained comfortable temperatures.

COHORT: Coordination of Heterogeneous Thermostatically Controlled Loads for Demand Flexibility